CN111177316A - Intelligent question and answer method and system based on subject word filtering - Google Patents

Intelligent question and answer method and system based on subject word filtering Download PDF

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CN111177316A
CN111177316A CN201911325753.9A CN201911325753A CN111177316A CN 111177316 A CN111177316 A CN 111177316A CN 201911325753 A CN201911325753 A CN 201911325753A CN 111177316 A CN111177316 A CN 111177316A
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潘建
汤绍雄
祝训醉
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Zhejiang University of Technology ZJUT
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Abstract

An intelligent question-answering method based on subject word filtering comprises the following steps: step 1, obtaining question information q proposed by a user0(ii) a Step 2, loading the subject word library T to obtain an initial word set S0(ii) a Step 3, obtaining a topic word set S1(ii) a Step 4, according to the topic models M and S1Get problem information q0A vectorized representation of w; step 5, reserving the candidate problems with the similarity larger than the threshold value t, and obtaining an initial candidate problem list L by sorting according to the descending order of the similarity0(ii) a Step 6, initializing L1,L2(ii) a Step 7, obtaining L0The first candidate question q; step 8, if q does not exist, go to step 9, otherwise, pair q and q0Matching character strings; step 9, if L1Not null, sort in reverse order and return L1Otherwise, sorting and returning L in reverse order2And then, the process is ended. And an intelligent question-answering system based on subject word filtering is provided. The invention enables the user to answer the questions more accurately.

Description

Intelligent question and answer method and system based on subject word filtering
Technical Field
The invention relates to an intelligent question and answer method and system based on subject word filtering.
Technical Field
Intelligent question answering aims at automatically providing answers to natural language questions posed by users. In recent years, with the mass growth of internet data, the improvement of computing power and the progress of natural language processing technology, intelligent question-answering methods and systems are rapidly developed and widely applied to daily life of people.
However, due to the diversity and openness of the questions, some of the existing intelligent question-answering algorithms have some disadvantages, for example, some community-oriented question-answering algorithms have too low accuracy of results due to large topic span, and some algorithms for some specific topics have no expansibility and cannot be applied to multiple topics at the same time, so that the questions put forward by the user cannot be answered with useful or high quality, and the user needs to spend more time searching for answers. Therefore, how to accurately search out high-quality answers based on the questions posed by the user has strong theoretical and practical values.
Disclosure of Invention
In order to improve the accuracy of intelligent question answering and enable a user to quickly obtain high-quality answers, the invention provides an intelligent question answering method and system based on subject word filtering.
The technical scheme adopted by the invention is as follows:
an intelligent question-answering method based on subject word filtering comprises the following steps:
step 1, obtaining question information q proposed by a user0；
Step 2, loading a subject word bank T and solving the problem information q0Performing word segmentation and stop word removal processing to obtain an initial word set S0；
Step 3, using the topic thesaurus T to S0Filtering to obtain a topic word set S1I.e. S1＝S0∩T；
Step 4, loading the theme model M, and according to M and S1Get problem information q0Vector w ({ S) }11,w1},{S12,w2},…,{S1n,wn}),S1i(i is 1,2, …, n, n is S1Number of middle words) is S1The term of (1), wiIs a word S1iWherein w isiThe value of (A) is;
wi＝M(S1i)；
step 5, calculating w and each question C in the question set C one by onejDegree of similarity p ofj(j is 1,2, …, m is the number of C question-answer pairs), the candidate questions with the similarity larger than the threshold value t are reserved, and the initial candidate question list L is obtained by sorting according to the descending order of the similarity0Wherein w is associated with problem cjVector of (2)
Figure BDA0002328331720000021
Figure BDA0002328331720000022
cjk(k＝1,2,…,
Figure BDA0002328331720000023
Is cjNumber of middle words) is S1The words and phrases in (1) or (b),
Figure BDA0002328331720000024
is a word cjkThe weight of (2) is obtained from a vector model set Cw of the problem;
step 6, initializing L1＝{},L2＝{}；
Step 7, obtaining L0The first candidate question q;
step 8, if q does not exist, go to step 9, otherwise, pair q and q0Carrying out character string matching:
the analysis process comprises the following steps:
(8.1) from question-answer Pair set CpObtaining an answer r of q;
(8.2) if q ═ q0Returning the answer r of q, ending, otherwise going to step 8.3;
(8.3) if
Figure BDA0002328331720000025
L1＝{(q,r)}∪L1Else L2＝{(q,r)}∪L2：
(8.4) from L0Deleting q, and returning to the step 7;
step 9, if L1Not null, sort in reverse order and return L1Otherwise, sorting and returning L in reverse order2And then, the process is ended.
Further, the question-answer library comprises 3 parts:
1. problem set C: only the questions in the question-answer pair set are included, so that data training is facilitated;
2. question and answer set Cp: storing in the form of 'question-answer';
3. vector set C of questionsw: the words are stored in a form of 'word 1, word 2 … -weight 1, weight 2 …', and are obtained by training a question set in a topic model;
the three parts are associated according to the unique index sequence number of the problem, and the data of the corresponding part can be acquired through the sequence number.
Further, in the step 2, the word segmentation adopts an NLPIR word segmentation system and adopts a subject word bank as a user-defined word bank.
In the step 3, the topic word stock is a pre-constructed word stock and is composed of topic keywords and topic-related high-frequency words, and the topic keywords are composed of topic-specific key words, such as the key words of a programming language, and can be obtained from official documents; the high frequency words are automatically extracted from the theme related e-books or documents using the NLPIR keyword extraction tool of the chinese academy of sciences.
In the step 4, the topic model is a model trained in advance according to the topic lexicon, and vectorization representation of the problem information is directly obtained through the topic model; the topic model is obtained by training a question set in a question-answer library and comprises the following steps:
1. loading a question set, performing word segmentation and stop word processing on the question set, and filtering out words which are not in a subject word library to obtain a corresponding initial word set;
2. calculating the weights of the words in the word segmentation result set through a TF _ IDF algorithm;
3. outputting the topic model to a file in a form of 'word weight';
4. outputting the vectorized representation of the problem set to a file in the form of 'word 1 word 2 … -weight 1 weight 2 …' according to the word segmentation result set and the corresponding weight;
in the topic model M, the words are stored according to the key value pairs of the word weight, so when the vectorization representation of the problem information is obtained according to the topic model, the weights of the words can be sequentially and directly obtained through the words.
In said step 5, a set of vectors C of the problemwThe vector representation of all the problems is saved, the similarity calculation can be directly calculated according to the vector, and the threshold t is the best predefined minimum similarity.
Further, in the step 8, the question-answer pair set CpThe questions and the corresponding answers are stored, and the corresponding answers can be obtained through the question indexes.
An intelligent question-answering system based on subject word filtering comprises the following modules:
the problem information acquisition module is used for acquiring problem information of a user;
the question-answer library module is used for storing a subject word library under a subject, a question-answer library and a subject model;
the natural language processing module is used for processing the problem information of the user so as to obtain a word set of the problem information;
the question-answer library matching module is used for matching the question information of the user with the questions in the question set of the question-answer library to obtain a related candidate question list;
the character string matching module is used for processing the candidate question list obtained from the question-answer library matching module and further matching the question information;
and the answer returning module returns the finally obtained answer to the user.
Further, the question answering library module comprises: 1) a topic word library: storing topic keywords and topic-related high-frequency words; 2) a question-answer library: a topic question-answer library is stored; 3) the topic model is as follows: the trained question word sets and vector representations are stored and are stored according to the question-answer pair sequence.
The natural language processing module comprises: word segmentation unit: dividing the question information into word lists, and adding a subject word library as one of bases for word segmentation; a stop word and subject word filtering unit: after word segmentation, stop words and words which do not belong to the subject word bank are filtered.
Furthermore, in the character string matching module, each question-answer pair is obtained from a question-answer library, if a question with the same information as the question exists, the answer of the question is directly returned, otherwise, the answer containing q is searched0Question-answer pair list L1If L is1Not null, sort in reverse order and return L1Otherwise, sorting and returning in reverse order without q0Question-answer pair list L2。
The technical conception of the invention is as follows: the method comprises the steps of obtaining question information, carrying out natural language processing and subject word filtering on the question information, obtaining a candidate question list after matching with a question-answering library, carrying out character string matching, and finally returning a result, so that the intelligent question-answering accuracy is improved.
In the process of asking questions of a user, the algorithm updates the question-answer library at regular time, for example, the period is 1 hour, if questions which are not recorded in the question-answer library appear, the question-answer pairs are recorded in the question-answer library after manual answering, and effective answering information is provided for the user.
The invention has the following beneficial effects: the method comprises the steps of filtering contents irrelevant to the subject in question information based on a specific subject word bank to enable the question information to be more suitable for the subject, and meanwhile, improving the matching degree of the question information and a question-answer bank by adopting a character string matching method to enable the question of a user to be answered more accurately.
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FIG. 1 is a flow chart of the method for implementing intelligent question answering according to the present invention,
figure 2 is a schematic diagram of a system module,
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 and 2, an intelligent question-answering method based on subject word filtering obtains user question information, then performs natural language processing to obtain corresponding word vectors, then performs question-answer library similarity matching and character string matching in sequence based on the obtained word vectors, and returns answers or question-answer pair lists to users after obtaining the answers or question-answer pair lists. The method comprises the following steps:
step 1, obtaining user question information q0(e.g., "what do i want to know the difference between the array and the pointer.
In this embodiment, the theme is set as programming language C/C + +, the segmentation adopts NLPIR segmentation system, and adds a theme thesaurus as a user-defined dictionary, the theme thesaurus includes C/C + +, keywords, operators, and "C language confusion" from the book: the method comprises the steps that high-frequency vocabularies extracted from partial sections in pointer, array, function and multi-file programming are extracted, the inactive vocabulary lists synthesize common inactive vocabulary lists provided by Baidu and Haohang, TF-IDF values are used as word weights during training of a theme model, and a similarity threshold value t is 0.6.
Step 2, loading the subject thesaurus T, and aligning q as shown in table 10Natural language processing including word segmentation and stop word removal to obtain initial word set S0(<Thinking, array, pointer, distinction>)：
Array of elements
Pointer with a movable finger
Character string
Output of
…
auto
break
…
++
…
&&
TABLE 1
Step 3, using the topic thesaurus T to S0Filtering to obtain a topic word set S1(<Array, pointer, distinction>)；
Step 4, loading the theme model M, as shown in Table 2, according to S1Get problem information q0Vector w { "array", 0.2002578}, { "pointer", 0.202271}, { "difference", 0.097653 });
Figure BDA0002328331720000061
Figure BDA0002328331720000071
TABLE 2
Step 5, calculating w and each question C in the question set C one by onejDegree of similarity p ofjThe method comprises the following steps:
when the j is equal to 1, the total weight of the alloy is less than 1,
Figure BDA0002328331720000072
Figure BDA0002328331720000073
Figure BDA0002328331720000074
Figure BDA0002328331720000075
likewise, can obtain
Figure BDA0002328331720000076
As shown in Table 3, the candidate questions greater than the threshold t are retained, and the initial candidate question list L is obtained by sorting the candidate questions in descending order of similarity0As shown in table 4:
Figure BDA0002328331720000077
Figure BDA0002328331720000081
TABLE 3
Figure BDA0002328331720000082
Figure BDA0002328331720000091
TABLE 4
Step 6, initializing L1＝{}，L2＝{}；
Step 7, selecting L0In the first candidate question, when q ═ is "actually say, i want to know what the difference between the array and the pointer is, can tell i? ", q0"what do i want to know the difference between the array and the pointer? ";
step 8, q and q are paired0Matching character strings;
(8.1) from question-answer Pair set CpGet the answer to q, when r ═ array auto allocate space, but … ";
(8.2)q≠q0go to step 8.3;
(8.3)
Figure BDA0002328331720000103
L1{ ("then why can the array and pointer declare as function parameters be interchanged1At this time L1As shown in table 5:
Figure BDA0002328331720000101
TABLE 5
(8.4) from L0Deleting q, and returning to the step 7;
step 7, selecting L0The first candidate problem, when q ═ is then "why can the array and pointer declarations be interchanged as functional parameters? ", q0"what do i want to know the difference between the array and the pointer? ";
step 8, q and q are paired0Matching character strings;
(8.1) from question-answer Pair set CpGet the answer to q, when r ═ array auto allocate space, but … ";
(8.2)q≠q0go to step 8.3;
(8.3)
Figure BDA0002328331720000104
L2{ ("then why can the array and pointer declare as function parameters be interchanged2At this time L2As shown in table 6:
Figure BDA0002328331720000102
TABLE 6
(8.4) from L0Deleting q, and returning to the step 7;
step 7, selecting L0In the first candidate question, when q ═ is "what is a void pointer, can tell me? ", q0"I want to know what the difference between the array and the pointer is？”；
Step 8, q and q are paired0Matching character strings;
(8.1) from question-answer Pair set CpObtaining the answer of q, wherein r is the meaning of ' void ' … ';
(8.2)q≠q0go to step 8.3;
(8.3)
Figure BDA0002328331720000112
L2{ ("what is a pointer2At this time L2As shown in table 7:
Figure BDA0002328331720000111
TABLE 7
(8.4) from L0Deleting m, and returning to the step 7;
step 7, no candidate answer exists;
step 8, proceeding to step 9;
step 9, L1Not empty, and only one record, return L1And then, the process is ended.
In this embodiment, the end question mark is used as a criterion for determining whether the question is question information, the end question mark is not included in the string matching, and the ellipses represent that the text is too long and are hidden and displayed.
It will be appreciated by persons skilled in the art that the foregoing is illustrative only and is not to be construed as limiting the invention, as variations and modifications of the foregoing examples are within the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. An intelligent question-answering method based on topic word filtering is characterized in that: the method comprises the following steps:
step 1, obtaining question information q proposed by a user0；
Step 2, loading the subject word bank T and aiming at the problem informationq0Performing word segmentation and stop word removal processing to obtain an initial word set S0；
Step 3, using the topic thesaurus T to S0Filtering to obtain a topic word set S1I.e. S1＝S0∩T；
Step 4, loading the theme model M, and according to M and S1Get problem information q0Vector w ({ S) }11,w1},{S12,w2},…,{S1n,wn}),S1i(i is 1,2, …, n, n is S1Number of middle words) is S1The term of (1), wiIs a word S1iWherein w isiThe value of (A) is;
wi＝M(S1i)；
step 5, calculating w and each question C in the question set C one by onejDegree of similarity p ofjJ is 1,2, …, m is the number of the C question-answer pairs, the candidate questions with the similarity larger than the threshold value t are reserved, and the initial candidate question list L is obtained by sorting according to the descending order of the similarity0Wherein w is associated with problem cjVector of (2)
Figure FDA0002328331710000011
Figure FDA0002328331710000012
cjk(
Figure FDA0002328331710000013
Figure FDA0002328331710000014
Is cjNumber of middle words) is S1The words and phrases in (1) or (b),
Figure FDA0002328331710000015
is a word cjkThe weight of (2) is obtained from a vector model set Cw of the problem;
step 6, initializing L1＝{},L2＝{}；
Step 7, obtaining L0The first candidate question q;
step 8, if q does not exist, go to step 9, otherwise, pair q and q0Carrying out character string matching:
the analysis process comprises the following steps:
(8.1) from question-answer Pair set CpObtaining an answer r of q;
(8.2) if q ═ q0Returning the answer r of q, ending, otherwise going to step 8.3;
(8.3) if
Figure FDA0002328331710000021
L1＝{(q,r)}∪L1Else L2＝{(q,r)}∪L2：
(8.4) from L0Deleting q, and returning to the step 7;
step 9, if L1Not null, sort in reverse order and return L1Otherwise, sorting and returning L in reverse order2And then, the process is ended.
2. The intelligent question-answering method based on subject word filtering according to claim 1, characterized in that:
the question-answer library comprises 3 parts:
1) problem set C: only the questions in the question-answer pair set are included, so that data training is facilitated;
2) question and answer set Cp: storing in the form of 'question-answer';
3) vector set C of questionsw: the words are stored in a form of 'word 1, word 2 … -weight 1, weight 2 …', and are obtained by training a question set in a topic model;
the three parts are associated according to the unique index sequence number of the problem, and the data of the corresponding part can be acquired through the sequence number.
3. The intelligent question-answering method based on subject word filtering according to claim 1 or 2, characterized in that: in the step 2, the word segmentation adopts an NLPIR word segmentation system and adopts a subject word bank as a user-defined word bank.
4. The intelligent question-answering method based on subject word filtering according to claim 1 or 2, characterized in that: in the step 3, the topic word stock is a pre-constructed word stock and consists of topic keywords and high-frequency words related to topics; the theme keywords are composed of key words of specific themes and can be acquired from official documents; the high frequency words are automatically extracted from the theme related e-books or documents using the NLPIR keyword extraction tool of the chinese academy of sciences.
5. The intelligent question-answering method based on subject word filtering according to claim 3, characterized in that: in step 4, the topic model is a model trained in advance according to the topic lexicon, and vectorization representation of the problem information is directly obtained through the topic model; the topic model is obtained by training a question set in a question-answer library and comprises the following steps:
1) loading a question set, performing word segmentation and stop word removal processing on the question set, and filtering out words outside a subject word bank to obtain a corresponding word segmentation result set;
2) calculating the weights of the words in the word segmentation result set through a TF _ IDF algorithm;
3) outputting the topic model to a file in a form of 'word weight';
4) outputting the vectorized representation of the problem set to a file in the form of 'word 1 word 2 … -weight 1 weight 2 …' according to the word segmentation result set and the corresponding weight;
in the topic model M, the words are stored according to the key value pairs of the word weight, so when the vectorization representation of the problem information is obtained according to the topic model, the weights of the words can be sequentially and directly obtained through the words.
6. The intelligent question-answering method based on subject word filtering according to claim 4, characterized in that: in step 5, the set of vectors C of the problemwStore all the problemsQuantity means, similarity calculation can be directly calculated according to vectors, and the threshold value t is the best predefined minimum similarity.
7. The intelligent question-answering method based on subject word filtering according to claim 3, characterized in that: in step 8, question-answer pair set CpThe question-answer pairs are stored, and the corresponding answers can be obtained through the question indexes.
8. The utility model provides an intelligence question-answering system based on subject word filters which characterized in that: the system comprises:
the problem information acquisition module is used for acquiring problem information of a user;
the question-answer library module is used for storing a subject word library under a subject, a question-answer library and a subject model;
the natural language processing module is used for processing the problem information of the user so as to obtain a word set of the problem information;
the question-answer library matching module is used for matching the question information of the user with the questions in the question set of the question-answer library to obtain a related candidate question list;
the character string matching module is used for processing the candidate question list obtained from the question-answer library matching module and further matching the question information;
and the answer returning module returns the finally obtained answer to the user.
9. The intelligent question-answering system based on subject word filtering according to claim 7, wherein: the question-answering library module comprises:
1) a topic word library: storing topic keywords and topic-related high-frequency words;
2) a question-answer library: a topic question-answer library is stored;
3) the topic model is as follows: the trained question word sets and vector representations are stored and are stored according to the question-answer pair sequence.
10. The intelligent question-answering system based on subject word filtering according to claim 7, wherein: the natural language module includes: word segmentation unit: dividing the question information into word lists, and adding a subject word library as one of bases for word segmentation; a stop word and subject word filtering unit: after word segmentation, stop words and words not belonging to the subject word bank are removed;
in the character string matching module, each question-answer pair is obtained from the question-answer library, if the question with the same information as the question exists, the answer of the question is directly returned, otherwise, the question containing q is searched0Question-answer pair list L1If L is1Not null, sort in reverse order and return L1If L is1If it is null, sorting in reverse order and returning that it does not contain q0Question-answer pair list L2。
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