CN111125150B - Search method for industrial field question-answering system - Google Patents
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Abstract
The invention belongs to the technical field of application of question-answering systems, and discloses a search method of a question-answering system in the industrial field. The invention comprises the following steps: acquiring preliminary retrieval information of Chinese natural language; matching the current preliminary retrieval information with a preset FAQ retrieval database to obtain a matching result; judging whether the matching result is a null value, if so, processing the current preliminary search information to obtain Chinese word segmentation, and if not, outputting an answer in the FAQ search database corresponding to the current matching result as a search result; performing entity recognition on the current Chinese word segmentation to obtain a plurality of entity groups; obtaining a plurality of initial abstract templates and abstract retrieval information; the current abstract search information is input into a preset graphic search database to be queried to obtain a query result, and an answer in the graphic database corresponding to the current query result is output as a search result. The invention solves the problems that the prior art needs to manually generate templates and the prior question-answering technology is not suitable for Chinese natural language.
Description
Technical Field
The invention belongs to the technical field of application of question-answering systems, and particularly relates to a search method of a question-answering system in the industrial field.
Background
At present, the prior art extracts answers through combination query language of common question-answer pair (FAQ) similarity calculation and semantic understanding of Chinese natural language, and provides evidence support for information retrieval by carrying out grammar, syntax analysis and keyword extraction on questions of a user to form a ranking query model, so that the forefront accurate answers are given, but the domestic question-answer system is different from foreign technologies in scale or research level.
In the process of implementing the present invention, the inventor finds that at least the following problems exist in the prior art:
1) Because of the specificity of Chinese natural language, foreign mature technology and research theory results can not be well utilized;
2) Lack of an associated basic corpus of Chinese natural language and an available evaluation mechanism;
3) In the prior art, template filling is performed through manual operation, so that the problems of complex logic, easy error and the like exist.
Disclosure of Invention
The present invention aims to solve at least to some extent one of the above technical problems.
To this end, the present invention aims to provide a search method of an industrial field question-answering system, which can enable.
The technical scheme adopted by the invention is as follows:
the industrial field question-answering system searching method comprises the following steps:
acquiring preliminary retrieval information of Chinese natural language;
matching the current preliminary retrieval information with a preset FAQ retrieval database to obtain a matching result;
judging whether the matching result is a null value, if so, processing the current preliminary search information to obtain Chinese word segmentation, and if not, outputting an answer in the FAQ search database corresponding to the current matching result as a search result;
performing entity recognition on the current Chinese word segmentation to obtain a plurality of entity groups;
obtaining a plurality of initial abstract templates according to the current plurality of entity groups, and obtaining abstract retrieval information according to the plurality of initial abstract templates;
the current abstract search information is input into a preset graphic search database to be queried to obtain a query result, and an answer in the graphic database corresponding to the current query result is output as a search result.
Preferably, when matching the current preliminary search information with a preset FAQ search database, the specific steps are as follows:
performing word matching on the current preliminary retrieval information and a preset FAQ retrieval database to obtain a first matching degree;
judging whether the first matching degree is larger than a first matching threshold value, if so, taking the first matching degree and an answer in a corresponding FAQ search database as matching results, and if not, carrying out intention recognition on the current preliminary search information to obtain intention search information;
carrying out intention recognition on the current intention retrieval information and a preset FAQ retrieval database to obtain a second matching degree;
judging whether the second matching degree is larger than a second threshold value, if so, taking the second matching degree and an answer in a corresponding FAQ search database as a matching result, and if not, setting the matching result as a null value.
Preferably, when intention recognition is performed on the current preliminary search information to obtain intention search information, a joint model for recognizing the slot position and the intention by using a semantic analysis tree is adopted as an intention recognition model.
Preferably, when the current Chinese word segmentation is subjected to entity recognition to obtain a plurality of entity groups, the specific steps are as follows:
obtaining at least one entity word according to the current Chinese word;
each entity word is subjected to entity recognition in sequence to obtain a plurality of entity groups;
wherein, each entity word corresponds to one entity group.
Preferably, when a plurality of initial abstract templates are obtained according to the current plurality of entity groups, the current plurality of entity groups are subjected to abstract combination to obtain a plurality of initial abstract templates.
Preferably, when abstract retrieval information is obtained according to a plurality of initial abstract templates, the concrete steps are as follows:
carrying out semantic analysis on each initial abstract template in sequence to obtain a plurality of process abstract templates;
matching each process abstract template with a preset abstract template library to obtain an abstract template;
and performing template filling operation on each abstract template to obtain abstract retrieval information.
Preferably, when the current abstract search information is input into a preset graph search database for query, the specific steps are as follows:
combining the current abstract search information into a special query statement of the graphic search database;
and executing the current professional query statement in the graph retrieval database to obtain a query result.
Preferably, the graphical search database is a NEO4J database.
Preferably, when outputting the answer in the graphic database corresponding to the current query result as the search result, the specific steps are as follows:
packaging answers in a graph database corresponding to the current query result;
returning the packaged answer to the front end for rendering;
and outputting the answer after the rendering is completed as a retrieval result.
The beneficial effects of the invention are as follows:
the invention solves the problems that the prior art needs to manually generate templates and the prior question-answering technology is not suitable for Chinese natural language, realizes the full-new search from data import to answer output based on search information, does not need manual intervention to automatically generate the templates, reduces tedious logic problems caused by manually generating the templates, reduces error steps, improves the accuracy and reliability of answers for searching knowledge question-answering, supplements a new question-answering system search method, and is suitable for popularization and use.
Other advantageous effects of the present invention will be described in detail in the detailed description.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of embodiment 1.
Detailed Description
The invention will be further elucidated with reference to the drawings and to specific embodiments. The present invention is not limited to these examples, although they are described in order to assist understanding of the present invention. Functional details disclosed herein are merely for describing example embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments of the invention. The terms "comprises," "comprising," "includes," and/or "including," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, and do not preclude the presence or addition of one or more other features, amounts, steps, operations, elements, components, and/or groups thereof.
It should be appreciated that in some alternative embodiments, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may in fact be executed substantially concurrently or the figures may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
It should be understood that specific details are provided in the following description to provide a thorough understanding of the example embodiments. However, it will be understood by those of ordinary skill in the art that the example embodiments may be practiced without these specific details. For example, a system may be shown in block diagrams in order to avoid obscuring the examples with unnecessary detail. In other instances, well-known processes, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the example embodiments.
Example 1:
as shown in fig. 1, the present embodiment provides a search method for an industrial field question-answering system, which includes the following steps:
acquiring preliminary retrieval information of Chinese natural language;
matching the current preliminary retrieval information with a preset FAQ retrieval database to obtain a matching result;
judging whether the matching result is a null value, if so, processing the current preliminary search information to obtain Chinese word segmentation, and if not, outputting an answer in the FAQ search database corresponding to the current matching result as a search result;
performing entity recognition on the current Chinese word segmentation to obtain a plurality of entity groups;
obtaining a plurality of initial abstract templates according to the current plurality of entity groups, and obtaining abstract retrieval information according to the plurality of initial abstract templates;
the current abstract search information is input into a preset graphic search database to be queried to obtain a query result, and an answer in the graphic database corresponding to the current query result is output as a search result.
Example 2
The technical scheme provided by the embodiment is further improved based on the technical scheme of embodiment 1, and the distinguishing technical characteristics of the embodiment and embodiment 1 are as follows:
in this embodiment, when matching the current preliminary search information with a preset FAQ search database, the specific steps are as follows:
performing word matching on the current preliminary retrieval information and a preset FAQ retrieval database to obtain a first matching degree;
judging whether the first matching degree is larger than a first matching threshold value, if so, taking the first matching degree and an answer in a corresponding FAQ search database as matching results, and if not, carrying out intention recognition on the current preliminary search information to obtain intention search information;
carrying out intention recognition on the current intention retrieval information and a preset FAQ retrieval database to obtain a second matching degree;
judging whether the second matching degree is larger than a second threshold value, if so, taking the second matching degree and an answer in a corresponding FAQ search database as a matching result, and if not, setting the matching result as a null value.
In actual use, the dynamic semantic slot can be customized according to service requirements, a flow logic is given by an expert in the field according to a multi-round service model, then the sequence and naming of each slot are filled according to the characteristics of the flow logic, and a clear speaking operation is set, so that a complementary technical scheme is formed for multi-round dialogue realization. The groove is information required to be completed for converting the primary user intention into an explicit user instruction in the multi-round dialogue process; a slot corresponds to a piece of information that needs to be acquired in the processing of a thing; the dynamic configurable multi-round semantic slot performs slot position self-definition according to service requirements, meets the requirement of performing multi-round data setting according to service scenes, and adds a new data access mode for multi-round conversations; the mode has the advantages of customized guidance according to requirements, and improvement in accuracy and service friendliness.
Example 3
The technical scheme provided by the embodiment is further improved based on the technical scheme of embodiment 2, and the distinguishing technical characteristics of the embodiment and embodiment 2 are as follows:
in this embodiment, when intention recognition is performed on the preliminary search information to obtain the intention search information, the intention recognition model is used to construct a joint model (recnn+viterbi) for recognizing the slot and the intention by using the semantic analysis tree.
It should be noted that, the recnn+viterbi model is input as a single word vector, and each part of speech is regarded as a weight vector (weight vector), so that the operation of each word in its path is a dot product operation of a simple word vector and a part of speech weight vector; when a parent node has multiple child branches, it can be seen that each branch sums with a dot product of weights. IN this embodiment, the intent recognition directly uses the output vector of the root node to make a classification, the slot recognition introduces the feature of the path vector, such as "IN", the path IN the semantic analysis tree is "IN-PP-NP", and each output vector of the path is subjected to a weighted operation to obtain the feature of the path, where the conca of the path features of three words is used as the tri-path feature to make the classification of the slot, so as to make a prediction on "IN", and the specific formula is as follows:
example 4
The technical scheme provided by the embodiment is further improved based on the technical scheme of any one of the embodiments 1 to 3, and the distinguishing technical characteristics of the embodiment and any one of the embodiments 1 to 3 are as follows:
in this embodiment, when performing entity recognition on a current chinese word to obtain a plurality of entity groups, the specific steps are as follows:
obtaining at least one entity word according to the current Chinese word;
each entity word is subjected to entity recognition in sequence to obtain a plurality of entity groups;
wherein, each entity word corresponds to one entity group.
Example 5
The technical scheme provided by this embodiment is a further improvement made on the basis of the technical scheme of embodiment 4, and the distinguishing technical features of this embodiment and embodiment 4 are as follows:
in this embodiment, when a plurality of initial abstract templates are obtained according to a current plurality of entity groups, the current plurality of entity groups are abstracted and combined to obtain a plurality of initial abstract templates.
Example 6
The technical scheme provided by this embodiment is a further improvement made on the basis of the technical scheme of embodiment 5, and the distinguishing technical features of this embodiment and embodiment 5 are as follows:
in this embodiment, when abstract search information is obtained according to a plurality of initial abstract templates, the specific steps are as follows:
carrying out semantic analysis on each initial abstract template in sequence to obtain a plurality of process abstract templates;
matching each process abstract template with a preset abstract template library to obtain an abstract template;
and performing template filling operation on each abstract template to obtain abstract retrieval information.
The abstract template is automatically generated, and the abstract template is illustrated by the following example:
generating an abstract template list by a plurality of abstract templates, wherein each abstract template in the abstract template list corresponds to one abstract template data; if the abstract template data is enhp advantage in enhss, the data is an abstract template, wherein 'enhss', 'enhp' are custom parts of speech, and are the total aliases of a noun, such as all marketable companies of a capital, and all companies can replace (ecdc) by a custom aliases, and are key steps of named entity recognition and abstract template semantic computation. The abstract templates are generalized according to the ontology design.
The abstract template data is imported according to the ontology design specification in the process, so that an abstract template generation strategy can be performed according to the scheme, automatic template generation can be performed without manual intervention, complicated logic of manual generation is reduced, error steps are reduced, the defect that the template is required to be manually generated in the prior art can be overcome, and a novel retrieval technical scheme from data import to knowledge output is performed for a template-based method.
Example 7
The technical scheme provided by this embodiment is a further improvement made on the basis of the technical scheme of embodiment 6, and the distinguishing technical features of this embodiment and embodiment 6 are as follows:
in this embodiment, when the current abstract search information is input to a preset graphic search database for query, the specific steps are as follows:
combining the current abstract search information into a special query statement of the graphic search database;
and executing the current professional query statement in the graph retrieval database to obtain a query result.
Example 8
The technical scheme provided by this embodiment is a further improvement made on the basis of the technical scheme of embodiment 7, and the distinguishing technical features of this embodiment and embodiment 7 are as follows:
in this embodiment, the graph retrieval database is a NEO4J database.
Example 9
The technical scheme provided by the embodiment is further improved based on the technical scheme of any one of the embodiments 1 to 8, and the distinguishing technical characteristics of the embodiment and any one of the embodiments 1 to 8 are as follows:
in this embodiment, when outputting the answer in the graphic database corresponding to the current query result as the search result, the specific steps are as follows:
packaging answers in a graph database corresponding to the current query result;
returning the packaged answer to the front end for rendering;
and outputting the answer after the rendering is completed as a retrieval result.
The embodiments described above are merely illustrative and may or may not be physically separate if reference is made to the unit being described as a separate component; if a component is referred to as being a unit, it may or may not be a physical unit, may be located in one place, or may be distributed over multiple network elements. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some of the technical features thereof can be replaced by equivalents. Such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
The invention is not limited to the alternative embodiments described above, but any person may derive other various forms of products in the light of the present invention. The above detailed description should not be construed as limiting the scope of the invention, which is defined in the claims and the description may be used to interpret the claims.
Claims (4)
1. A search method of an industrial field question-answering system is characterized in that: the method comprises the following steps:
acquiring preliminary retrieval information of Chinese natural language;
matching the current preliminary retrieval information with a preset FAQ retrieval database to obtain a matching result;
judging whether the matching result is a null value, if so, processing the current preliminary search information to obtain Chinese word segmentation, and if not, outputting an answer in the FAQ search database corresponding to the current matching result as a search result;
performing entity recognition on the current Chinese word segmentation to obtain a plurality of entity groups;
obtaining a plurality of initial abstract templates according to the current plurality of entity groups, and obtaining abstract retrieval information according to the plurality of initial abstract templates;
inputting the current abstract search information into a preset graphic search database for searching to obtain a search result, and outputting an answer in the graphic database corresponding to the current search result as a search result;
when the current Chinese word segmentation is subjected to entity recognition to obtain a plurality of entity groups, the specific steps are as follows:
obtaining at least one entity word according to the current Chinese word;
each entity word is subjected to entity recognition in sequence to obtain a plurality of entity groups;
wherein, each entity word corresponds to one entity group;
when a plurality of initial abstract templates are obtained according to the current plurality of entity groups, carrying out abstract combination on the current plurality of entity groups to obtain a plurality of initial abstract templates;
when abstract retrieval information is obtained according to a plurality of initial abstract templates, the concrete steps are as follows:
carrying out semantic analysis on each initial abstract template in sequence to obtain a plurality of process abstract templates;
matching each process abstract template with a preset abstract template library to obtain an abstract template;
performing template filling operation on each abstract template to obtain abstract retrieval information;
when the current abstract search information is input into a preset graph search database for query, the specific steps are as follows:
combining the current abstract search information into a special query statement of the graphic search database;
executing the current professional query statement in the graph retrieval database to obtain a query result;
when matching the current preliminary retrieval information with a preset FAQ retrieval database, the specific steps are as follows:
performing word matching on the current preliminary retrieval information and a preset FAQ retrieval database to obtain a first matching degree;
judging whether the first matching degree is larger than a first matching threshold value, if so, taking the first matching degree and an answer in a corresponding FAQ search database as matching results, and if not, carrying out intention recognition on the current preliminary search information to obtain intention search information;
carrying out intention recognition on the current intention retrieval information and a preset FAQ retrieval database to obtain a second matching degree;
judging whether the second matching degree is larger than a second threshold value, if so, taking the second matching degree and an answer in a corresponding FAQ search database as a matching result, and if not, setting the matching result as a null value.
2. The industrial field question-answering system retrieval method according to claim 1, wherein: when intention recognition is carried out on the current preliminary retrieval information to obtain intention retrieval information, the adopted intention recognition model adopts a joint model for recognizing the slot position and the intention by utilizing a semantic analysis tree.
3. The industrial field question-answering system retrieval method according to claim 1, wherein: the graph retrieval database adopts a NEO4J database.
4. The industrial field question-answering system retrieval method according to claim 1, wherein: when the answer in the graphic database corresponding to the current query result is output as the search result, the specific steps are as follows:
packaging answers in a graph database corresponding to the current query result;
returning the packaged answer to the front end for rendering;
and outputting the answer after the rendering is completed as a retrieval result.
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