CN111259659A - Information processing method and device - Google Patents
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Abstract
The embodiment of the application discloses an information processing method and device. One embodiment of the method comprises: acquiring a word combination to be processed, wherein the word combination to be processed comprises an entity and the attribute of the entity; determining a knowledge type corresponding to the word combination to be processed in a preset structured data set, and determining attributes belonging to the knowledge type as candidate attributes, wherein the candidate attributes comprise at least two; and determining candidate attributes corresponding to the attributes in the word combination to be processed based on the similarity between at least two candidate attributes and the word combination to be processed. According to the embodiment of the application, the candidate attribute corresponding to the attribute in the word combination can be quickly and accurately determined in the preset structured data set, so that the unfamiliar word combination can be automatically associated to the structured data, the labor consumption is avoided, and the association efficiency and accuracy are improved.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to the technical field of internet, and particularly relates to an information processing method and device.
Background
With the development of internet technology, a huge amount of information is generated on the internet every day. The information has various sources and contents, and how to collect and arrange the information is a problem to be solved.
Because the vocabulary is very flexible to use, the same vocabulary can have multiple usages in different scenes, and therefore, the collected vocabulary is generally required to be manually organized.
Disclosure of Invention
The embodiment of the application provides an information processing method and device.
In a first aspect, an embodiment of the present application provides an information processing method, including: acquiring a word combination to be processed, wherein the word combination to be processed comprises an entity and the attribute of the entity; determining a knowledge type corresponding to a word combination to be processed in a preset structured data set, and determining attributes belonging to the knowledge type as candidate attributes, wherein the candidate attributes comprise at least two; and determining candidate attributes corresponding to the attributes in the word combination to be processed based on the similarity between the at least two candidate attributes and the word combination to be processed.
In some embodiments, the word combinations to be processed further include attribute values associated with the attributes; determining a knowledge type corresponding to a word combination to be processed in a preset structured data set, wherein the knowledge type comprises the following steps: and determining the knowledge type of the concept of the entity and the knowledge type of the concept of the attribute value in the preset structured data set, wherein the knowledge type of the entity and the knowledge type of the attribute value are at least one.
In some embodiments, determining a knowledge type corresponding to a word combination to be processed in a preset structured data set includes: carrying out upper processing on the entity to obtain an upper word of the entity; and determining a knowledge type corresponding to the superior word of the entity in a preset structured data set, and taking the knowledge type as a knowledge type corresponding to the word combination to be processed.
In some embodiments, the word combinations to be processed further include attribute values associated with the attributes; the method further comprises the following steps: carrying out upper processing on the attribute value to obtain an upper word of the attribute value; and determining a knowledge type corresponding to the hypernym of the entity, and using the knowledge type as a knowledge type corresponding to the word combination to be processed, including: determining a knowledge type corresponding to a hypernym of the entity, and determining a knowledge type corresponding to a hypernym of the attribute value; and taking the knowledge type corresponding to the hypernym of the entity and the knowledge type corresponding to the hypernym of the attribute value as the knowledge type corresponding to the word combination to be processed.
In some embodiments, before determining the candidate attribute corresponding to the attribute in the word combination to be processed based on the similarity between at least two candidate attributes and the word combination to be processed, the method further includes: for each of at least two of an entity, an attribute and an attribute value in a word combination to be processed, determining a feature of the entity, the attribute and the attribute value, wherein the feature of each of the at least two includes at least two; fusing the characteristics of at least two of the words, and taking the fusion result as the characteristics of the word combination to be processed; and determining candidate attributes corresponding to the attributes in the word combinations to be processed based on the similarity between the at least two candidate attributes and the word combinations to be processed, wherein the candidate attributes comprise: ordering the similarity between the features of the word combination to be processed and the features of the at least two candidate attributes; and taking the candidate attribute corresponding to the highest similarity in the obtained similarity sequence as the candidate attribute corresponding to the attribute in the word combination to be processed.
In some embodiments, ranking the similarity between the features of the word combination to be processed and the features of the at least two candidate attributes comprises: inputting the characteristics of the word combination to be processed and the characteristics of the at least two candidate attributes into a pre-trained sequencing model, and sequencing the similarity between the characteristics of the word combination to be processed and the characteristics of the at least two candidate attributes through the pre-trained sequencing model.
In some embodiments, the method further comprises: for each of at least one of an entity, an attribute, and an attribute value in a word combination to be processed, determining a feature of the one, wherein the feature of each of the at least one includes a fused feature of a Jacard feature and a bag of words feature; fusing the characteristics of at least two of the characteristics to obtain target fusion characteristics; determining a similarity between the target fusion feature and the features of each determined candidate attribute; and selecting a preset number of candidate attributes or a preset proportion of candidate attributes from the determined candidate attributes as at least two candidate attributes according to the sequence of similarity from large to small.
In some embodiments, the pre-trained ranking model may be trained by: acquiring a sample set, wherein the sample set comprises a positive sample and a negative sample, the positive sample comprises a positive sample word combination and an attribute sample, the negative sample comprises a negative sample word combination and an attribute sample, and the similarity between the characteristics of the positive sample word combination and the characteristics of the attribute sample is greater than the similarity between the characteristics of the negative sample word combination and the characteristics of the attribute sample; inputting a sample sequence consisting of a plurality of samples in a sample set into a sequencing model to be trained, and predicting a sequencing result of similarity among features in the samples of the sample sequence; and training a sequencing model to be trained based on the predicted sequencing result to obtain a pre-trained sequencing model.
In some embodiments, obtaining a sample set comprises: taking a word combination which belongs to a knowledge type and corresponds to a target attribute in a preset structured data set as a positive sample word combination, wherein the target attribute is a candidate attribute corresponding to an attribute in the word combination to be processed; and taking the word combinations which belong to the knowledge type and do not correspond to the target attribute in the preset structured data set as negative sample word combinations.
In a second aspect, an embodiment of the present application provides an information processing apparatus, including: the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is configured to acquire a word combination to be processed, and the word combination to be processed comprises an entity and the attribute of the entity; the candidate determining unit is configured to determine a knowledge type corresponding to a word combination to be processed in a preset structured data set, and determine attributes belonging to the knowledge type as candidate attributes, wherein the candidate attributes comprise at least two; and the attribute determining unit is configured to determine candidate attributes corresponding to the attributes in the word combinations to be processed based on the similarity between the at least two candidate attributes and the word combinations to be processed.
In some embodiments, the word combinations to be processed further include attribute values associated with the attributes; the candidate determining unit is configured to determine the knowledge type corresponding to the word combination to be processed in the preset structured data set according to the following mode: and determining the knowledge type of the concept of the entity and the knowledge type of the concept of the attribute value in the preset structured data set, wherein the knowledge type of the entity and the knowledge type of the attribute value are at least one.
In some embodiments, the candidate determining unit is configured to determine the knowledge type corresponding to the word combination to be processed, in the preset structured data set, as follows: carrying out upper processing on the entity to obtain an upper word of the entity; and determining a knowledge type corresponding to the superior word of the entity in a preset structured data set, and taking the knowledge type as a knowledge type corresponding to the word combination to be processed.
In some embodiments, the word combinations to be processed further include attribute values associated with the attributes; the device still includes: the upper unit is configured to carry out upper processing on the attribute value to obtain an upper word of the attribute value; and a candidate determination unit configured to perform determination of a knowledge type corresponding to a hypernym of the entity, and to take the knowledge type as a knowledge type corresponding to a word combination to be processed, as follows: determining a knowledge type corresponding to a hypernym of the entity, and determining a knowledge type corresponding to a hypernym of the attribute value; and taking the knowledge type corresponding to the hypernym of the entity and the knowledge type corresponding to the hypernym of the attribute value as the knowledge type corresponding to the word combination to be processed.
In some embodiments, the apparatus further comprises: a feature determination unit configured to determine, for each of at least two of entities, attributes, and attribute values in the word combination to be processed, a feature of at least two of the entities, the attributes, and the attribute values in the word combination to be processed before determining a candidate attribute corresponding to an attribute in the word combination to be processed based on a similarity between the at least two candidate attributes and the word combination to be processed; a fusion unit configured to fuse features of the at least two words, and take a fusion result as a feature of the word combination to be processed; and the attribute determining unit is further configured to perform the following steps of determining candidate attributes corresponding to attributes in the word combination to be processed based on the similarity between at least two candidate attributes and the word combination to be processed: ordering the similarity between the features of the word combination to be processed and the features of at least two candidate attributes; and taking the candidate attribute corresponding to the highest similarity in the obtained similarity sequence as the candidate attribute corresponding to the attribute in the word combination to be processed.
In some embodiments, the attribute determining unit is further configured to perform the ranking of the similarity between the features of the word combination to be processed and the features of at least two of the candidate attributes as follows: inputting the features of the word combination to be processed and the features of the at least two candidate attributes into a pre-trained ranking model, and ranking the similarity between the features of the word combination to be processed and the features of the at least two candidate attributes through the pre-trained ranking model.
In some embodiments, the apparatus further comprises: a first determination unit configured to determine, for each of at least one of an entity, an attribute, and an attribute value in the combination of words to be processed, a feature of the same, wherein the feature of each of the at least one includes a fusion feature of a Jacard feature and a bag of words feature; a target determination unit configured to fuse features of the at least two to obtain a target fusion feature; a similarity determination unit configured to determine a similarity between the target fusion feature and a feature of each of the determined candidate attributes; and the selecting unit is configured to select a preset number or a preset proportion of candidate attributes from the determined candidate attributes as the at least two candidate attributes according to the sequence of similarity from large to small.
In some embodiments, the pre-trained ranking model may be trained by: acquiring a sample set, wherein the sample set comprises a positive sample and a negative sample, the positive sample comprises a positive sample word combination and an attribute sample, the negative sample comprises a negative sample word combination and an attribute sample, and the similarity between the characteristics of the positive sample word combination and the characteristics of the attribute sample is greater than the similarity between the characteristics of the negative sample word combination and the characteristics of the attribute sample; inputting a sample sequence consisting of a plurality of samples in the sample set into a sequencing model to be trained, and predicting a sequencing result of similarity among features in the samples of the sample sequence; and training the sequencing model to be trained based on the predicted sequencing result to obtain the pre-trained sequencing model.
In some embodiments, the obtaining a sample set comprises: taking a word combination which belongs to the knowledge type and corresponds to a target attribute in the preset structured data set as the positive sample word combination, wherein the target attribute is a candidate attribute corresponding to an attribute in the word combination to be processed; and taking the word combination which belongs to the knowledge type and does not correspond to the target attribute in the preset structured data set as the negative sample word combination.
In a third aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a storage device for storing one or more programs which, when executed by one or more processors, cause the one or more processors to implement a method as in any embodiment of the information processing method.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements a method as in any one of the embodiments of the information processing method.
According to the information processing scheme provided by the embodiment of the application, a word combination to be processed is obtained, wherein the word combination to be processed comprises an entity and the attribute of the entity; determining a knowledge type corresponding to the word combination to be processed in a preset structured data set, and determining attributes belonging to the knowledge type as candidate attributes, wherein the candidate attributes comprise at least two; and determining candidate attributes corresponding to the attributes in the word combination to be processed based on the similarity between at least two candidate attributes and the word combination to be processed. According to the embodiment of the application, the candidate attribute corresponding to the attribute in the word combination can be quickly and accurately determined in the preset structured data set, so that the unfamiliar word combination can be automatically associated to the structured data, the labor consumption is avoided, and the association efficiency and accuracy are improved.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram to which some embodiments of the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of an information processing method according to the present application;
FIG. 3 is a schematic diagram of an application scenario of an information processing method according to the present application;
FIG. 4 is a flow diagram of yet another embodiment of an information processing method according to the present application;
FIG. 5 is a schematic block diagram of one embodiment of an information processing apparatus according to the present application;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing an electronic device according to some embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows an exemplary system architecture 100 to which embodiments of the information processing method or information processing apparatus of the present application may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various communication client applications, such as video applications, live applications, instant messaging tools, mailbox clients, social platform software, and the like, may be installed on the terminal devices 101, 102, and 103.
Here, the terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they 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 terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., multiple pieces of software or software modules to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 105 may be a server providing various services, such as a background server providing support for the terminal devices 101, 102, 103. The background server may analyze and perform other processing on the received data such as the word combinations, and feed back a processing result (for example, candidate attributes corresponding to attributes in the word combinations) to the terminal device.
It should be noted that the information processing method provided in the embodiment of the present application may be executed by the server 105 or the terminal devices 101, 102, and 103, and accordingly, the information processing apparatus may be provided in the server 105 or the terminal devices 101, 102, and 103.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow 200 of one embodiment of an information processing method according to the present application is shown. The information processing method comprises the following steps:
In the present embodiment, an execution subject of the information processing method (e.g., a server or a terminal device shown in fig. 1) may acquire a word combination to be processed. The word combinations herein may comprise at least two words, e.g. may comprise entities, e.g. an entity may be a name of a person. Furthermore, the word combinations may also include attributes of the entities.
In this embodiment, the executing entity may determine a knowledge type (type) corresponding to the word combination to be processed in a preset structured data set. The knowledge type is a generic concept, and for example, can be a generic concept of an entity. The execution main body can determine the knowledge type corresponding to the word combination in various modes. For example, the execution body may obtain an ID (Identity) of an entity word in the word combination, and determine a knowledge type to which the ID belongs. In addition, the execution subject may determine the hypernym of the entity, determine a knowledge type synonymous with the hypernym, and use the knowledge type as a knowledge type corresponding to the word combination to be processed. In a preset structured data set, there are several attributes that belong to each knowledge type under that knowledge type.
The preset structured data set is a data set conforming to a preset constraint (schema), for example, the preset constraint may be that a word combination formed by connecting three words is constrained, and the first word and the last word are names of people. For example, the word combination is Zhang San-wife-Liu Ying.
For example, the entity is a person name, and the knowledge type includes "singer", "person", "thing". Attributes belonging to the "people" knowledge type, i.e., attributes under the knowledge type include "name", "wife", "blood type", "age", "gender". Attributes belonging to the "things" knowledge type include "name", "attributes", and the like.
In this embodiment, the execution main body may determine, based on the word combination to be processed and the similarity between each of the at least two candidate attributes, a candidate attribute corresponding to an attribute in the word combination. In practice, the execution subject may determine the candidate attribute corresponding to the attribute in the word combination in various ways. For example, the execution body may rank the similarity between each candidate attribute and the word combination to be processed, and use the candidate attribute corresponding to the highest similarity as the candidate attribute corresponding to the attribute in the word combination to be processed.
Specifically, the similarity between the candidate attribute and the word combination to be processed may be a similarity between a feature of the candidate attribute and a feature of the word combination to be processed. The feature of the word combination herein may include a feature of an attribute in the word combination, and may also include a feature of an attribute value in the word combination, a feature of an entity, and the like.
The candidate attribute corresponding to the attribute determined by the execution subject establishes a mapping relationship between the attribute and the candidate attribute. In this way, the execution subject may use the association relationship between the candidate attribute and other words in the preset structured data set as the association relationship of the attribute, and may use the knowledge type to which the candidate attribute belongs as the knowledge type of the attribute.
Optionally, after step 203, the executing agent may compare the similarity of the candidate attribute corresponding to the attribute in the word combination to be processed with a preset similarity threshold. If the similarity is greater than or equal to the similarity threshold, the candidate attributes corresponding to the attributes in the word combination to be processed can be associated to a preset structured data set, and can also be associated to a knowledge graph. If the similarity is smaller than the similarity threshold, the candidate attribute is abandoned.
The method provided by the embodiment of the application can quickly and accurately determine the candidate attribute corresponding to the attribute in the word combination in the preset structured data set, thereby being beneficial to realizing automatic association of strange word combinations to structured data, avoiding the consumption of manpower and improving the efficiency and accuracy of association.
In some optional implementation manners of this embodiment, the word combination to be processed further includes an attribute value associated with the attribute; step 202 may include: and determining the knowledge type of the concept of the entity and the knowledge type of the concept of the attribute value in the preset structured data set, wherein the knowledge type of the entity and the knowledge type of the attribute value are at least one.
In these alternative implementations, the executing agent may determine the knowledge type directly from the preset structured data set. Specifically, the word combination may be an SPO triple, i.e., an entity, an attribute, and an attribute value (Subject-Predicate-Object). In the preset structured data set, at least one level of knowledge type exists. For example, three levels of knowledge types may include: "singer", "character", "thing". The execution body can determine the concept, namely paraphrase, of the entity and determine the knowledge type of the concept in the preset structured data set. In addition, the execution body can also determine the concept of the attribute value and determine the knowledge type of the concept. Specifically, in the preset structured data set, there are a plurality of concepts belonging to each knowledge type, where the concepts may be concepts of entities, concepts of attributes, concepts of attribute values, and the like.
The execution subject may use the knowledge types determined for both the entity and the attribute value as the knowledge types corresponding to the word combinations to be processed.
These implementations can accurately determine the knowledge type directly from a preset structured set of values.
In some optional implementations of this embodiment, step 202 may include: carrying out upper processing on the entity to obtain an upper word of the entity; and determining a knowledge type corresponding to the superior word of the entity in a preset structured data set, and taking the knowledge type as a knowledge type corresponding to the word combination to be processed.
In these optional implementations, the execution subject may perform upper-level processing on the entity, so as to obtain an upper-level word of the entity. Then, the executing body may determine the knowledge type corresponding to the hypernym, and use the knowledge type as the knowledge type corresponding to the word combination to be processed. Specifically, the upper processing of the original word means upper generalization, and an upper word having a larger range and more generalization than the original word is obtained.
The execution subject may determine the knowledge type corresponding to the hypernym of the entity in various ways. For example, the execution subject may only use a knowledge type completely consistent with the hypernym among the knowledge types of the preset structured data set as the knowledge type corresponding to the hypernym of the entity. For another example, the execution subject may further use a knowledge type having a similarity of sense to the hypernym greater than a preset value, for example, 95%, as the knowledge type corresponding to the hypernym of the entity. In addition, the executing body may first determine a knowledge type (first one) completely consistent with the hypernym, determine a knowledge type (second one) with semantic similarity greater than a preset threshold, and use both the knowledge type (first one and second one) as the knowledge type corresponding to the hypernym of the entity.
The realization modes can determine the knowledge type more comprehensively through the hypernym, and the recall rate of determining the knowledge type is improved.
In some optional application scenarios of these implementations, the word combinations to be processed further include attribute values associated with the attributes; the above method may further comprise: carrying out upper processing on the attribute value to obtain an upper word of the attribute value; and, determining a knowledge type corresponding to the hypernym of the entity in the implementations, and taking the knowledge type as a knowledge type corresponding to the word combination to be processed, may include: determining a knowledge type corresponding to a hypernym of the entity, and determining a knowledge type corresponding to a hypernym of the attribute value; and taking the knowledge type corresponding to the hypernym of the entity and the knowledge type corresponding to the hypernym of the attribute value as the knowledge type corresponding to the word combination to be processed.
In these application scenarios, the execution body may perform upper-level processing not only on the entity, but also on the attribute value to obtain an upper-level word of the attribute value. And determining the knowledge type corresponding to the hypernym of the attribute value by adopting a mode of determining the knowledge type corresponding to the hypernym of the entity. Then, the execution body may use the knowledge type corresponding to the hypernym of the entity and the knowledge type corresponding to the hypernym of the attribute value as the knowledge type corresponding to the word combination to be processed.
These application scenarios may determine hypernyms for attribute values, thereby further expanding the recall rate for determining knowledge types by using attribute values based on the utilization of entities.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the information processing method according to the present embodiment. In the application scenario of fig. 3, the execution subject 301 may obtain a word combination "zhang-wife" 302 to be processed, where the word combination to be processed includes an entity "zhang", and an attribute "wife" of the entity. The execution subject 301 determines a knowledge type "singer" and "person" 303 corresponding to a word combination to be processed in a preset structured data set, and determines attributes belonging to the knowledge type as candidate attributes 304, wherein the candidate attributes include 25. The execution main body 301 determines a candidate attribute "wife" 305 corresponding to an attribute "wife" in the word combination to be processed, based on the similarity between the candidate attribute and the word combination to be processed.
With further reference to FIG. 4, a flow 400 of yet another embodiment of an information processing method is shown. The flow 400 of the information processing method includes the following steps:
In the present embodiment, an execution subject of the information processing method (e.g., a server or a terminal device shown in fig. 1) may acquire a word combination to be processed. The word combinations herein may comprise a plurality of words, e.g. may comprise entities, e.g. an entity may be a name of a person. Furthermore, the word combinations may also include attributes of the entities.
In this embodiment, the executing entity may determine a knowledge type corresponding to a word combination to be processed in a preset structured data set. The execution main body can determine the knowledge type corresponding to the word combination in various modes and determine at least two candidate attributes.
In step 403, for each of at least two of entities, attributes and attribute values in the word combination to be processed, determining a feature of the entity, the attribute and the attribute value, wherein the feature of each of the at least two includes at least two.
In this embodiment, the execution body may determine characteristics of a plurality of word combinations. The features here may be features with common substrings removed. In particular, the features may include at least one of: the method comprises the following steps of obtaining a Jaccard (jaccard) characteristic, a bag of words (Bow) characteristic, a characteristic obtained by performing word embedding (embedding) processing on a Generalized Regression Neural Network (GRNN) model, and a characteristic obtained by performing word embedding (embedding) on a skip word (skip gram) model.
In practice, the characteristics of an entity, attribute, or attribute value may be at least one of: the characteristics obtained by general word segmentation, the characteristics obtained after word segmentation is refined (the granularity of the word segmentation is smaller than that of the general word segmentation), and the characteristics of hypernyms. In addition, the characteristics of the word combination may also include co-occurrence characteristics between the entity and the attribute value, that is, the entity and the attribute value in the word combination, and the jaccard characteristics of the entity and the attribute value in the determined knowledge type.
And step 404, fusing the characteristics of at least two of the words, and taking the fusion result as the characteristics of the word combination to be processed.
In this embodiment, the execution subject may determine features of a plurality of word combinations and merge the features. In particular, the features of the words are vectors, and thus, the fusion between features may refer to the concatenation of vectors.
In practice, a variety of features may be determined for each, such as the Jacard feature and the bag-of-words feature. The execution agent may perform weighted averaging of the respective features of any one of the features by using weights preset for the respective features, and thereby take the result of the weighted averaging as the feature of the one of the features.
In this embodiment, the execution subject may determine similarities between features of the word combinations to be processed and features of the candidate attributes, and rank the similarities to obtain a similarity sequence.
In some optional implementations of this embodiment, step 405 may include: inputting the characteristics of the word combination to be processed and the characteristics of the at least two candidate attributes into a pre-trained sequencing model, and sequencing the similarity between the characteristics of the word combination to be processed and the characteristics of the at least two candidate attributes through the pre-trained sequencing model.
In these optional implementation manners, the execution subject may implement outputting the respective similarity degrees after the ranking through a pre-trained ranking model. The ranking model here may be an LTR (LTR) model. In particular, the execution agent may input a sequence to the order model. Each element in the sequence includes a feature of the word combination to be processed and a feature of a candidate attribute. The features of the candidate attributes comprised by the different elements are different. The execution body may determine similarity of the feature of the word combination in each element and the feature of the candidate attribute using an ordering model. And then, the execution body uses a sequencing model to sequence the similarity and acquire a similarity sequence.
These implementations may utilize a ranking model to accurately rank the similarities.
In some optional application scenarios of these implementations, the pre-trained ranking model can be trained by: acquiring a sample set, wherein the sample set comprises a positive sample and a negative sample, the positive sample comprises a positive sample word combination and an attribute sample, the negative sample comprises a negative sample word combination and an attribute sample, and the similarity between the characteristics of the positive sample word combination and the characteristics of the attribute sample is greater than the similarity between the characteristics of the negative sample word combination and the characteristics of the attribute sample; inputting a sample sequence consisting of a plurality of samples in a sample set into a sequencing model to be trained, and predicting a sequencing result of similarity among features in the samples of the sample sequence; and training a sequencing model to be trained based on the predicted sequencing result to obtain a pre-trained sequencing model.
In these optional application scenarios, the execution subject may input the samples in the sample set into the ranking model to be trained, thereby predicting the similarity between features in each input sample, and predicting the order between the similarities. The samples input at any one time may include positive samples and/or negative samples. The samples in the sample set may be input in one or more times, each time a sample sequence comprising a plurality of samples may be input.
Specifically, the features in each sample herein refer to features of a positive sample word combination and features of an attribute sample, or features of a negative sample word combination and features of an attribute sample.
In practice, the similarity between the features of the positive sample word combinations and the features of the attribute samples may be greater than a similarity threshold. The similarity between the features of the negative sample word combinations and the features of the attribute samples may be smaller than the similarity threshold, or may be smaller than another similarity threshold. The value of the further similarity threshold here is smaller than the similarity threshold mentioned above.
The application scenarios can train the ranking model by using the positive samples and the negative samples, so that an accurate ranking model is obtained.
Optionally, the acquiring the sample set in the application scenario may include: taking a word combination which belongs to a knowledge type and corresponds to a target attribute in a preset structured data set as a positive sample word combination, wherein the target attribute is a candidate attribute corresponding to an attribute in the word combination to be processed; and taking the word combinations which belong to the knowledge type and do not correspond to the target attribute in the preset structured data set as negative sample word combinations.
In these optional application scenarios, the word combinations that belong to the above knowledge types and correspond to the target attribute, among the word combinations already associated in the preset structured data set, may be used as the positive sample word combinations. In addition, the execution subject may further use a word combination that belongs to the knowledge type and does not correspond to the target attribute as a negative sample word combination. Specifically, a word combination corresponding to each attribute is included in the preset structured data set, and a word combination not corresponding to the attribute also exists.
The optional application scenarios can increase the number of samples by taking word combinations in the preset structured data set as samples, so that the trained ranking model is more accurate.
And step 406, taking the candidate attribute corresponding to the highest similarity in the obtained similarity sequence as the candidate attribute corresponding to the attribute in the word combination to be processed.
In this embodiment, the execution subject may use the candidate attribute with the highest similarity as the candidate attribute corresponding to the attribute in the word combination to be processed.
The embodiment can more accurately represent the word combinations by determining various characteristics of at least two of the word combinations, and improves the accuracy of determining the similarity.
In some optional implementations of this embodiment, the method may further include: for each of at least one of an entity, an attribute, and an attribute value in a word combination to be processed, determining a feature of the one, wherein the feature of each of the at least one includes a fused feature of a Jacard feature and a bag of words feature; fusing the characteristics of at least two of the characteristics to obtain target fusion characteristics; determining a similarity between the target fusion feature and the features of each determined candidate attribute; and selecting a preset number of candidate attributes or a preset proportion of candidate attributes from the determined candidate attributes as at least two candidate attributes according to the sequence of the corresponding similarity from large to small.
In these alternative implementations, the executing entity may perform a screening of the candidate attributes before step 403, so as to screen out the at least two candidate attributes. In particular, the execution agent may determine, for each of at least one of an entity, an attribute value, a plurality of characteristics of the one, the plurality of characteristics including a jaccard characteristic and a bag of words characteristic.
After the execution subjects sort the similarity, a similarity sequence can be obtained. In this way, the execution subject may determine the similarity degrees in a preset number or a preset proportion from the similarity degree sequence in descending order, and use the candidate attributes corresponding to the determined similarity degrees as at least two candidate attributes in the step 402 and the step 405.
The implementation manners can perform initial screening on the candidate attributes to improve the efficiency of determining the candidate attributes corresponding to the word combinations.
With further reference to fig. 5, as an implementation of the method shown in the above figures, the present application provides an embodiment of an information processing apparatus, which corresponds to the embodiment of the method shown in fig. 2, and which may include the same or corresponding features or effects as the embodiment of the method shown in fig. 2, in addition to the features described below. The device can be applied to various electronic equipment.
As shown in fig. 5, the information processing apparatus 500 of the present embodiment includes: an acquisition unit 501, a candidate determination unit 502, and an attribute determination unit 503. The obtaining unit 501 is configured to obtain a word combination to be processed, where the word combination to be processed includes an entity and an attribute of the entity; a candidate determining unit 502 configured to determine a knowledge type corresponding to a word combination to be processed in a preset structured data set, and determine attributes belonging to the knowledge type as candidate attributes, where the candidate attributes include at least two; the attribute determining unit 503 is configured to determine candidate attributes corresponding to attributes in the word combinations to be processed based on the similarity between at least two candidate attributes and the word combinations to be processed.
In some embodiments, the acquisition unit 501 of the information processing apparatus 500 may acquire a word combination to be processed. The word combinations herein may comprise at least two words, e.g. may comprise entities, e.g. an entity may be a name of a person. Furthermore, the word combinations may also include attributes of the entities.
In some embodiments, the candidate determining unit 502 may determine a knowledge type corresponding to the word combination to be processed in a preset structured data set. The knowledge type is a generic concept, and for example, can be a generic concept of an entity. The execution main body can determine the knowledge type corresponding to the word combination in various modes.
In some embodiments, the attribute determining unit 503 may determine, based on the word combination to be processed and the similarity between each of the at least two candidate attributes, a candidate attribute corresponding to an attribute in the word combination. In practice, the execution subject may determine the candidate attribute corresponding to the attribute in the word combination in various ways.
In some optional implementations of this embodiment, the word combination to be processed further includes an attribute value associated with the attribute; the candidate determining unit is configured to determine the knowledge type corresponding to the word combination to be processed in the preset structured data set as follows: and determining the knowledge type of the concept of the entity and the knowledge type of the concept of the attribute value in a preset structured data set, wherein the knowledge type of the entity and the knowledge type of the attribute value are at least one.
In some optional implementations of this embodiment, the candidate determining unit is configured to determine the knowledge type corresponding to the word combination to be processed in the preset structured data set according to the following manner: carrying out upper processing on the entity to obtain an upper word of the entity; and determining a knowledge type corresponding to the superior word of the entity in a preset structured data set, and taking the knowledge type as a knowledge type corresponding to the word combination to be processed.
In some optional implementations of this embodiment, the word combination to be processed further includes an attribute value associated with the attribute; the device further comprises: the upper unit is configured to perform upper processing on the attribute value to obtain an upper word of the attribute value; and the candidate determining unit is configured to execute the determination of the knowledge type corresponding to the superior word of the entity and take the knowledge type as the knowledge type corresponding to the word combination to be processed as follows: determining a knowledge type corresponding to the hypernym of the entity, and determining a knowledge type corresponding to the hypernym of the attribute value; and taking the knowledge type corresponding to the hypernym of the entity and the knowledge type corresponding to the hypernym of the attribute value as the knowledge type corresponding to the word combination to be processed.
In some optional implementations of this embodiment, the apparatus further includes: a feature determination unit configured to determine, for each of at least two of entities, attributes, and attribute values in the word combination to be processed, a feature of at least two of the entities, the attributes, and the attribute values in the word combination to be processed before determining a candidate attribute corresponding to an attribute in the word combination to be processed based on a similarity between the at least two candidate attributes and the word combination to be processed; a fusion unit configured to fuse features of the at least two words, and take a fusion result as a feature of the word combination to be processed; and the attribute determining unit is further configured to perform the following steps of determining candidate attributes corresponding to attributes in the word combination to be processed based on the similarity between at least two candidate attributes and the word combination to be processed: ordering the similarity between the features of the word combination to be processed and the features of at least two candidate attributes; and taking the candidate attribute corresponding to the highest similarity in the obtained similarity sequence as the candidate attribute corresponding to the attribute in the word combination to be processed.
In some optional implementations of this embodiment, the attribute determining unit is further configured to perform the ranking of the similarity between the feature of the word combination to be processed and the features of at least two of the candidate attributes as follows: inputting the features of the word combination to be processed and the features of the at least two candidate attributes into a pre-trained ranking model, and ranking the similarity between the features of the word combination to be processed and the features of the at least two candidate attributes through the pre-trained ranking model.
In some optional implementations of this embodiment, the apparatus further includes: a first determination unit configured to determine, for each of at least one of an entity, an attribute, and an attribute value in the combination of words to be processed, a feature of the same, wherein the feature of each of the at least one includes a fusion feature of a Jacard feature and a bag of words feature; a target determination unit configured to fuse features of the at least two to obtain a target fusion feature; a similarity determination unit configured to determine a similarity between the target fusion feature and a feature of each of the determined candidate attributes; and the selecting unit is configured to select a preset number or a preset proportion of candidate attributes from the determined candidate attributes as the at least two candidate attributes according to the sequence of similarity from large to small.
In some optional implementations of this embodiment, the pre-trained ranking model may be obtained by training through the following steps: acquiring a sample set, wherein the sample set comprises a positive sample and a negative sample, the positive sample comprises a positive sample word combination and an attribute sample, the negative sample comprises a negative sample word combination and an attribute sample, and the similarity between the characteristics of the positive sample word combination and the characteristics of the attribute sample is greater than the similarity between the characteristics of the negative sample word combination and the characteristics of the attribute sample; inputting a sample sequence consisting of a plurality of samples in the sample set into a sequencing model to be trained, and predicting a sequencing result of similarity among features in the samples of the sample sequence; and training the sequencing model to be trained based on the predicted sequencing result to obtain the pre-trained sequencing model.
In some optional implementations of this embodiment, the obtaining the sample set includes: taking a word combination which belongs to the knowledge type and corresponds to a target attribute in the preset structured data set as the positive sample word combination, wherein the target attribute is a candidate attribute corresponding to an attribute in the word combination to be processed; and taking the word combination which belongs to the knowledge type and does not correspond to the target attribute in the preset structured data set as the negative sample word combination.
As shown in fig. 6, electronic device 600 may include a processing means (e.g., central processing unit, graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 600 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. 6 may represent one device or may represent multiple devices as desired.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, 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 such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of embodiments of the present disclosure. It should be noted that the computer readable medium described in the 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 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 embodiments of the present disclosure, however, a computer readable signal medium may comprise 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.
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 application. 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 the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, a candidate determination unit, and an attribute determination unit. Where the names of these units do not in some cases constitute a limitation on the unit itself, for example, the acquiring unit may also be described as a "unit that acquires a word combination to be processed".
As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: acquiring a word combination to be processed, wherein the word combination to be processed comprises an entity and the attribute of the entity; determining a knowledge type corresponding to the word combination to be processed in a preset structured data set, and determining attributes belonging to the knowledge type as candidate attributes, wherein the candidate attributes comprise at least two; and determining candidate attributes corresponding to the attributes in the word combination to be processed based on the similarity between at least two candidate attributes and the word combination to be processed.
The above description is only a preferred embodiment of the application 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 herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (15)
1. An information processing method, the method comprising:
acquiring a word combination to be processed, wherein the word combination to be processed comprises an entity and the attribute of the entity;
determining a knowledge type corresponding to the word combination to be processed in a preset structured data set, and determining attributes belonging to the knowledge type as candidate attributes, wherein the candidate attributes comprise at least two;
and determining candidate attributes corresponding to the attributes in the word combination to be processed based on the similarity between at least two candidate attributes and the word combination to be processed.
2. The method of claim 1, wherein the word combination to be processed further comprises an attribute value associated with the attribute;
determining a knowledge type corresponding to the word combination to be processed in a preset structured data set, wherein the knowledge type comprises the following steps:
and determining the knowledge type of the concept of the entity and the knowledge type of the concept of the attribute value in a preset structured data set, wherein the knowledge type of the entity and the knowledge type of the attribute value are at least one.
3. The method of claim 1, wherein the determining a knowledge type corresponding to the word combination to be processed in a preset structured data set comprises:
carrying out upper processing on the entity to obtain an upper word of the entity;
and determining a knowledge type corresponding to the superior word of the entity in a preset structured data set, and taking the knowledge type as a knowledge type corresponding to the word combination to be processed.
4. The method of claim 3, wherein the word combination to be processed further comprises an attribute value associated with the attribute;
the method further comprises the following steps:
carrying out upper processing on the attribute value to obtain an upper word of the attribute value; and
the determining the knowledge type corresponding to the hypernym of the entity and using the knowledge type as the knowledge type corresponding to the word combination to be processed includes:
determining a knowledge type corresponding to the hypernym of the entity, and determining a knowledge type corresponding to the hypernym of the attribute value;
and taking the knowledge type corresponding to the hypernym of the entity and the knowledge type corresponding to the hypernym of the attribute value as the knowledge type corresponding to the word combination to be processed.
5. The method according to claim 2 or 4, wherein before determining the candidate attribute corresponding to the attribute in the word combination to be processed based on the similarity between at least two candidate attributes and the word combination to be processed, the method further comprises:
for each of at least two of entities, attributes and attribute values in the word combination to be processed, determining the characteristics of the entity, the attributes and the attribute values, wherein the characteristics of each of the at least two comprise at least two;
fusing the characteristics of each of the at least two words, and taking a fusion result as the characteristics of the word combination to be processed; and
the determining, based on the similarity between the at least two candidate attributes and the to-be-processed word combination, a candidate attribute corresponding to an attribute in the to-be-processed word combination includes:
ordering the similarity between the features of the word combination to be processed and the features of at least two candidate attributes;
and taking the candidate attribute corresponding to the highest similarity in the obtained similarity sequence as the candidate attribute corresponding to the attribute in the word combination to be processed.
6. The method of claim 5, wherein said ranking similarity between features of the word combination to be processed and features of at least two of the candidate attributes comprises:
inputting the features of the word combination to be processed and the features of the at least two candidate attributes into a pre-trained ranking model, and ranking the similarity between the features of the word combination to be processed and the features of the at least two candidate attributes through the pre-trained ranking model.
7. The method of claim 5 or 6, wherein the method further comprises:
for each of at least one of an entity, an attribute, and an attribute value in the set of words to be processed, determining a feature of the same, wherein the feature of each of the at least one includes a fused feature of a Jacard feature and a bag of words feature;
fusing the characteristics of at least two of the above-mentioned two to obtain a target fusion characteristic;
determining a similarity between the target fusion feature and features of each determined candidate attribute;
and selecting a preset number or a preset proportion of candidate attributes from the determined candidate attributes as the at least two candidate attributes according to the sequence of similarity from large to small.
8. The method of claim 6, wherein the pre-trained ranking model is trained by:
acquiring a sample set, wherein the sample set comprises a positive sample and a negative sample, the positive sample comprises a positive sample word combination and an attribute sample, the negative sample comprises a negative sample word combination and an attribute sample, and the similarity between the characteristics of the positive sample word combination and the characteristics of the attribute sample is greater than the similarity between the characteristics of the negative sample word combination and the characteristics of the attribute sample;
inputting a sample sequence consisting of a plurality of samples in the sample set into a sequencing model to be trained, and predicting a sequencing result of similarity among features in the samples of the sample sequence;
and training the sequencing model to be trained based on the predicted sequencing result to obtain the pre-trained sequencing model.
9. The method of claim 8, wherein the obtaining a sample set comprises:
taking a word combination which belongs to the knowledge type and corresponds to a target attribute in the preset structured data set as the positive sample word combination, wherein the target attribute is a candidate attribute corresponding to an attribute in the word combination to be processed;
and taking the word combination which belongs to the knowledge type and does not correspond to the target attribute in the preset structured data set as the negative sample word combination.
10. An information processing apparatus, the apparatus comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is configured to acquire a word combination to be processed, and the word combination to be processed comprises an entity and the attribute of the entity;
the candidate determining unit is configured to determine a knowledge type corresponding to the word combination to be processed in a preset structured data set, and determine attributes belonging to the knowledge type as candidate attributes, wherein the candidate attributes comprise at least two;
and the attribute determining unit is configured to determine candidate attributes corresponding to the attributes in the word combination to be processed based on the similarity between at least two candidate attributes and the word combination to be processed.
11. The apparatus of claim 10, wherein the to-be-processed word combination further comprises an attribute value associated with the attribute;
the candidate determining unit is configured to determine the knowledge type corresponding to the word combination to be processed in the preset structured data set as follows:
and determining the knowledge type of the concept of the entity and the knowledge type of the concept of the attribute value in a preset structured data set, wherein the knowledge type of the entity and the knowledge type of the attribute value are at least one.
12. The apparatus according to claim 10, wherein the candidate determining unit is configured to perform the determining of the knowledge type corresponding to the word combination to be processed in the preset structured data set as follows:
carrying out upper processing on the entity to obtain an upper word of the entity;
and determining a knowledge type corresponding to the superior word of the entity in a preset structured data set, and taking the knowledge type as a knowledge type corresponding to the word combination to be processed.
13. The apparatus of claim 12, wherein the to-be-processed word combination further comprises an attribute value associated with the attribute;
the device further comprises:
the upper unit is configured to perform upper processing on the attribute value to obtain an upper word of the attribute value; and
the candidate determining unit is configured to perform the determining of the knowledge type corresponding to the hypernym of the entity and take the knowledge type as the knowledge type corresponding to the word combination to be processed as follows:
determining a knowledge type corresponding to the hypernym of the entity, and determining a knowledge type corresponding to the hypernym of the attribute value;
and taking the knowledge type corresponding to the hypernym of the entity and the knowledge type corresponding to the hypernym of the attribute value as the knowledge type corresponding to the word combination to be processed.
14. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-9.
15. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1-9.
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