CN112052297A

CN112052297A - Information generation method and device, electronic equipment and computer readable medium

Info

Publication number: CN112052297A
Application number: CN202010930563.6A
Authority: CN
Inventors: 林馨怡; 彭婉莹; 汪忠超
Original assignee: Beijing ByteDance Network Technology Co Ltd
Current assignee: Beijing ByteDance Network Technology Co Ltd
Priority date: 2020-09-07
Filing date: 2020-09-07
Publication date: 2020-12-08
Anticipated expiration: 2040-09-07
Also published as: CN112052297B

Abstract

The embodiment of the disclosure discloses an information generation method, an information generation device, electronic equipment and a computer readable medium. One embodiment of the method comprises: 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 by 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. The embodiment realizes the extraction of the targeted search results, saves the search time of the user, and improves the search efficiency, thereby improving the use experience of the user.

Description

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

Technical Field

The embodiment of the disclosure relates to the technical field of computers, in particular to a method and a device for generating information, electronic equipment and a computer readable medium.

Background

With the advancement of the internet, the information on the network is more complex, and a simple question can be answered from many levels and from different angles. When a user finds a desired answer in a search result, much time is wasted, resulting in low search efficiency.

Disclosure of Invention

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary 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 generating 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, including: 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 by 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 at 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 comprising: an acquisition unit configured to acquire a search result set of the target question in response to determining that the category of the target question is an open question category; the extracting unit is configured to extract a target entity from the initial entity set based on the entity type matched by the target problem 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 aggregated entity data related information 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 presenting information, the apparatus comprising: the system comprises an acquisition unit, a query unit and a query unit, wherein the acquisition unit 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; the display unit is 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 the selecting unit is configured to select 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 fifth aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement any of the methods described above.

In a sixth aspect, some embodiments of the disclosure provide a computer readable medium having a computer program stored thereon, where the program is to implement any of the above-mentioned methods when executed by a processor.

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 by the target problem to obtain a target entity set; and an entity is extracted from a large amount of information, so that a user can more efficiently obtain answers, 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 search time of the user, and improves the search efficiency, thereby improving the use experience of the user.

Drawings

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

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

FIG. 2 is a flow diagram 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, in accordance with some embodiments of the present disclosure;

FIG. 4 is a flow diagram of some embodiments of a method for presenting information in accordance with the present disclosure;

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

FIG. 6 is a schematic block diagram of some embodiments of an apparatus for displaying information in accordance with 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 is to be understood that the 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 for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.

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

It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.

It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.

The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the 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, in accordance with some embodiments of the present disclosure.

As shown in the application scenario of fig. 1, first, the execution subject of the information generation method may be the server 101. The server 101 may obtain the search result set 103 of 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 eaten in constipation? ". The open question category generally refers to questions with multiple answers. Then, based on the entity type 104 matched to the target question 102, extracting a target entity from the initial entity set 105 to obtain a target entity set 106; the initial entity set 105 is obtained based on the search result set 103. Then, aggregating the target entities in the target entity set 106 to obtain at least one aggregated

entity

107 and 109 and the aggregated entity data related information corresponding to each aggregated entity; as an example, the entity type 104 may be "food". The post-polymerization entity 107 can be "banana", the post-polymerization entity 108 can be "roughage", and the post-polymerization entity 109 can be "soybean". The post-aggregation entity data related information corresponding to the post-aggregation entity 107 may be "689 pieces", and the post-aggregation entity data related information corresponding to the post-aggregation entity 108 may be "89 pieces", and the post-aggregation entity data related information corresponding to the post-aggregation entity 109 may be "68 pieces". Finally, an entity information sequence 110 is generated based on at least one post-aggregation entity 107-109 and the post-aggregation entity data related information corresponding to each post-aggregation entity. The entity information sequence 110 can be banana, coarse grain and soybean.

It is understood that the information generating method may be executed by the server machine 101, or may be executed by other devices, or may be executed by various software programs. Furthermore, the execution body may be various electronic devices having a display screen, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, desktop computers, and the like. When the execution subject is software, the software can be installed in the electronic device listed above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And 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 an information generation method according to the present disclosure is shown. The information generation method comprises the following steps:

step 201, in response to determining that the category of the target question is the openness question category, acquiring a search result set of the target question.

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

In some optional implementations of some embodiments, before obtaining the set of search results for the target problem in response to determining that the category of the target problem is the open problem category, the method further includes: performing intention analysis on the target problem, and determining the category of the target problem, wherein the category comprises at least one of the following items: the open problem category and the unicity problem category. The above-mentioned category of simplex questions generally refers to questions having 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 the openness problem category. The intent model may be trained with the sample question as input and the sample score as the desired output.

As an example, the intention model may be obtained by performing the following training steps based on a set of training samples: respectively inputting the sample problems 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 problem 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 is greater than the preset accuracy threshold, taking the initial machine learning model as a trained intention model; and adjusting parameters of the initial machine learning model in response to the determination that the accuracy is not greater than the preset accuracy threshold, forming a training sample set by using unused training samples, using the adjusted initial machine learning model as the initial machine learning model, and executing the training step again.

It will be appreciated that after the above training, the above intent model may be used to characterize the correspondence between sample questions and sample scores. The intention model mentioned above may be the bert (bidirectional Encoder expressions from transformations) algorithm. The Bert algorithm described above may be a new type of language model. It is a new language model because it trains a pre-trained deep bi-directional representation by jointly adjusting bi-directional transformers in all layers.

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

In some embodiments, the execution subject may extract the target entity from the initial entity set based on the entity type matched by the target problem, to obtain a target entity set. Here, when the matched entity type is "food". The set of initial entities may be 'gut digestion', 'gut motility', 'hypomotility', 'inflammation', 'banana', 'roughage', 'soy', 'walnut'. The set of target entities may then be 'banana', 'roughage', 'soybean', 'walnut'.

By way of example, the entity type may be identified using dependency analysis, template matching, etc. in conjunction with CRF (Conditional Random Field). As an example, the entity type of the target question is determined according to the matching result of the target question and a preset template. For example, the preset templates include: "what [ category word ] is good, the above target question may be" what watch is good ", and the entity type that can be analyzed for this target question is" watch ".

In some optional implementations of some embodiments, the entity type is obtained by: acquiring a relevant text of the target problem; for example, when the target question is "china female star", the associated text may be "china female star". And when the target question is voice, recognizing voice content in the voice, and using the recognized voice content as the associated text of the target question. Performing word segmentation processing on the associated text to obtain at least one word; here, the word segmentation process may be to perform word segmentation on the information related to the article by using a word segmentation device. Determining the weight score of each word in the at least one word, and generating a weight score set; as an example, each word may be scored based on different algorithms, resulting in a weight score, generating a set of weight scores. For example, the above 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 distribution set; as an example, the execution subject may select the target word from the at least one word in order of the weight scores from large to small. And generating the entity type based on the target words.

As an example, the target words and each target word in the target vocabulary are subjected to relevancy scoring to obtain a relevancy score set; and selecting a target word from the target vocabulary according to the degree of association score, and taking the selected target word as an entity type. As an example, when the above-mentioned target question is "what is eaten in constipation? "when, the key to extract is" what to eat ". The target words in the target vocabulary may be 'food', 'car', 'ornament', 'clothes', 'star'. An association score between the keyword and each target word may be calculated using inter-Point Mutual Information (PMI). The inter-Point Mutual Information (PMI) is mainly used for calculating semantic similarity between words, and the basic idea is to count the probability of simultaneous occurrence of two words in a text, wherein if the probability is higher, the correlation is tighter, and the association degree is higher. The set of association scores may be "'80', '20', '10', '40'. The target word selected from the target vocabulary by the size of the relevancy score may be "food" and the entity type matched may be "food".

In some optional implementations of some embodiments, the initial entity set is obtained by: carrying out coarse screening operation on the search result set to obtain a candidate answer set; the coarse screening may be performed based on a source of each search result in the set of search results. As an example, when the above-mentioned target question is "what is eaten in constipation? "the source of each search result may be a hospital of a different level, an individual, or from a different website. Then we can extract the search results whose source is hospital from the above search result set to compose a candidate answer set. Extracting entities from the candidate answer set to form an initial entity set; the initial entity set comprises the extracted entities and the corresponding relations between the entities and the corresponding candidate answers. The correspondence may be an identifier of a candidate answer extracted from the entity. The above identification may be an 8-bit 2-ary number. The above-mentioned identifications may correspond one-to-one to the candidate answers. As an example, the candidate answer sets in the candidate answer sets are sequentially input into an entity extraction model to obtain at least one initial entity, and an initial entity set is generated. The entity extraction model can be obtained by training through a training sample set. The training samples in the training sample set comprise sample candidate answers and sample entities, the entity extraction model takes the sample candidate answers as input, and the sample entities are obtained by training as expected output.

Step 203, 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.

In some embodiments, the execution main 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 set of target entities are "corn", "potato", can be aggregated into the entity "roughage", and then the aggregated entity is "roughage". As an example, the set of target entities may be "corn, potato, banana, walnut", wherein the number of the above-mentioned aggregated entities "roughage" 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 main body may generate the entity information sequence based on the at least one post-aggregation entity and the post-aggregation entity data related information corresponding to each post-aggregation entity. The at least one aggregated entity may be sorted according to the number of the aggregated entity data related information from large to small. As an example, when the number of the aggregated entities "bananas" is 689, the number of "roughages" is 89, the number of "soybeans" is 67, and the number of "walnuts" is 60. The obtained volume information sequence can be banana, coarse grain, soybean and walnut.

In some optional implementations of some embodiments, the method further comprises: calculating the weighted score of each candidate answer in the candidate answer set; responding to the weighted score larger than a preset threshold value, and generating a behavior score based on the behavior characteristics corresponding to the search result; and generating a comprehensive score based on the weighted score and the behavior score. The weighted score may be a score obtained by weighting the relevance score, the authority score, and the overall quality score. The weighting may be calculated and added according to a preset ratio by the relevance score, the authority score and the comprehensive quality score, or may be directly added. The preset threshold may be preset. The behavior score may be determined by counting the number of clicks of the candidate answer by the web-wide user or the length of time spent on the presentation page of the candidate answer. The behavioral characteristic score of the search results with the higher number of clicks will be higher than the behavioral characteristic score of the search results with the lower number of clicks. For example, the behavior score may be the sum of the number of clicks of the candidate answer set by the web user divided by the number of clicks of each candidate answer set by the web user, and then multiplied by 100. For example, the number of clicks of the above candidate answers by the web-wide user may be 80. The sum of the number of clicks of the network-wide user on each candidate answer in the candidate answer set may be 10000. Then the behavior score may be 80/1000 x 100-8. As an example, the weighted score and the behavior score may be added to generate a composite score.

In some optional implementations of some embodiments, 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.

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 a target question word vector group and a 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 corresponding to the candidate answer. For example, if it is a question in the medical field, the candidate answers issued by the medical professional will have an authority score higher than that of the average person. The authoritative scores of different physicians will also vary, and the authoritative score of the specialist will be higher than the authoritative score of the attending physician. The composite quality score may be determined according to interaction data of a user viewing the candidate answer with respect to the candidate answer. The interaction data can be the number of praise, the number of comment and the forwarding number corresponding to the candidate answer. For example, the composite quality score for a large number of candidate answers with interaction data will be higher than the composite quality score for a small number of candidate answers with interaction data.

In the information generating method disclosed in some embodiments of the present disclosure, first, a search result set of the target problem is obtained; then, extracting a target entity from the initial entity set based on the entity type matched by the target problem to obtain a target entity set; and an entity is extracted from a large amount of information, so that a user can more efficiently obtain answers, 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 search time of the user, and improves the search 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, in accordance with some embodiments of the present disclosure.

As shown in the application scenario of fig. 3, first, the 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 a target number of the target entities (shown as "banana, roughage, soybean, walnut") and data-related information of the target entities (shown as "689 answers, 89 answers, 67 answers, 60 answers") at a first preset position 306 on an information display page 305; the data-related information includes the number of each target entity and the identification of the candidate answer corresponding to each entity. The above identification may be an 8-bit 2-ary number. The above-mentioned identifications may correspond one-to-one to the candidate answers. As an example, the entity data related information 307-310 of the target entities with the target number (4 shown in the figure) is selected from the entity information sequence 304 in the descending order of the number corresponding to each target entity to be displayed at the first preset position 306 on the information display page 305. A predetermined number (3 shown in the figure) of candidate answers 311-.

It will be appreciated that the method for generating information may be performed by the terminal device 301, or may be performed by other devices, or may be performed 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 e-book reader, a laptop portable computer, a desktop computer, and the like. Furthermore, the execution body may also be embodied as a server, software, or the like. When the execution subject is software, the software can be installed in the electronic device listed above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And 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 in accordance with the present disclosure is shown. The method for displaying information comprises the following steps:

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

In some embodiments, an executing body (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, wherein the data related information comprises candidate answer information corresponding to the target entity. For example, a user may input a question query request in the form of voice, text, etc. in the client, and then the client may transmit the question query request input by the user to the search engine, so that the search engine may receive the question query request transmitted by the client. The search engine may first obtain a web page text corresponding to the question query request, and extract a target entity from the web page text to obtain an entity information sequence. The candidate answer set may be a webpage text set obtained by performing a rough screening operation on the corresponding webpage 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 an information display page.

In some embodiments, the execution subject may present a target number of target entities and data related information of the target entities at a first preset position on an information presentation page. As an example, a target number of target entities and data related information of the target entities are selected from the entity sequence in a descending order of the number corresponding to each target entity to be displayed at a first preset position on an information display page. The target number may be predetermined. 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 to be displayed at a second preset position on the information display page.

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

As an example, in response to that no trigger operation for the data-related information displayed at the first preset position is detected, a predetermined number of candidate answers are selected from the candidate answer set corresponding to the target entity with the largest number of target entities to be 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 search result set. The target condition may be that when the score of the search result reaches a target value or the score is decreased from large to small, the search results in the search result set are sorted in proportion to the previous target in the search result sequence. The target ratio may be a predetermined ratio. For example, the target proportion may be 10%. The trigger operation can be click operation, voice control, sliding operation and the like.

In some optional implementations of some embodiments, a target candidate answer corresponding to each target entity is determined; 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 scores from large to small to obtain a candidate answer sequence of the target entity; and selecting the candidate answers with the front 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 comprises: in response to detecting a trigger operation for data-related information of a target entity shown in the first preset position, 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 candidate answers of the entity of the preset number of items on 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 candidate answers to the predetermined number of entry entities may be displayed at a third predetermined position on the information presentation page.

As another example, in response to detecting a trigger operation for data-related information of a target entity shown in the first preset position, a target entity candidate answer including the target entity is determined from the candidate answer set based on candidate answer information corresponding to the target entity; and displaying the candidate answers of the entity of the preset number of entry marks on a candidate answer display page.

The method for displaying information disclosed by 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, wherein the data related information comprises candidate answer information corresponding to the target entity. Then, displaying 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. The user can visually see the data related information of the target number of entities corresponding to the question query request. And then, 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. The realization mode realizes diversified information display, and then the user can more efficiently obtain answers.

With further reference to fig. 5, as an implementation of the above-described methods for the above-described figures, the present disclosure provides some embodiments of an apparatus for presenting information, which correspond to those of the method embodiments described above for fig. 2, and which may be applied in particular to various 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. The obtaining unit 501 is configured to obtain a search result set of the target question in response to determining that the category of the target question is an open question category; an extracting unit 502, configured to extract a target entity from the initial entity set based on the entity type matched by the target problem, so as 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 aggregated entity data related information corresponding to each aggregated entity; and the generating unit 504 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 some optional implementations of some embodiments, the initial entity set is obtained by: carrying out 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 entities and the corresponding relations between the entities and the corresponding candidate answers.

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: the open problem category and the unicity problem category.

In some optional implementations of some embodiments, the entity type is obtained by: acquiring a relevant text of the target problem; performing word segmentation processing on the associated text to obtain at least one word; determining the weight score 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 distribution set; and generating the entity type based on the target words.

In some optional implementations of some embodiments, the information generating apparatus 500 is further configured to: calculating the weighted score of each candidate answer in the candidate answer set; responding to the weighted score larger than a preset threshold value, and generating a behavior score based on the behavior characteristics corresponding to the search result; and generating a comprehensive score based on the weighted score and the behavior score.

It will be understood that the elements described in the apparatus 500 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 500 and the units included therein, and are not described herein again.

With further reference to fig. 6, as an implementation of the above-described methods for the above-described figures, the present disclosure provides some embodiments of an apparatus for presenting information, which correspond to those of the method embodiments described above for fig. 2, and which may be applied in particular to various 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 obtaining unit 601 is 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; a presentation unit 602 configured to present a target number of the target entities and data related information of the target entities at a first preset position on an information presentation page; and the selecting unit 603 is configured to select a predetermined number of candidate answers from the candidate answer set to be displayed at a second predetermined position on the information display page.

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

In some optional implementations of some embodiments, the means for presenting information 600 is further configured to: in response to detecting a trigger operation for data-related information of a target entity shown in the first preset position, 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 candidate answers of the entity of the preset number of items on a third preset position on the information display page.

It will be understood that the elements described in the apparatus 600 correspond to various steps in the method described with reference to fig. 4. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 600 and the units included therein, and are not described herein again.

Referring now to fig. 7, a schematic diagram of an electronic device (e.g., the server of fig. 1) 700 suitable for use in implementing some embodiments of the present disclosure is shown. The terminal device in some embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. The terminal device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the use range of the embodiments of the present disclosure.

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

Generally, the following devices may be connected to the I/O interface 705: input devices 706 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 707 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; a storage device 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 illustrates an electronic device 700 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 7 may represent one device or may represent multiple devices as desired.

In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams 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 illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network via communications means 709, or may be installed from storage 708, or may be installed from ROM 702. The computer program, when executed by the processing device 701, performs the above-described functions defined in the methods of some embodiments of the present disclosure.

It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination 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 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, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications 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 network.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled 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 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 by 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 embodiments of the present disclosure may be written in any combination of 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 type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart 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 software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be 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 in some cases constitute a limitation on the unit itself, for example, the obtaining unit may also be described as a "unit that obtains a search result set of the above-described target question in response to determining that the category of the target question is an open question".

The functions described herein above 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: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), 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 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 by 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 entity set is obtained by: carrying out 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 entities and the corresponding relations between the entities and the corresponding candidate answers.

According to one or more embodiments of the present disclosure, before the obtaining of the search result set of the target question in response to determining that the category of the target question is the open question category, the method further includes: performing intention analysis on the target problem, and determining the category of the target problem, wherein the category comprises at least one of the following items: the open problem category and the unicity problem category.

According to one or more embodiments of the present disclosure, the entity type is obtained by: acquiring a relevant text of the target problem; performing word segmentation processing on the associated text to obtain at least one word; determining the weight score 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 distribution set; and generating the entity type based on the target words.

According to one or more embodiments of the present disclosure, the method further includes: calculating the weighted score of each candidate answer in the candidate answer set; responding to the weighted score larger than a preset threshold value, and generating a behavior score based on the behavior characteristics corresponding to the search result; and generating a comprehensive score based on the weighted score and the behavior score.

According to one or more embodiments of the present disclosure, 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.

In accordance with 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 at 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 to be displayed at a second preset position on the information display page includes: determining a target candidate answer 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 method further includes: in response to detecting a trigger operation for data-related information of a target entity shown in the first preset position, 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 candidate answers of the entity of the preset number of items on 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 search result set of the target question in response to determining that the category of the target question is an open question category; the extracting unit is configured to extract a target entity from the initial entity set based on the entity type matched by the target problem 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 aggregated entity data related information 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, 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: the open problem category and the unicity problem category.

According to one or more embodiments of the present disclosure, the apparatus described above is further configured to: calculating the weighted score of each candidate answer in the candidate answer set; responding to the weighted score larger than a preset threshold value, and generating a behavior score based on the behavior characteristics corresponding to the search result; and generating a comprehensive score based on the weighted score and the behavior score.

According to one or more embodiments of the present disclosure, there is provided an apparatus for presenting information, including: the system comprises an acquisition unit, a query unit and a query unit, wherein the acquisition unit 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; the display unit is 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 the selecting unit is configured to select 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 selection unit in the above apparatus is further configured to: determining a target candidate answer 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 apparatus described above is further configured to: in response to detecting a trigger operation for data-related information of a target entity shown in the first preset position, 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 candidate answers of the entity of the preset number of items on 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; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement a method as described in any of the embodiments above.

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

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology 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-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.

Claims

1. An information generating 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 by 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.

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

carrying out 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; wherein the initial entity set comprises the extracted entities and the corresponding relations between the entities and the corresponding candidate answers.

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

performing intent analysis on the target problem, and determining a category of the target problem, wherein the category comprises at least one of the following items: the open problem category and the unicity problem category.

4. The method of claim 1, wherein the entity type is obtained by:

acquiring a relevant text of the target problem;

performing word segmentation processing on the associated text to obtain at least one word;

determining the weight score 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 set of weight scores;

generating the entity type based on the target term.

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

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

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

generating a composite score based on the weighted score and the behavioral score.

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

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

determining an authority score and a composite quality score of the candidate answer;

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

7. 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 target entities and data related information of the target entities at 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 on a second preset position on the information display page.

8. The method of claim 7, wherein the selecting a predetermined number of candidate answers from the candidate answer set to be presented at a second preset position on the information presentation page comprises:

determining a target candidate answer corresponding to each target entity;

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

9. The method of claim 7, wherein the method further comprises:

in response to detecting a trigger operation for data-related information of a target entity shown on the first preset position, determining a target entity candidate answer comprising the target entity from the candidate answer set based on candidate answer information corresponding to the target entity;

displaying candidate answers of a preset number of entry mark entities at a third preset position on the information display page.

10. An information generating apparatus comprising:

an acquisition unit configured to acquire a search result set of a target question in response to determining that a category of the target question is an open question category;

the extracting unit is configured to extract a target entity from the initial entity set based on the entity type matched by the target problem 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 aggregated entity data related information corresponding to each aggregated entity;

a generating unit configured to generate an entity information sequence based on the at least one post-aggregation entity and the post-aggregation entity data related information corresponding to each post-aggregation entity.

11. An apparatus for presenting information, comprising:

the system comprises an acquisition unit, a query unit and a query unit, wherein the acquisition unit 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;

the display unit is 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;

the selection unit is configured to select a preset number of candidate answers from the candidate answer set to be displayed on a second preset position on the information display page.

12. 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, cause the one or more processors to implement the method of any one of claims 1-6, 7-9.

13. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-6, 7-9.