CN112052297B

CN112052297B - Information generation method, apparatus, electronic device and computer readable medium

Info

Publication number: CN112052297B
Application number: CN202010930563.6A
Authority: CN
Inventors: 林馨怡; 彭婉莹; 汪忠超
Original assignee: Douyin Vision Co Ltd
Current assignee: Douyin Vision Co Ltd
Priority date: 2020-09-07
Filing date: 2020-09-07
Publication date: 2024-03-22
Anticipated expiration: 2040-09-07
Also published as: CN112052297A

Abstract

Embodiments of the present disclosure disclose an information generation method, an apparatus, an electronic device, and a computer readable medium. One embodiment of the method comprises the following steps: in response to determining that the category of the target question is an open question category, obtaining a set of search results for the target question; extracting a target entity from the initial entity set based on the entity type matched with the target problem to obtain a target entity set; aggregating the target entities in the target entity set to obtain at least one aggregated entity and the related information of the aggregated entity data corresponding to each aggregated entity; and generating an entity information sequence based on the at least one aggregated entity and the aggregated entity data related information corresponding to each aggregated entity. The embodiment realizes the extraction of the targeted search results, saves the time of searching by the user, and improves the searching efficiency, thereby improving the use experience of the user.

Description

Information generation method, apparatus, electronic device and computer readable medium

Technical Field

Embodiments of the present disclosure relate to the field of computer technology, and more particularly, to a method, an apparatus, an electronic device, and a computer readable medium for generating specific information.

Background

With the advancement of the internet, the information on the network is more complex, and a simple question can often get many answers from many levels and different angles. When a user searches for a desired answer in the search results, much time is wasted, resulting in inefficient searching.

Disclosure of Invention

The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Some embodiments of the present disclosure propose an information generation method, apparatus, electronic device, and computer readable medium to solve the technical problems mentioned in the background section above.

In a first aspect, some embodiments of the present disclosure provide an information generating method, the method including: in response to determining that the category of the target problem is an open problem category, acquiring a search result set of the target problem; extracting a target entity from the initial entity set based on the entity type matched with the target problem to obtain a target entity set; aggregating the target entities in the target entity set to obtain at least one aggregated entity and aggregated entity data related information corresponding to each aggregated entity; and generating an entity information sequence based on the at least one aggregated entity and the aggregated entity data related information corresponding to each aggregated entity.

In a second aspect, some embodiments of the present disclosure provide a method for presenting information, the method comprising: receiving a question query request, and acquiring a candidate answer set and an entity information sequence corresponding to the question query request; the entity information sequence comprises at least one target entity and data related information of the target entity, wherein the data related information comprises candidate answer information corresponding to the target entity; displaying a target number of the target entities and data related information of the target entities on a first preset position on an information display page; and selecting a preset number of candidate answers from the candidate answer set to be displayed at a second preset position on the information display page.

In a third aspect, some embodiments of the present disclosure provide an information generating apparatus, the apparatus including: an acquisition unit configured to acquire a set of search results of the target problem in response to determining that a category of the target problem is an open problem category; the extraction unit is configured to extract target entities from the initial entity set based on the entity types matched with the target problems to obtain a target entity set; the aggregation unit is configured to aggregate the target entities in the target entity set to obtain at least one aggregated entity and the related information of the aggregated entity data corresponding to each aggregated entity; and the generating unit is configured to generate an entity information sequence based on the at least one aggregated entity and the aggregated entity data related information corresponding to each aggregated entity.

In a fourth aspect, some embodiments of the present disclosure provide an apparatus for displaying information, the apparatus comprising: an obtaining unit configured to receive a question query request, and obtain a candidate answer set and an entity information sequence corresponding to the question query request; the entity information sequence comprises at least one target entity and data related information of the target entity, wherein the data related information comprises candidate answer information corresponding to the target entity; the display unit is configured to display a target number of the target entities and data related information of the target entities on a first preset position on the information display page; and a selecting unit configured to select a predetermined number of candidate answers from the candidate answer set to be presented at a second preset position on the information presentation page.

In a fifth aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; and a storage device having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the method of any of the above.

In a sixth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program when executed by a processor implements any of the methods described above.

Firstly, determining that the category of a target problem is an open problem category, and acquiring a search result set of the target problem; then, extracting a target entity from the initial entity set based on the entity type matched with the target problem to obtain a target entity set; the entity is extracted from a large amount of information, so that a user can obtain answers more efficiently, and the searching time is further saved. Then, aggregating the target entities in the target entity set to obtain at least one aggregated entity and aggregated entity data related information corresponding to each aggregated entity; and finally, generating an entity information sequence based on the at least one aggregated entity and the aggregated entity data related information corresponding to each aggregated entity. The embodiment realizes the extraction of the targeted search results, saves the time of searching by the user, and improves the searching efficiency, thereby improving the use experience of the user.

Drawings

The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.

FIG. 1 is a schematic illustration of one application scenario of an information generation method according to some embodiments of the present disclosure;

FIG. 2 is a flow chart of some embodiments of an information generation method according to the present disclosure;

FIG. 3 is a schematic diagram of one application scenario of a method for presenting information according to some embodiments of the present disclosure;

FIG. 4 is a flow chart of some embodiments of a method for presenting information according to the present disclosure;

FIG. 5 is a schematic diagram of the structure of some embodiments of an information generating apparatus according to the present disclosure;

FIG. 6 is a schematic structural diagram of some embodiments of an apparatus for presenting information according to the present disclosure;

fig. 7 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.

It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.

The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Fig. 1 is a schematic diagram of one application scenario of a method for presenting information according to some embodiments of the present disclosure.

As shown in the application scenario of fig. 1, first, an execution subject of the information generating method may be the server 101. The server 101 may obtain the set of search results 103 for the target issue 102 upon determining that the category of the target issue 102 is an open issue category. For example, the target question 102 may be "what is constipation? ". The open question category generally refers to questions that have multiple answers. Then, extracting target entities from the initial entity set 105 based on the entity types 104 matched with the target questions 102 to obtain a target entity set 106; wherein the initial set of entities 105 is derived based on the set of search results 103. Then, aggregating the target entities in the target entity set 106 to obtain at least one aggregated entity 107-109 and aggregated entity data related information corresponding to each aggregated entity; as an example, the entity type 104 may be "food. The post-aggregation entity 107 may be a "banana", the post-aggregation entity 108 may be a "coarse grain" and the post-aggregation entity 109 may be a "soybean". The aggregated entity data related information corresponding to the aggregated entity 107 may be "689 pieces," the aggregated entity data related information corresponding to the aggregated entity 108 may be "89 pieces," and the aggregated entity data related information corresponding to the aggregated entity 109 may be "68 pieces. Finally, an entity information sequence 110 is generated based on the at least one aggregated entity 107-109 and the aggregated entity data related information corresponding to each aggregated entity. The entity information sequence 110 may be "banana, coarse grain, soybean".

It is to be understood that the information generating method may be executed by the server 101, or may be executed by another device, or may be executed by various software programs. Further, the execution subject may be various electronic devices with a display screen, including but not limited to smartphones, tablet computers, electronic book readers, laptop and desktop computers, and the like. When the execution subject is software, the execution subject can be installed in the electronic device enumerated above. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein.

It should be understood that the number of servers in fig. 1 is merely illustrative. There may be any number of servers, as desired for implementation.

With continued reference to fig. 2, a flow 200 of some embodiments of information generation methods according to the present disclosure is shown. The information generating method comprises the following steps:

in step 201, in response to determining that the category of the target question is an open question category, a set of search results for the target question is obtained.

In some embodiments, the execution subject of the information generating method (e.g., the server 101 shown in fig. 1) may acquire the search result set of the target problem described above in a case where it is determined that the category of the target problem is an open problem category. For example, the target question may be "what is constipation? ". The open question category generally refers to questions that have multiple answers. The set of search results includes at least two search results. The search result may be an answer to the target question, and may be web page text. For example, when the above-mentioned objective problem is "what is being eaten by weight-loss? The search results may be articles or web pages that describe what fruit is consumed to reduce weight. The set of search results may then be at least one article or web page describing what may be consumed for weight loss.

In some optional implementations of some embodiments, before the obtaining the set of search results for the target issue in response to determining that the category of the target issue is an open issue category, the method further includes: performing intent analysis on the target problem to determine a category of the target problem, wherein the category comprises at least one of the following: open problem category and singleness problem category. The above-mentioned singleness problem category generally refers to a problem with one answer. As an example, the target question may be input into an intent model, resulting in a score for the target question. And when the score reaches a preset threshold value, determining the category of the target problem as an openness problem category. The intent model may be trained with sample questions as input and sample scores as desired outputs.

As an example, the intent model may be obtained by performing the following training steps based on a training sample set: respectively inputting sample questions of at least one training sample in the training sample set into an initial machine learning model to obtain a score corresponding to each sample question in the at least one training sample; comparing the score corresponding to each sample question in the at least one training sample with the corresponding sample score; determining the prediction accuracy of the initial machine learning model according to the comparison result; determining whether the prediction accuracy is greater than a preset accuracy threshold; in response to determining that the accuracy rate is greater than the preset accuracy rate threshold, taking the initial machine learning model as a trained intent model; and in response to determining that the accuracy is not greater than the preset accuracy threshold, adjusting parameters of the initial machine learning model, and using unused training samples to form a training sample set, using the adjusted initial machine learning model as an initial machine learning model, and executing the training step again.

It will be appreciated that after the training, the intent model may be used to characterize the correspondence between sample questions and sample scores. The intent model mentioned above may be the Bert (Bidirectional Encoder Representations from Transformers) algorithm. The above-described Bert algorithm may be a novel language model. A new language model is said because it trains the pre-training depth bi-directional representation by jointly adjusting the bi-directional transformers in all layers.

Step 202, extracting a target entity from the initial entity set based on the entity type matched with the target problem, so as to obtain a target entity set.

In some embodiments, the executing entity may extract the target entity from the initial entity set based on the entity type to which the target problem is matched, to obtain a target entity set. Here, when the entity type matched is "food". The initial set of entities may be "" gastrointestinal digestion ', "" intestinal peristalsis ', "" hypomotility ', "" inflammation ', "" banana ', "" coarse food grain ', "" soybean ', "" walnut. The set of target entities may be `banana`, `coarse grain`, `soybean`, `walnut`.

By way of example, the entity type may be identified using methods of dependency analysis, template matching, etc., in conjunction with CRF (Conditional Random Field ). As an example, the entity type of the target problem is determined according to the matching result of the target problem and the preset template. For example, the preset template includes: "what [ category words ] are good," what the above-mentioned target problem can be "what watch is good," and the type of entity that can analyze this target problem is "watch.

In some alternative implementations of some embodiments, the entity types are obtained by: acquiring an associated text of the target problem; for example, when the target problem is "chinese female star", the related text may be "chinese female star". When the target question is voice, voice content is recognized from the voice, and the recognized voice content is used as the associated text of the target question. Word segmentation processing is carried out on the related text to obtain at least one word; here, the word segmentation process may be to segment the article-related information by using a word segmentation device. Determining weight scores of each word in the at least one word, and generating a weight score set; as an example, each term may be scored based on different algorithms, resulting in a weight score, generating a set of weight scores. For example, the algorithm may be a TF-IDF (term frequency-inverse document frequency) algorithm, a textRank algorithm, or the like. Selecting a target word from the at least one word based on the weight subset; as an example, the execution subject may select the target word from the above-described at least one word in order of the weight score from large to small. And generating the entity type based on the target word.

As an example, performing relevance scoring on the target words and each target word in the target vocabulary to obtain a relevance score set; and selecting target words from the target vocabulary according to the size of the relevance score, and taking the selected target words as entity types. By way of example, when the above-mentioned target problem is "what is constipation? "at the time, the key extracted is" what to eat ". The target words in the target vocabulary may be "" food ', "" car', "" ornament ', "" clothing', "" star "". The relevance score between the keyword and each target word may be calculated using inter-Point Mutual Information (PMI). The mutual information (PMI) between the points is mainly used for calculating the semantic similarity between words, and the basic idea is to count the probability that two words appear simultaneously in a text, and if the probability is larger, the correlation is tighter and the correlation is higher. The set of relevance scores may be "" '80', '20', '10', '40', '. The target word selected from the target vocabulary according to the size of the relevancy score may be "food", and then the type of entity matched may be "food".

In some alternative implementations of some embodiments, the initial set of entities is obtained by: performing coarse screening operation on the search result set to obtain a candidate answer set; the coarse screening operation may be performed based on the source of each search result in the set of search results. By way of example, when the above-mentioned target problem is "what is constipation? "when the source of each search result may be a different level of hospital, an individual, or from a different website. We can extract search results from the set of search results that were sourced by the hospital to form a candidate answer set. Extracting entities from the candidate answer set to form an initial entity set; the initial entity set includes the extracted entity and the corresponding relation between the entity and the corresponding candidate answer. The correspondence may be an identification of a candidate answer extracted to the entity. The identification may be an 8-bit 2-ary number. The above-mentioned identification may correspond to the candidate answer one by one. As an example, the candidate answer sets in the candidate answer sets are sequentially input into the entity extraction model to obtain at least one initial entity, and an initial entity set is generated. The entity extraction model may be trained by a set of training samples. The training samples in the training sample set comprise sample candidate answers and sample entities, and the entity extraction model is obtained by taking the sample candidate answers as input and the sample entities as expected output for training.

Step 203, aggregating the target entities in the target entity set to obtain at least one aggregated entity and related information of aggregated entity data corresponding to each aggregated entity.

In some embodiments, the executing body may aggregate the target entities in the target entity set to obtain at least one aggregated entity and aggregated entity data related information corresponding to each aggregated entity. The aggregated entity data related information may be the number of the aggregated entities in the target entity set. For example, the target entities in the target entity set are "corn", "potato", which may be aggregated as an entity "coarse grain", then the aggregated entity is "coarse grain". As an example, the set of target entities may be "corn, potato, banana, walnut", where the number of the above-mentioned aggregated entities "coarse food grain" is 2.

Step 204, generating an entity information sequence based on the at least one aggregated entity and the aggregated entity data related information corresponding to each aggregated entity.

In some embodiments, the execution body may generate the entity information sequence based on the at least one aggregated entity and the aggregated entity data related information corresponding to each aggregated entity. The at least one aggregated entity may be ordered from large to small according to the number of the aggregated entity data related information. As an example, when the number of the aggregated entities "bananas" is 689, the number of "coarse grains" is 89, the number of "soybeans" is 67, and the number of "walnuts" is 60. The obtained body information sequence can be banana, coarse grain, soybean and walnut.

In some optional implementations of some embodiments, the method further includes: calculating a weighted score of each candidate answer in the candidate answer set; generating a behavior score based on behavior features corresponding to the search results in response to the weighted score being greater than a preset threshold; based on the weighted scores and the behavioral scores, a composite score is generated. The weighted score may be a weighted score of the relevance score, the authority score, and the composite quality score. The weighting may be performed by calculating and adding the relevance score, the authority score, and the comprehensive quality score according to a preset ratio, or may be performed by directly adding. The preset threshold may be preset. The action score may be determined by counting the number of clicks of the candidate answer by the whole network user, or the stay time on the display page of the candidate answer. The behavioral characteristic score of a search result with a large number of clicks will be higher than that of a search result with a small number of clicks. As an example, the behavior score may be a result of dividing the number of clicks of the candidate answer by the sum of the number of clicks of the whole network user on each candidate answer in the candidate answer set, and multiplying the sum by 100. For example, the number of clicks of the candidate answer by the full-network user may be 80. The sum of the number of clicks of the full-network user on each candidate answer in the candidate answer set may be 10000. The behavior score may be 80/1000 x 100=8. As an example, the weighted score and the behavioral score may be added to generate a composite score.

In some alternative implementations of some embodiments, the weighted score is obtained by: calculating the correlation score of the target question and the candidate answer; determining authority scores and comprehensive quality scores of the candidate answers; and weighting the relevance score, the authority score and the comprehensive quality score to obtain the weighted score.

As an example, the target question and the candidate answer may be segmented to obtain a target question word group and a candidate answer word group. And generating word vectors corresponding to each word in the target question word group and the candidate answer word group to respectively obtain the target question word vector group and the candidate answer word vector group. And respectively adding each word vector in the target question word vector group and the candidate answer word vector group to obtain a target question feature vector and a candidate answer feature vector, and calculating by using a PageRank algorithm to obtain the relevance score. The authority score may be determined according to the content publisher to which the candidate answer corresponds. For example, if a question in the medical field, the authority score of a candidate answer issued by a professional will be higher than the authority score of an average person. The authority scores of different doctors are also different, and the authority score of an expert will be higher than that of the attending physician. The composite quality score may be determined based on interaction data of the candidate answer by a user viewing the candidate answer. The interaction data may be the praise number, comment number and forwarding number corresponding to the candidate answer. For example, the composite quality score of a candidate answer with a large number of interaction data will be higher than the composite quality score of a candidate answer with a small number of interaction data.

The information generating method disclosed in some embodiments of the present disclosure includes first obtaining a search result set of the above-mentioned target problem; then, extracting a target entity from the initial entity set based on the entity type matched with the target problem to obtain a target entity set; the entity is extracted from a large amount of information, so that a user can obtain answers more efficiently, and the searching time is further saved. Then, aggregating the target entities in the target entity set to obtain at least one aggregated entity and aggregated entity data related information corresponding to each aggregated entity; and finally, generating an entity information sequence based on the at least one aggregated entity and the aggregated entity data related information corresponding to each aggregated entity. The embodiment realizes the extraction of the targeted search results, saves the time of searching by the user, and improves the searching efficiency, thereby improving the use experience of the user.

Fig. 3 is a schematic diagram of one application scenario of a method for presenting information according to some embodiments of the present disclosure.

As shown in the application scenario of fig. 3, first, an execution subject of the method for presenting information may be the terminal device 301. The terminal device 301 may receive the question query request 302, and obtain a candidate answer set 303 and an entity information sequence 304 corresponding to the question query request 302; the entity information sequence 304 includes at least one target entity and data related information of the target entity, where the data related information includes candidate answer information corresponding to the target entity; displaying data-related information (shown as '689 answers, 89 answers, 67 answers, 60 answers') of a target number of the target entities (shown as 'bananas, coarse grains, soybeans, walnuts' in the figure) on a first preset position 306 on the information display page 305; the data-related information includes the number of each target entity and the identification of candidate answers corresponding to each entity. The identification may be an 8-bit 2-ary number. The above-mentioned identification may correspond to the candidate answer one by one. As an example, the entity data related information 307-310 of the target number (4 are shown in the figure) of target entities is selected from the entity information sequence 304 in order of the number corresponding to each target entity from the top to the bottom, and is displayed at the first preset position 306 on the information display page 305. A predetermined number (3 shown) of candidate answers 311-313 are selected from the candidate answer set 303 to be presented at a second preset location 314 on the information presentation page 305.

It will be appreciated that the method for generating information may be performed by the terminal device 301, or by other devices, or by various software programs. The terminal device 301 may be, for example, various electronic devices with a display screen, including but not limited to a smart phone, a tablet computer, an electronic book reader, a laptop portable computer, a desktop computer, and the like. The execution body may be embodied as a server, software, or the like. When the execution subject is software, the execution subject can be installed in the electronic device enumerated above. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein.

It should be understood that the number of terminal devices in fig. 3 is merely illustrative. There may be any number of terminal devices, as desired for implementation.

With continued reference to fig. 4, a flow 400 of some embodiments of a method for presenting information according to the present disclosure is shown. The method for displaying information comprises the following steps:

step 401, receiving a question query request, and obtaining a candidate answer set and an entity information sequence corresponding to the question query request.

In some embodiments, an execution subject (e.g., the terminal device 301 shown in fig. 3) of the method for presenting information may receive a question query request, and obtain a candidate answer set and an entity information sequence corresponding to the question query request. The entity information sequence comprises at least one target entity and data related information of the target entity, and the data related information comprises candidate answer information corresponding to the target entity. For example, a user may input a question query in a client in the form of voice, text, or the like, and the client may then send the question query input by the user to a search engine, so that the search engine may receive the question query sent by the client. The search engine can firstly acquire the webpage text corresponding to the problem inquiry request, and extract the target entity from the webpage text to obtain an entity information sequence. The candidate answer set may be a web page text set obtained by performing a coarse screening operation on the corresponding web page text.

Step 402, displaying a target number of the target entities and data related information of the target entities at a first preset position on the information display page.

In some embodiments, the execution body may display the target number of target entities and the data-related information of the target entities at a first preset position on the information display page. As an example, a target number of target entities and data related information of the target entities are selected from the entity sequence according to the order from the large number to the small number, and displayed at a first preset position on an information display page. The target number may be preset. The first preset position may be a preset position on the information display page.

Step 403, selecting a predetermined number of candidate answers from the candidate answer set, and displaying the selected candidate answers at a second preset position on the information display page.

In some embodiments, the executing entity may select a predetermined number of candidate answers from the candidate answer set to be presented at a second preset location on the information presentation page. As an example, a predetermined number of candidate answers may be selected from the candidate answer set in order of the score corresponding to each candidate answer from the large to the small, and presented at the second preset position on the above information presentation page. The second preset position may be a preset position on the information display page. The predetermined number may be preset.

As an example, in response to not detecting a triggering operation for the data-related information displayed at the first preset position, a predetermined number of candidate answers are selected from a candidate answer set corresponding to a target entity with the largest number of target entities, and displayed at a second preset position on the information display page. The candidate answer may be a search result meeting a target condition in the set of search results. The target condition may be that when the score of the search result reaches a target value or the score is from large to small, the search results in the search result set are ranked, and the target condition is a previous target proportion in the search result sequence. The target ratio may be a predetermined ratio. For example, the target proportion may be 10%. The triggering operation may be a click operation, a voice control, a slide operation, or the like.

In some optional implementations of some embodiments, determining a target candidate answer for each target entity; the target candidate answer may be a candidate answer including the target entity. And selecting a preset number of target candidate answers for each target entity to display. The predetermined number may be predetermined. As an example, the candidate answers including the target entity may be ranked according to the score from large to small, so as to obtain a candidate answer sequence of the target entity; and selecting the candidate answers with the previous target proportion in the candidate answer sequence. The target ratio may be a predetermined ratio. For example, the target proportion may be 10%.

In some optional implementations of some embodiments, the method further includes: responding to the triggering operation of detecting the data related information of the target entity displayed at the first preset position, and determining target entity candidate answers comprising the target entity from the candidate answer set based on the candidate answer information corresponding to the target entity; and displaying the entity candidate answers of the preset number of the item marks at a third preset position on the information display page. The third preset position may be the same as or different from the second preset position. As an example, the preset number of target entity candidate answers may be displayed on a third preset position on the information presentation page.

As another example, in response to detecting a triggering operation for data-related information of a target entity displayed at the first preset location, determining a target entity candidate answer including the target entity from the candidate answer set based on candidate answer information corresponding to the target entity; and displaying the entity candidate answers of the preset number of item marks on a candidate answer display page.

The method for displaying information disclosed in some embodiments of the present disclosure includes first, receiving a question query request, and obtaining a candidate answer set and an entity information sequence corresponding to the question query request; the entity information sequence comprises at least one target entity and data related information of the target entity, and the data related information comprises candidate answer information corresponding to the target entity. And then, displaying the target number of the target entities and the data related information of the target entities on a first preset position on the information display page. The user can intuitively see the data related information of the target number of entities corresponding to the problem inquiry request. And then selecting a preset number of candidate answers from the candidate answer set to display at a second preset position on the information display page. The implementation mode realizes diversified information display, and further enables a user to obtain answers more efficiently.

With further reference to fig. 5, as an implementation of the method described above for each of the above figures, the present disclosure provides some embodiments of an apparatus for presenting information, which apparatus embodiments correspond to those described above for fig. 2, and which apparatus is particularly applicable in a variety of electronic devices.

As shown in fig. 5, the information generating apparatus 500 of some embodiments includes: an acquisition unit 501, an extraction unit 502, an aggregation unit 503, and a generation unit 504. Wherein, the obtaining unit 501 is configured to obtain a search result set of the target problem in response to determining that the category of the target problem is an open problem category; an extracting unit 502 configured to extract a target entity from the initial entity set based on the entity type to which the target problem is matched, to obtain a target entity set; an aggregation unit 503, configured to aggregate the target entities in the target entity set to obtain at least one aggregated entity and related information of aggregated entity data corresponding to each aggregated entity; and a generating unit 504 configured to generate an entity information sequence based on the at least one aggregated entity and the aggregated entity data related information corresponding to each aggregated entity.

In some alternative implementations of some embodiments, the initial set of entities is obtained by: performing coarse screening operation on the search result set to obtain a candidate answer set; extracting entities from the candidate answer set to form an initial entity set; the initial entity set includes the extracted entity and the corresponding relation between the entity and the corresponding candidate answer.

In some optional implementations of some embodiments, the information generating apparatus 500 further includes: a first determining unit configured to perform intent analysis on the target problem, and determine a category of the target problem, wherein the category includes at least one of: open problem category and singleness problem category.

In some alternative implementations of some embodiments, the entity types are obtained by: acquiring an associated text of the target problem; word segmentation processing is carried out on the related text to obtain at least one word; determining weight scores of each word in the at least one word, and generating a weight score set; selecting a target word from the at least one word based on the weight subset; and generating the entity type based on the target word.

In some optional implementations of some embodiments, the information generating apparatus 500 is further configured to: calculating a weighted score of each candidate answer in the candidate answer set; generating a behavior score based on behavior features corresponding to the search results in response to the weighted score being greater than a preset threshold; based on the weighted scores and the behavioral scores, a composite score is generated.

It will be appreciated that the elements described in the apparatus 500 correspond to the various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting benefits described above with respect to the method are equally applicable to the apparatus 500 and the units contained therein, and are not described in detail herein.

With further reference to fig. 6, as an implementation of the method described above for each of the above figures, the present disclosure provides some embodiments of an apparatus for presenting information, which apparatus embodiments correspond to those described above for fig. 2, and which apparatus is particularly applicable in a variety of electronic devices.

As shown in fig. 6, an apparatus 600 for presenting information of some embodiments includes: an acquisition unit 601, a presentation unit 602 and a selection unit 603. The acquiring unit 601 is configured to receive a question query request, and acquire a candidate answer set and an entity information sequence corresponding to the question query request; the entity information sequence comprises at least one target entity and data related information of the target entity, wherein the data related information comprises candidate answer information corresponding to the target entity; a display unit 602 configured to display a target number of the target entities and data-related information of the target entities at a first preset position on an information display page; and a selecting unit 603 configured to select a predetermined number of candidate answers from the candidate answer set to be presented at a second preset position on the information presentation page.

In some optional implementations of some embodiments, the selection unit 603 in the apparatus 600 for presenting information is further configured to: determining target candidate answers corresponding to each target entity; and selecting a preset number of target candidate answers for each target entity to display.

In some alternative implementations of some embodiments, the means 600 for presenting information is further configured to: responding to the triggering operation of detecting the data related information of the target entity displayed at the first preset position, and determining target entity candidate answers comprising the target entity from the candidate answer set based on the candidate answer information corresponding to the target entity; and displaying the entity candidate answers of the preset number of the item marks at a third preset position on the information display page.

It will be appreciated that the elements described in the apparatus 600 correspond to the various steps in the method described with reference to fig. 4. Thus, the operations, features and resulting benefits described above with respect to the method are equally applicable to the apparatus 600 and the units contained therein, and are not described in detail herein.

Referring now to fig. 7, a schematic diagram of an electronic device (e.g., server in fig. 1) 700 suitable for use in implementing some embodiments of the present disclosure is shown. Terminal devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), car terminals (e.g., car navigation terminals), and the like, as well as stationary terminals such as digital TVs, desktop computers, and the like. The terminal device shown in fig. 7 is only one example, and should not impose any limitation on the functions and scope of use of the embodiments of the present disclosure.

As shown in fig. 7, the electronic device 700 may include a processing means (e.g., a central processor, a graphics processor, etc.) 701, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage means 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data required for the operation of the electronic device 700 are also stored. The processing device 701, the ROM 702, and the RAM703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

In general, the following devices may be connected to the I/O interface 705: input devices 706 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 707 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 708 including, for example, a memory card; and a communication device 709. The communication means 709 may allow the electronic device 700 to communicate wirelessly or by wire with other devices to exchange data. While fig. 7 shows an electronic device 700 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 7 may represent one device or a plurality of devices as needed.

In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 709, or from storage 708, or from ROM 702. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing means 701.

It should be noted that, in some embodiments of the present disclosure, the computer readable medium may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: in response to determining that the category of the target problem is an open problem category, acquiring a search result set of the target problem; extracting a target entity from the initial entity set based on the entity type matched with the target problem to obtain a target entity set; aggregating the target entities in the target entity set to obtain at least one aggregated entity and aggregated entity data related information corresponding to each aggregated entity; and generating an entity information sequence based on the at least one aggregated entity and the aggregated entity data related information corresponding to each aggregated entity.

Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes an acquisition unit, an extraction unit, a sorting unit, and a presentation unit. Where the names of these units do not constitute a limitation on the unit itself in some cases, for example, the acquisition unit may also be described as "a unit that acquires a set of search results of the above-described target problem in response to determining that the category of the target problem is an openness problem".

The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

According to one or more embodiments of the present disclosure, there is provided an information generating method including: in response to determining that the category of the target problem is an open problem category, acquiring a search result set of the target problem; extracting a target entity from the initial entity set based on the entity type matched with the target problem to obtain a target entity set; aggregating the target entities in the target entity set to obtain at least one aggregated entity and aggregated entity data related information corresponding to each aggregated entity; and generating an entity information sequence based on the at least one aggregated entity and the aggregated entity data related information corresponding to each aggregated entity.

According to one or more embodiments of the present disclosure, the initial set of entities is obtained by: performing coarse screening operation on the search result set to obtain a candidate answer set; extracting entities from the candidate answer set to form an initial entity set; the initial entity set includes the extracted entity and the corresponding relation between the entity and the corresponding candidate answer.

According to one or more embodiments of the present disclosure, before the obtaining the set of search results for the target issue in response to determining that the category of the target issue is an open issue category, the method further includes: performing intent analysis on the target problem to determine a category of the target problem, wherein the category comprises at least one of the following: open problem category and singleness problem category.

According to one or more embodiments of the present disclosure, the above entity types are obtained by: acquiring an associated text of the target problem; word segmentation processing is carried out on the related text to obtain at least one word; determining weight scores of each word in the at least one word, and generating a weight score set; selecting a target word from the at least one word based on the weight subset; and generating the entity type based on the target word.

According to one or more embodiments of the present disclosure, the above method further comprises: calculating a weighted score of each candidate answer in the candidate answer set; generating a behavior score based on behavior features corresponding to the search results in response to the weighted score being greater than a preset threshold; based on the weighted scores and the behavioral scores, a composite score is generated.

According to one or more embodiments of the present disclosure, the weighted score is obtained by: calculating the correlation score of the target question and the candidate answer; determining authority scores and comprehensive quality scores of the candidate answers; and weighting the relevance score, the authority score and the comprehensive quality score to obtain the weighted score.

According to one or more embodiments of the present disclosure, there is provided a method for presenting information, comprising: receiving a question query request, and acquiring a candidate answer set and an entity information sequence corresponding to the question query request; the entity information sequence comprises at least one target entity and data related information of the target entity, wherein the data related information comprises candidate answer information corresponding to the target entity; displaying a target number of the target entities and data related information of the target entities on a first preset position on an information display page; and selecting a preset number of candidate answers from the candidate answer set to be displayed at a second preset position on the information display page.

According to one or more embodiments of the present disclosure, the selecting a predetermined number of candidate answers from the candidate answer set for presentation at a second preset location on the information presentation page includes: determining target candidate answers corresponding to each target entity; and selecting a preset number of target candidate answers for each target entity to display.

According to one or more embodiments of the present disclosure, the above method further comprises: responding to the triggering operation of detecting the data related information of the target entity displayed at the first preset position, and determining target entity candidate answers comprising the target entity from the candidate answer set based on the candidate answer information corresponding to the target entity; and displaying the entity candidate answers of the preset number of the item marks at a third preset position on the information display page.

According to one or more embodiments of the present disclosure, there is provided an information generating apparatus including: an acquisition unit configured to acquire a set of search results of the target problem in response to determining that a category of the target problem is an open problem category; the extraction unit is configured to extract target entities from the initial entity set based on the entity types matched with the target problems to obtain a target entity set; the aggregation unit is configured to aggregate the target entities in the target entity set to obtain at least one aggregated entity and the related information of the aggregated entity data corresponding to each aggregated entity; and the generating unit is configured to generate an entity information sequence based on the at least one aggregated entity and the aggregated entity data related information corresponding to each aggregated entity.

According to one or more embodiments of the present disclosure, further comprising: a first determining unit that performs intent analysis on the target problem and determines a category of the target problem, wherein the category includes at least one of: open problem category and singleness problem category.

According to one or more embodiments of the present disclosure, the above-described apparatus is further configured to: calculating a weighted score of each candidate answer in the candidate answer set; generating a behavior score based on behavior features corresponding to the search results in response to the weighted score being greater than a preset threshold; based on the weighted scores and the behavioral scores, a composite score is generated.

According to one or more embodiments of the present disclosure, there is provided an apparatus for displaying information, including: an obtaining unit configured to receive a question query request, and obtain a candidate answer set and an entity information sequence corresponding to the question query request; the entity information sequence comprises at least one target entity and data related information of the target entity, wherein the data related information comprises candidate answer information corresponding to the target entity; the display unit is configured to display a target number of the target entities and data related information of the target entities on a first preset position on the information display page; and a selecting unit configured to select a predetermined number of candidate answers from the candidate answer set to be presented at a second preset position on the information presentation page.

According to one or more embodiments of the present disclosure, the selection unit in the above apparatus is further configured to: determining target candidate answers corresponding to each target entity; and selecting a preset number of target candidate answers for each target entity to display.

According to one or more embodiments of the present disclosure, the above-described apparatus is further configured to: responding to the triggering operation of detecting the data related information of the target entity displayed at the first preset position, and determining target entity candidate answers comprising the target entity from the candidate answer set based on the candidate answer information corresponding to the target entity; and displaying the entity candidate answers of the preset number of the item marks at a third preset position on the information display page.

According to one or more embodiments of the present disclosure, there is provided an electronic device including: one or more processors; and a storage device having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the method as described in any of the embodiments above.

According to one or more embodiments of the present disclosure, there is provided a computer readable medium having stored thereon a computer program, wherein the program, when executed by a processor, implements a method as described in any of the embodiments above.

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.

Claims

1. An information generation method, comprising:

in response to determining that the category of the target question is an open question category, obtaining a search result set of the target question;

extracting a target entity from the initial entity set based on the entity type matched with the target problem to obtain a target entity set; wherein the entity type is obtained by the following steps: acquiring an associated text of the target problem; word segmentation processing is carried out on the associated text to obtain at least one word; determining weight scores of all words in the at least one word, and generating a weight score set; selecting target words from the at least one word according to the sequence from the big weight to the small weight based on the weight score set; generating the entity type based on the target word, including: performing relevance scoring on the target words and each target word in a target vocabulary to obtain a relevance score set; selecting target words from the target words according to the degree of association, and taking the selected target words as entity types;

Aggregating the target entities in the target entity set to obtain at least one aggregated entity and aggregated entity data related information corresponding to each aggregated entity;

and generating an entity information sequence based on the at least one aggregated entity and the aggregated entity data related information corresponding to each aggregated entity.

2. The method of claim 1, wherein the initial set of entities is obtained by:

performing coarse screening operation on the search result set to obtain a candidate answer set;

extracting entities from the candidate answer set to form an initial entity set; the initial entity set comprises the extracted entity and the corresponding relation between the entity and the corresponding candidate answer.

3. The method of claim 1, wherein prior to the obtaining the set of search results for the target issue in response to determining that the category of target issue is an open issue category, the method further comprises:

performing intent analysis on the target problem to determine a category of the target problem, wherein the category comprises at least one of the following: open problem category and singleness problem category.

4. The method of claim 1, wherein the method further comprises:

calculating a weighted score of each candidate answer in the candidate answer set;

generating a behavior score based on behavior features corresponding to the search results in response to the weighted score being greater than a preset threshold;

based on the weighted scores and the behavioral scores, a composite score is generated.

5. The method of claim 4, wherein the weighted score is obtained by:

calculating a relevance score of the target question and the candidate answer;

determining authority scores and comprehensive quality scores of the candidate answers;

and weighting the relevance score, the authority score and the comprehensive quality score to obtain the weighted score.

6. A method for displaying information, comprising:

receiving a question query request, and acquiring a candidate answer set corresponding to the question query request and an entity information sequence obtained by the information generation method according to any one of claims 1-5; wherein,

the entity information sequence comprises at least one target entity and data related information of the target entity, wherein the data related information comprises candidate answer information corresponding to the target entity;

Displaying a target number of target entities and data related information of the target entities on a first preset position on an information display page;

and selecting a preset number of candidate answers from the candidate answer set to be displayed at a second preset position on the information display page.

7. The method of claim 6, wherein the selecting a predetermined number of candidate answers from the set of candidate answers for presentation at a second preset location on the information presentation page comprises:

determining target candidate answers corresponding to each target entity;

and selecting a preset number of target candidate answers for each target entity for display.

8. The method of claim 6, wherein the method further comprises:

in response to detecting a triggering operation of data-related information for a target entity presented at the first preset location,

determining target entity candidate answers comprising the target entity from the candidate answer set based on candidate answer information corresponding to the target entity;

and displaying the entity candidate answers of the preset number of the item marks at a third preset position on the information display page.

9. An information generating apparatus comprising:

An acquisition unit configured to acquire a set of search results of a target question, which is an answer to the target question, in response to determining that a category of the target question is an open question category;

the extraction unit is configured to extract target entities from the initial entity set based on the entity types matched with the target problems to obtain a target entity set; wherein the entity type is obtained by the following steps: acquiring an associated text of the target problem; word segmentation processing is carried out on the associated text to obtain at least one word; determining weight scores of all words in the at least one word, and generating a weight score set; selecting target words from the at least one word according to the sequence from the big weight to the small weight based on the weight score set; generating the entity type based on the target word, including: performing relevance scoring on the target words and each target word in a target vocabulary to obtain a relevance score set; selecting target words from the target words according to the degree of association, and taking the selected target words as entity types;

The aggregation unit is configured to aggregate the target entities in the target entity set to obtain at least one aggregated entity and the related information of the aggregated entity data corresponding to each aggregated entity;

and the generating unit is configured to generate an entity information sequence based on the at least one aggregated entity and the aggregated entity data related information corresponding to each aggregated entity.

10. An apparatus for displaying information, comprising:

an obtaining unit configured to receive a question query request, and obtain a candidate answer set corresponding to the question query request and an entity information sequence obtained by an information generating method according to any one of claims 1 to 5; the entity information sequence comprises at least one target entity and data related information of the target entity, wherein the data related information comprises candidate answer information corresponding to the target entity;

the display unit is configured to display a target number of target entities and data related information of the target entities on a first preset position on an information display page;

and a selection unit configured to select a predetermined number of candidate answers from the candidate answer set to be presented at a second preset position on the information presentation page.

11. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon;

when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-5, 6-8.

12. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1-5, 6-8.