CN106202036B - A kind of verb Word sense disambiguation method and device based on interdependent constraint and knowledge - Google Patents
A kind of verb Word sense disambiguation method and device based on interdependent constraint and knowledge Download PDFInfo
- Publication number
- CN106202036B CN106202036B CN201610496860.8A CN201610496860A CN106202036B CN 106202036 B CN106202036 B CN 106202036B CN 201610496860 A CN201610496860 A CN 201610496860A CN 106202036 B CN106202036 B CN 106202036B
- Authority
- CN
- China
- Prior art keywords
- word
- interdependent
- meaning
- verb
- ambiguity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/237—Lexical tools
- G06F40/247—Thesauruses; Synonyms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/284—Lexical analysis, e.g. tokenisation or collocates
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Machine Translation (AREA)
Abstract
The invention discloses a kind of verb Word sense disambiguation methods and device based on interdependent constraint and knowledge.Method includes: to carry out interdependent syntactic analysis to large-scale corpus, collects resulting interdependent tuple and counts its frequency, constructs interdependent knowledge base；Interdependent syntactic analysis is carried out to sentence where ambiguity verb, extracts 17 kinds of interdependent tuples for meeting setting condition, the interdependent constraint set as ambiguity verb；According to semantic dictionary, it is each meaning of a word of ambiguity verb, successively extracts synset, antisense word set, upper word set as the meaning of a word of the corresponding meaning of a word and represent word set；Word set is represented according to interdependent knowledge base and the meaning of a word, successively calculates posterior probability of each meaning of a word in interdependent constraint set of ambiguity verb；Select the correct meaning of a word of posterior probability selection ambiguity verb.Using the present invention, the effect of interdependent syntactic analysis, the more acurrate meaning of a word for effectively determining ambiguity verb can be given full play to.
Description
Technical field
The present invention relates to natural language processing technique fields, and in particular to a kind of verb word based on interdependent constraint and knowledge
Adopted disambiguation method and device.
Background technique
Word sense disambiguation refers to according to the context environmental of ambiguity word and determines its meaning of a word automatically.Word sense disambiguation is natural language
The basic task of process field has machine translation, information retrieval, text classification, automatic abstract etc. and directly affects.
The Word sense disambiguation method in knowledge based library is currently the only can be really applied to extensive word sense disambiguation task
Method.Its effect is mainly influenced by three factors: first is that the scale and quality of knowledge base, second is that context-sensitive selected ci poem is selected
Accuracy, third is that meaning of a word relatedness computation method.Existing Knowledge Database method can be divided into automatic building and artificial
Construct two ways.The former obtains knowledge, such as Term co-occurrence, language model by the method for statistical learning automatically from corpus
Deng；This method does not consider the syntax of word, lexical relation, inevitably the interference by the noise word of some short distances.Afterwards
The artificial constructed knowledge base of person；The magnanimity scale of the knowledge needed for word sense disambiguation, it is clear that be difficult to realize.The existing meaning of a word disappears
Discrimination method often uses the method for sliding window when selecting context-sensitive word for ambiguity selected ci poem；This method cannot exclude closely
The noise word of distance, while remote related term can be ignored.The selection method of this sliding window does not account for ambiguity word
The difference of part of speech；Different parts of speech have the characteristics that it is different, for its carry out related term selection when should treat with a certain discrimination；Existing method
Obviously the difference of part of speech is ignored.Existing meaning of a word relatedness computation method often considers the correlation of the meaning of a word just with dictionary
Degree, and have ignored the degree of correlation that the meaning of a word is considered from syntax or semantic relation.These problems existing for existing method restrict
The promotion of word sense disambiguation effect.
The above technical problem present in Word sense disambiguation method in face of existing knowledge based library, the invention patent is for dynamic
The characteristics of word word sense disambiguation, sufficiently excavates the advantage of interdependent syntactic analysis technology, realizes a kind of based on interdependent constraint and knowledge
Verb Word sense disambiguation method and device make every effort to the solution that can push these problems to a certain extent.
Summary of the invention
To solve the shortcomings of the prior art, the invention discloses a kind of verb meaning of a word based on interdependent constraint and knowledge
Disambiguation method and device, more accurately to determine the meaning of a word of ambiguity verb.
For this purpose, the invention provides the following technical scheme:
A kind of verb Word sense disambiguation method based on interdependent constraint and knowledge, comprising the following steps:
Step 1: carrying out interdependent syntactic analysis to large-scale corpus, collecting resulting interdependent tuple and counting its frequency, structure
Build interdependent knowledge base；
Step 2: carrying out interdependent syntactic analysis to sentence where ambiguity verb, therefrom extracts governing word and dependent is
Notional word and dependence are the interdependent tuple of 17 kinds of setting types, the interdependent constraint set as ambiguity verb；
Be each meaning of a word of ambiguity verb Step 3: according to semantic dictionary, successively extract synset, antisense word set, on
Position word set represents word set as the meaning of a word of the corresponding meaning of a word；
Step 4: representing word set according to interdependent knowledge base and the meaning of a word, each meaning of a word of ambiguity verb is successively calculated interdependent
Constrain the posterior probability of set；
Step 5: selecting the maximum meaning of a word of posterior probability as the correct of ambiguity verb according to the calculated result of step 4
The meaning of a word；If multiple meaning of a word obtain equal maximum a posteriori probability simultaneously, therefrom select the highest meaning of a word of word frequency dynamic as ambiguity
The correct meaning of a word of word.
In verb Word sense disambiguation method based on interdependent constraint and knowledge, the interdependent tuple is triple form, including
Dependency relationship type, governing word, dependent may be expressed as: dependency relationship type (governing word, dependent)；Wherein governing word packet
Original shape and part-of-speech information containing governing word, dependent include the original shape and part-of-speech information of dependent.
Further, in the step 1, when constructing interdependent knowledge base, specifically:
Step 1-1) to each document in Large Scale Corpus, successively carry out at interdependent syntactic analysis and lemmatization
Reason collects the interdependent tuple wherein contained, and records the frequency of occurrence of each interdependent tuple；
Step 1-2) summarize the interdependent tuple-set for including in each document and frequency information, obtain interdependent knowledge base.
Further, in the step 2, when extracting the interdependent constraint set of ambiguity verb, specifically:
Step 2-1) interdependent syntactic analysis and lemmatization processing are carried out to the sentence where ambiguity verb, collection wherein relates to
And the interdependent tuple of ambiguity verb；
Step 2-2) the interdependent tuple being collected into is filtered, only retain governing word and dependent be notional word and according to
Deposit the tuple that relationship is following 17 kinds setting types: adjective is supplied (acomp), and adverbial word modifies (advmod), is connected side by side
(conj), direct object (dobj), infinitive modify (infmod), indirect object (iobj), nominal subject (nsubj), quilt
Gerund subject (nsubjpass), participle modification (partmod), preposition modify (prep), and preposition subordinate clause modifies (prepc),
Phrasal verb particle (prt), purpose subordinate clause modify (purpcl), and relative clause modifies (rcmod), and the time modifies (tmod), open
It puts subordinate clause to supply (xcomp), the control subject (xsubj) of open subordinate clause.
Step 2-3) interdependent constraint set by the set of interdependent tuple resulting after filtering, as ambiguity verb.
Further, in the step 3, when the meaning of a word for extracting each meaning of a word represents word set, specifically:
Step 3-1) synset of the current meaning of a word is obtained according to the Synonyms relationship of WordNet；
Step 3-2) the antisense word set of the current meaning of a word is obtained according to the Antonym relationship of WordNet；
Step 3-3) the upper word set of the current meaning of a word is obtained according to the Hypernym relationship of WordNet；
Step 3-4) above-mentioned three classes word set is merged, phrase and ambiguity verb are rejected behind, the word as the current meaning of a word
Justice represents word set.
Further, in the step 4, when calculating posterior probability of the meaning of a word in interdependent constraint set, specifically:
Step 4-1) it successively calculates each meaning of a word and represents posterior probability of the word under each interdependent constraint condition, specifically:
It the meaning of a word is represented into a certain meaning of a word in word set represents word and be denoted asA certain interdependent constraint tuple is denoted as rj' and table
It is shown as: rj(w1,w2)；
If ambiguity verb is the governing word in interdependent constraint tuple, this posterior probability is calculated by formula (1)；
Wherein,Expression dependency relationship type is rj, governing word beDependent is w2Interdependent tuple
Quantity；c(rj,*,w2) expression dependency relationship type be rj, dependent w2Interdependent tuple quantity；M is indicated in semantic dictionary
The sum for the verB forms for including；
If ambiguity verb is the dependent in interdependent constraint tuple, this posterior probability is calculated by formula (2)；
Wherein,Expression dependency relationship type is rj, governing word w1, dependent beInterdependent tuple
Quantity；c(rj,w1, *) expression dependency relationship type be rj, governing word w1Interdependent tuple quantity；M is indicated in semantic dictionary
The sum for the verB forms for including.
Step 4-2) posterior probability of each meaning of a word under the conditions of interdependent constraint set is successively calculated, specifically:
It is assumed that conditional sampling each other between each interdependent constraint tuple, then this posterior probability can be calculated by formula (3)；
Wherein, siIndicate that a certain meaning of a word, R indicate interdependent constraint set,Indicate that the meaning of a word represents word set, rj' indicate a certain
Interdependent constraint tuple,Indicate that a certain meaning of a word represents word.
A kind of verb word sense disambiguation device based on interdependent constraint and knowledge, comprising:
Interdependent construction of knowledge base unit collects resulting interdependent member for carrying out interdependent syntactic analysis to large-scale corpus
Group simultaneously counts its frequency, constructs interdependent knowledge base；
Extraction unit is gathered in the interdependent constraint of ambiguity word, for carrying out interdependent syntactic analysis to sentence where ambiguity verb, from
Middle extraction governing word and dependent are notional word and dependence is the interdependent tuples of 17 kinds of setting types, as ambiguity verb
Interdependent constraint set；
The ambiguity word meaning of a word represents word set extraction unit, for being each meaning of a word of ambiguity verb, successively according to semantic dictionary
Synset, antisense word set, upper word set are extracted as the meaning of a word of the corresponding meaning of a word and represents word set；
It is dynamic successively to calculate ambiguity for representing word set according to interdependent knowledge base and the meaning of a word for meaning of a word posterior probability computing unit
Posterior probability of each meaning of a word of word in interdependent constraint set；
Ambiguity word meaning transference unit selects posteriority general for the output data according to meaning of a word posterior probability computing unit
The correct meaning of a word of the maximum meaning of a word of rate as ambiguity verb；If multiple meaning of a word obtain equal maximum a posteriori probability simultaneously, from
The middle correct meaning of a word for selecting the highest meaning of a word of word frequency as ambiguity verb.
In verb word sense disambiguation device based on interdependent constraint and knowledge, the interdependent tuple is triple form, including
Dependency relationship type, governing word, dependent may be expressed as: dependency relationship type (governing word, dependent)；Wherein governing word packet
Original shape and part-of-speech information containing governing word, dependent include the original shape and part-of-speech information of dependent.
Further, the interdependent construction of knowledge base unit further include:
The interdependent processing unit of single document, for each document in Large Scale Corpus, successively carrying out interdependent syntax point
Analysis and lemmatization processing, collect the interdependent tuple wherein contained, and record the frequency of occurrence of each interdependent tuple；
Interdependent knowledge Merging unit, for summarizing the interdependent tuple-set for including in each document and frequency information, obtain according to
Deposit knowledge base；
Further, extraction unit is gathered in the interdependent constraint of the ambiguity word further include:
The interdependent processing unit of ambiguity sentences, for carrying out interdependent syntactic analysis and lemmatization to the sentence where ambiguity verb
The interdependent tuple for being directed to ambiguity verb is collected in processing；
Interdependent tuple filter element only retains governing word and dependent for being filtered to the interdependent tuple being collected into
Be the tuples that notional word and dependence set type for following 17 kinds: adjective is supplied (acomp), adverbial word modification
(advmod), connection (conj) side by side, direct object (dobj), infinitive modify (infmod), indirect object (iobj), noun
Property subject (nsubj), passive nominal subject (nsubjpass), participle modification (partmod), preposition modify (prep), preposition
Subordinate clause modifies (prepc), phrasal verb particle (prt), and purpose subordinate clause modifies (purpcl), and relative clause modifies (rcmod), when
Between modify (tmod), open subordinate clause is supplied (xcomp), the control subject (xsubj) of open subordinate clause；
Collector unit is gathered in interdependent constraint, for after filter resulting interdependent tuple gather as ambiguity verb according to
Deposit constraint set；
Further, the ambiguity word meaning of a word represents word set extraction unit further include:
It is synonymous to represent word extraction unit, the synonym of the current meaning of a word is obtained for the Synonyms relationship according to WordNet
Collection；
Antisense represents word extraction unit, and the antonym of the current meaning of a word is obtained for the Antonym relationship according to WordNet
Collection；
It is upper to represent word extraction unit, the hypernym of the current meaning of a word is obtained for the Hypernym relationship according to WordNet
Collection；
The meaning of a word represents conflation of words unit, for synset, antisense word set, upper set of words simultaneously, to be rejected phrase and discrimination
For adopted verb behind, the meaning of a word as the current meaning of a word represents word set；
Further, the meaning of a word posterior probability computing unit further include:
The meaning of a word represents word posterior probability computing unit, represents word under specific interdependent constraint condition for calculating the specific meaning of a word
Posterior probability；
Posterior probability computing unit of meaning of a word under the conditions of interdependent constraint set, for calculating the specific meaning of a word in interdependent constraint
Posterior probability under the conditions of set.
Beneficial effects of the present invention:
1, the present invention completes the building of interdependent knowledge base using interdependent syntactic analysis technology, it is contemplated that the sentence between word
Method, semantic relation, constructed interdependent knowledge base have better quality.
2, the characteristics of being directed to verb, the close interdependent tuple of the semantic relation of preferred 17 seed types of the present invention, constructs it
Interdependent constraint set, can reduce the interference of other unrelated tuples, keep the selection of its context related term more accurate.
3, the characteristics of being directed to verb, the preferred synset of the present invention, the word of antisense word set, upper word set as the corresponding meaning of a word
Justice represents word set, can relatively accurately assess the meaning of a word in the appropriateness of context environmental.
4, calculation method of the meaning of a word proposed by the present invention in the posterior probability of interdependent constraint set, it is contemplated that syntax, semanteme
Relationship more fully can accurately assess the matching degree of the meaning of a word and context environmental.
5, the verb Word sense disambiguation method and device proposed by the present invention based on interdependent constraint and knowledge, can be automatically performed
The building of interdependent knowledge base accurately selects interdependent constraint tuple, and calculates the posterior probability of the meaning of a word, and disambiguation with higher is just
True rate improves the word sense disambiguation effect of verb.
Detailed description of the invention
Fig. 1 is the flow chart of the verb Word sense disambiguation method according to embodiment of the present invention based on interdependent constraint and knowledge；
Fig. 2 is the structural representation of the verb word sense disambiguation device according to embodiment of the present invention based on interdependent constraint and knowledge
Figure；
Fig. 3 is the structural schematic diagram according to the interdependent construction of knowledge base unit of embodiment of the present invention；
Fig. 4 is the structural schematic diagram according to the interdependent constraint set extraction unit of embodiment of the present invention ambiguity word；
Fig. 5 is the structural schematic diagram that word set extraction unit is represented according to the embodiment of the present invention ambiguity word meaning of a word；
Fig. 6 is the structural schematic diagram according to embodiment of the present invention meaning of a word posterior probability computing unit.
Specific embodiment:
The scheme of embodiment in order to enable those skilled in the art to better understand the present invention with reference to the accompanying drawing and is implemented
Mode is described in further detail inventive embodiments.
To sentence " The homelessness people we examined had a multitude of
Ambiguity verb examine in physical disorders. " is carried out for disambiguation processing.
According to WordNet 3.0, the word sense information of verb examine is as shown in table 1.
Table 1
Wherein, #v represents part of speech as verb, and #1~#5 represents five different meaning of a word numbers.
The flow chart of verb Word sense disambiguation method of the embodiment of the present invention based on interdependent constraint and knowledge, as shown in Figure 1, packet
Include following steps.
Step 101, interdependent knowledge base is constructed.
Interdependent syntactic analysis is carried out to large-scale corpus, collect resulting interdependent tuple and counts its frequency, is constructed interdependent
Knowledge base, specifically:
Step 1-1) to each document in Large Scale Corpus, successively carry out at interdependent syntactic analysis and lemmatization
Reason collects the interdependent tuple wherein contained, and records the frequency of occurrence of each interdependent tuple；
Step 1-2) summarize the interdependent tuple-set for including in each document and frequency information, obtain interdependent knowledge base.
In the embodiment of the present invention, use Reuter Corpus as corpus, wherein contain Reuter artificially collect it is whole
More than 80 ten thousand news documents of reason；Interdependent syntactic analysis tool is using Stanford Parser provided by Stanford University
Method analyzer using englishPCFG.ser.gz language model, and allows to carry out dependence folding and transmitting processing；It borrows
WordNet 3.0 is helped to carry out lemmatization.
Interdependent syntactic analysis and morphology are carried out to the news documents in Reuter Corpus piece by piece according to step 1-1) first
Reduction treatment is collected shaped like " relation (w1,w2) " interdependent tuple, and record their frequency of occurrence.(the invention patent
Interdependent tuple " relation (w described in specific embodiment1,w2) " in governing word w1With dependent w2It include its original shape
And part-of-speech information).
Then merged according to step 1-2) the interdependent tuple-set for including by each news documents and frequency information, obtained interdependent
Knowledge base.It include altogether different types of interdependent tuple 13417302 in finally obtained interdependent knowledge base, frequency of occurrence is total
Be 93850841.
Step 102, the interdependent constraint set of ambiguity verb is extracted.
Interdependent syntactic analysis is carried out to sentence where ambiguity verb, the interdependent tuple of 17 seed types is therefrom extracted, as discrimination
The interdependent constraint set of adopted verb, specifically:
Step 2-1) interdependent syntactic analysis and lemmatization processing are carried out to the sentence where ambiguity verb, collection wherein relates to
And the interdependent tuple of ambiguity verb.
In the embodiment of the present invention, interdependent syntactic analysis tool is using Stanford Parser provided by Stanford University
Parser using englishPCFG.ser.gz language model, and allows to carry out dependence folding and transmitting processing；
Lemmatization is carried out by WordNet3.0.
To sentence " The homelessness people we examined had a multitude of
After physical disorders. " carries out interdependent syntactic analysis and lemmatization processing, obtained interdependent tuple-set includes such as
Lower tuple: det (people, the), nn (people, homelessness), dobj (examine, people), nsubj
(have,people)、nsubj(examine,we)、rcmod(people,examine)、det(multitude,a)、dobj
(have,multitude)、prep(multitude,disorder)、amod(disorder,physical)。
It is collected from above-mentioned interdependent tuple-set and is related to the tuple of ambiguity verb examine, obtained interdependent tuple set
Closing includes following tuple: dobj (examine, people), nsubj (examine, we), rcmod (people, examine).
Step 2-2) the interdependent tuple being collected into is filtered, only retain governing word and dependent be notional word and according to
Deposit the tuple that relationship is following 17 kinds setting types: adjective is supplied (acomp), and adverbial word modifies (advmod), is connected side by side
(conj), direct object (dobj), infinitive modify (infmod), indirect object (iobj), nominal subject (nsubj), quilt
Gerund subject (nsubjpass), participle modification (partmod), preposition modify (prep), and preposition subordinate clause modifies (prepc),
Phrasal verb particle (prt), purpose subordinate clause modify (purpcl), and relative clause modifies (rcmod), and the time modifies (tmod), open
It puts subordinate clause to supply (xcomp), the control subject (xsubj) of open subordinate clause.
In the embodiment of the present invention, interdependent tuple-set obtained to step 2-1) is filtered, only retain governing word and
Dependent is notional word and dependence is the tuple of 17 kinds of setting types, and filtered interdependent tuple-set includes following member
Group: dobj (examine, people), rcmod (people, examine).
Step 2-3) interdependent constraint set by the set of interdependent tuple resulting after filtering, as ambiguity verb.
Interdependent constraint in the embodiment of the present invention, by the obtained interdependent tuple-set of step 2-2), as ambiguity verb
Set.Interdependent constraint set can be obtained and include following tuple: dobj (examine, people), rcmod (people, examine).
It should be noted that in embodiments of the present invention, governing word and dependent in interdependent tuple include original shape and
Part-of-speech information.For word involved in interdependent constraint set, examine refers to that verb examine, people are word of naming
people。
Step 103, the meaning of a word for extracting ambiguity verb represents word set.
According to semantic dictionary WordNet 3.0, it is each meaning of a word of ambiguity verb, successively extracts synset, antonym
Collection, upper word set represent word set as the meaning of a word of the corresponding meaning of a word, specifically:
Step 3-1) synset of the current meaning of a word is obtained according to the Synonyms relationship of WordNet；
Step 3-2) the antisense word set of the current meaning of a word is obtained according to the Antonym relationship of WordNet；
Step 3-3) the upper word set of the current meaning of a word is obtained according to the Hypernym relationship of WordNet；
Step 3-4) above-mentioned three classes word set is merged, phrase and ambiguity verb are rejected behind, the word as the current meaning of a word
Justice represents word set.
In embodiments of the present invention, for the explanation of the processing of each meaning of a word of ambiguity verb examine, with examine#
For v#1.
For meaning of a word examine#v#1, by step 3-1) can obtain its synset be analyze, analyse, study,
canvass,canvas,examine}；It is empty set that its antisense word set, which can be obtained, by step 3-2)；Its hypernym can be obtained by step 3-3)
Integrate as empty set；By step 3-4), aforementioned three classes word set is merged, and rejects phrase and examine behind, the meaning of a word can be obtained
The meaning of a word of examine#v#1 represents word set as { analyze, analyse, study, canvass, canvas }.
Similarly, for meaning of a word examine#v#2, by step 3-1) to step 3-4), can obtain its meaning of a word represent word set as
{see}。
Similarly, for meaning of a word examine#v#3, by step 3-1) to step 3-4), can obtain its meaning of a word represent word set as
{investigate,probe}。
Similarly, for meaning of a word examine#v#4, by step 3-1) to step 3-4), can obtain its meaning of a word represent word set as
{question,query}。
Similarly, for meaning of a word examine#v#5, by step 3-1) to step 3-4), can obtain its meaning of a word represent word set as
{test,prove,try,essay,evaluate,judge}。
Step 104, each meaning of a word posterior probability of ambiguity verb is calculated.
Word set is represented according to interdependent knowledge base and the meaning of a word, each meaning of a word for successively calculating ambiguity verb is gathered in interdependent constraint
Posterior probability, specifically:
Step 4-1) it successively calculates each meaning of a word and represents posterior probability of the word under each interdependent constraint condition, specifically:
It the meaning of a word is represented into a certain meaning of a word in word set represents word and be denoted asA certain interdependent constraint tuple is denoted as rj' and table
It is shown as: rj(w1,w2)；
If ambiguity verb is the governing word in interdependent constraint tuple, this posterior probability is calculated by formula (1)；
Wherein,Expression dependency relationship type is rj, governing word beDependent is w2Interdependent tuple
Quantity；c(rj,*,w2) expression dependency relationship type be rj, dependent w2Interdependent tuple quantity；M is indicated in semantic dictionary
The sum for the verB forms for including；
If ambiguity verb is the dependent in interdependent constraint tuple, this posterior probability is calculated by formula (2)；
Wherein,Expression dependency relationship type is rj, governing word w1, dependent beInterdependent tuple
Quantity；c(rj,w1, *) expression dependency relationship type be rj, governing word w1Interdependent tuple quantity；M is indicated in semantic dictionary
The sum for the verB forms for including.
Step 4-2) posterior probability of each meaning of a word under the conditions of interdependent constraint set is successively calculated, specifically:
It is assumed that conditional sampling each other between each interdependent constraint tuple, then this posterior probability can be calculated by formula (3)；
Wherein, siIndicate that a certain meaning of a word, R indicate interdependent constraint set,Indicate that the meaning of a word represents word set, rj' indicate it is a certain according to
Constraint tuple is deposited,Indicate that a certain meaning of a word represents word.
In embodiments of the present invention, because in WordNet 3.0 verB forms sum be 11488, therefore in formula (1) and (2)
M value be set as 11488.
By taking meaning of a word examine#v#3 as an example, illustrate step 4-1) to the specific operation process of step 4-3).
It has been obtained by step 102, interdependent constraint set R includes following tuple: dobj (examine, people), rcmod
(people,examine)。
It has been obtained by step 103, meaning of a word examine#v#3 (is denoted as s3) the meaning of a word represent word setFor investigate,
probe}。
It is successively calculated by step 4-1)In each meaning of a word represent word each interdependent constraint condition in interdependent constraint set R
Under posterior probability, process is as follows:
Because of the governing word that examine is interdependent constraint tuple dobj (examine, people), therefore the meaning of a word represents word
Posterior probability of the investigate in the interdependent constraint tuple can be calculated by formula (1)；According to obtained by step 101 statistics
Interdependent knowledge base, can obtain the value of c (dobj, investigate, people) as the value of 24, c (dobj, *, people) is
26854；Therefore it can obtain:
Because of the dependent that examine is interdependent constraint tuple rcmod (people, examine), therefore the meaning of a word represents word
Posterior probability of the investigate in the interdependent constraint tuple can be calculated by formula (2)；According to obtained by step 101 statistics
Interdependent knowledge base, can obtain the value of c (rcmod, people, investigate) as the value of 4, c (rcmod, people, *) is
8930；Therefore it can obtain:
Similarly, it can obtain:
P (probe | dobj, people)=1.3040529967137864E-4
P (probe | rcmod, people)=4.8976393378391615E-5
Posterior probability of meaning of a word examine#v#3 under the conditions of interdependent constraint set is calculated by step 4-2), process is as follows:
Interdependent constraint tuple included in known interdependent constraint set R be respectively as follows: dobj (examine, people),
rcmod(people,examine)；The W of meaning of a word examine#v#3s3For { investigate, probe }.
Word is represented firstly, for each meaning of a word of meaning of a word examine#v#3Calculate separately it
Word investigate represented for the meaning of a word, substitutes into step 4-1) calculated result, can obtain:
Word is represented for other meaning of a word, can similarly be obtained:
Then, according to formula (3), fromIn select a maximum value as P
(s3|R)；P (s can be obtained3| R) value be 1.5966953138331207E-7.
For the other each meaning of a word examine#v#1, examine#v#2, examine#v#4, examine#v#5, respectively
It is denoted as s1、s2、s4、s5；By step 4-1) and step 4-2), it can similarly obtain:
P(s1| R)=4.087540003412789E-8
P(s2| R)=3.544663596709528E-5
P(s4| R)=2.499786183337134E-6
P(s5| R)=2.499786183337134E-6
Step 105, the correct meaning of a word of ambiguity verb is selected according to meaning of a word posterior probability.
According to the calculated result of step 104, select the maximum meaning of a word of posterior probability as the correct meaning of a word of ambiguity verb；If
Multiple meaning of a word obtain equal maximum a posteriori probability simultaneously, then therefrom select the highest meaning of a word of word frequency as the correct of ambiguity verb
The meaning of a word.
By step 104, compare P (s1|R)、P(s2|R)、P(s3|R)、P(s4|R)、P(s5| R) size, it is known that P (s2|R)
Value it is maximum, therefore by meaning of a word s2, i.e. examine#v#2, the correct meaning of a word as ambiguity verb examine.
It should be noted that in step 105, if multiple meaning of a word obtain equal maximum a posteriori probability, basis simultaneously
The word frequency information of WordNet3.0 therefrom selects the highest meaning of a word of word frequency as the correct meaning of a word of ambiguity verb.
By the above operating procedure, the word sense disambiguation work of ambiguity verb examine can be completed.
Correspondingly, the embodiment of the present invention also provides a kind of verb word sense disambiguation device based on interdependent constraint and knowledge,
Structural schematic diagram is as shown in Figure 2.
In this embodiment, described device includes:
Interdependent construction of knowledge base unit 201 is collected resulting interdependent for carrying out interdependent syntactic analysis to large-scale corpus
Tuple simultaneously counts its frequency, constructs interdependent knowledge base；
Extraction unit 202 is gathered in the interdependent constraint of ambiguity word, for carrying out interdependent syntactic analysis to sentence where ambiguity verb,
It therefrom extracts governing word and dependent is notional word and dependence is the interdependent tuple of 17 kinds of setting types, it is dynamic as ambiguity
The interdependent constraint set of word；
The ambiguity word meaning of a word represents word set extraction unit 203, for being each meaning of a word of ambiguity verb according to semantic dictionary,
Synset, antisense word set, upper word set successively, which are extracted, as the meaning of a word of the corresponding meaning of a word represents word set；
Meaning of a word posterior probability computing unit 204 successively calculates ambiguity for representing word set according to interdependent knowledge base and the meaning of a word
Posterior probability of each meaning of a word of verb in interdependent constraint set；
Ambiguity word meaning transference unit 205 selects posteriority for the output data according to meaning of a word posterior probability computing unit
The correct meaning of a word of the meaning of a word of maximum probability as ambiguity verb；If multiple meaning of a word obtain equal maximum a posteriori probability simultaneously,
Therefrom select the highest meaning of a word of word frequency as the correct meaning of a word of ambiguity verb；
It should be noted that in embodiments of the present invention, interdependent tuple described in each Component units is triple in the device
Form, including dependency relationship type, governing word, dependent may be expressed as: dependency relationship type (governing word, dependent)；Wherein
Governing word includes the original shape and part-of-speech information of governing word, and dependent includes the original shape and part-of-speech information of dependent.
The structural schematic diagram of the interdependent construction of knowledge base unit 201 of Fig. 2 shown device as shown in figure 3, comprising:
The interdependent processing unit 301 of single document, for successively carrying out interdependent syntax to each document in Large Scale Corpus
Analysis and lemmatization processing, collect the interdependent tuple wherein contained, and record the frequency of occurrence of each interdependent tuple；
Interdependent knowledge Merging unit 302 is obtained for summarizing the interdependent tuple-set for including in each document and frequency information
Interdependent knowledge base.
The structural schematic diagram of the interdependent constraint set extraction unit 202 of the ambiguity word of Fig. 2 shown device is as shown in figure 4, it is wrapped
It includes:
The interdependent processing unit 401 of ambiguity sentences, for carrying out interdependent syntactic analysis and morphology to the sentence where ambiguity verb
The interdependent tuple for being directed to ambiguity verb is collected in reduction treatment；
Interdependent tuple filter element 402 only retains governing word and subordinate for being filtered to the interdependent tuple being collected into
Word is notional word and dependence is the tuple of following 17 kinds setting types: adjective is supplied (acomp), adverbial word modification
(advmod), connection (conj) side by side, direct object (dobj), infinitive modify (infmod), indirect object (iobj), noun
Property subject (nsubj), passive nominal subject (nsubjpass), participle modification (partmod), preposition modify (prep), preposition
Subordinate clause modifies (prepc), phrasal verb particle (prt), and purpose subordinate clause modifies (purpcl), and relative clause modifies (rcmod), when
Between modify (tmod), open subordinate clause is supplied (xcomp), the control subject (xsubj) of open subordinate clause；
Collector unit 403 is gathered in interdependent constraint, and the set for resulting interdependent tuple after filtering is as ambiguity verb
Interdependent constraint set.
The ambiguity word meaning of a word of Fig. 2 shown device represents the structural schematic diagram of word set extraction unit 203 as shown in figure 5, it is wrapped
It includes:
It is synonymous to represent word extraction unit 501, for obtaining the synonymous of the current meaning of a word according to the Synonyms relationship of WordNet
Word set；
Antisense represents word extraction unit 502, and the antisense of the current meaning of a word is obtained for the Antonym relationship according to WordNet
Word set；
It is upper to represent word extraction unit 503, for obtaining the upper of the current meaning of a word according to the Hypernym relationship of WordNet
Word set；
The meaning of a word represents conflation of words unit 504, for by synset, antisense word set, upper set of words simultaneously, reject phrase and
For ambiguity verb behind, the meaning of a word as the current meaning of a word represents word set.
The structural schematic diagram of the meaning of a word posterior probability computing unit 204 of Fig. 2 shown device as shown in fig. 6, comprising:
The meaning of a word represents word posterior probability computing unit 601, represents word in specific interdependent constraint item for calculating the specific meaning of a word
Posterior probability under part；
Posterior probability computing unit 602 of meaning of a word under the conditions of interdependent constraint set, for calculating the specific meaning of a word interdependent
Posterior probability under the conditions of constraint set.
Fig. 2~verb word sense disambiguation device shown in fig. 6 based on interdependent constraint and knowledge can be integrated into various hard
In part entity.For example, the verb word sense disambiguation device based on interdependent constraint and knowledge can be integrated into: PC, plate
Among the equipment such as computer, smart phone, work station.
Can by instruction or instruction set storage storing mode by embodiment of the present invention proposed based on it is interdependent about
Beam and the verb Word sense disambiguation method of knowledge are stored on various storage mediums.These storage mediums include but is not limited to: soft
Disk, CD, hard disk, memory, USB flash disk, CF card, SM card etc..
In conclusion in embodiments of the present invention, interdependent syntactic analysis is carried out to large-scale corpus, collect it is resulting according to
It deposits tuple and counts its frequency, construct interdependent knowledge base；Interdependent syntactic analysis is carried out to sentence where ambiguity verb, is therefrom extracted
Governing word and dependent are notional word and dependence is the interdependent tuple of 17 kinds of setting types, as the interdependent of ambiguity verb
Constraint set；According to semantic dictionary, it is each meaning of a word of ambiguity verb, successively extracts synset, antisense word set, upper word set
The meaning of a word as the corresponding meaning of a word represents word set；Word set is represented according to interdependent knowledge base and the meaning of a word, successively calculates each of ambiguity verb
Posterior probability of a meaning of a word in interdependent constraint set；Select the maximum meaning of a word of posterior probability as the correct meaning of a word of ambiguity verb
If (multiple meaning of a word obtain equal maximum a posteriori probability simultaneously, therefrom select the highest meaning of a word of word frequency as ambiguity verb
The correct meaning of a word).It can be seen that realizing the verb meaning of a word based on interdependent constraint and knowledge after using embodiment of the present invention
It disambiguates.Embodiment of the present invention can use interdependent syntactic analysis technology and complete the building of interdependent knowledge base, to improve knowledge
The quality in library；It is preferred that the interdependent tuple of 17 seed types to exclude the interference of unrelated tuple makes the choosing of its context related term
It selects more accurate；It is preferred that the meaning of a word of 3 seed types represents word set, so that relatively accurately the assessment meaning of a word is in the suitable of context environmental
Conjunction degree；The meaning of a word is proposed in the calculation method of the posterior probability of interdependent constraint set, it is contemplated that syntax, semantic relation, thus
More fully accurately assess the matching degree of the meaning of a word and context environmental.Embodiment of the present invention realized based on it is interdependent about
The verb Word sense disambiguation method and device of beam and knowledge, can be automatically performed the building of interdependent knowledge base, accurately select interdependent
Tuple is constrained, and calculates the posterior probability of the meaning of a word, disambiguation accuracy with higher.
Embodiment in this specification is described in a progressive manner, and mutually the same similar part may refer to each other.
For Installation practice, since it is substantially similar to the method embodiment, so describe fairly simple, correlation
Place illustrates referring to the part of embodiment of the method.
The embodiment of the present invention has been described in detail above, and specific embodiment used herein carries out the present invention
It illustrates, the above embodiments are only used to help understand methods and apparatus of the present invention；Meanwhile for the one of this field
As technical staff, according to the thought of the present invention, there will be changes in the specific implementation manner and application range, therefore this explanation
Book should not be construed as limiting the invention.
Claims (9)
1. a kind of verb Word sense disambiguation method based on interdependent constraint and knowledge, is being characterized in that, method includes the following steps:
Step 1: carrying out interdependent syntactic analysis to large-scale corpus, collect resulting interdependent tuple and simultaneously count its frequency, building according to
Deposit knowledge base；
Step 2: interdependent syntactic analysis is carried out to sentence where ambiguity verb,
The interdependent tuple being collected into is filtered, only retains governing word and dependent is notional word and dependence is following
The tuple of 17 kinds of setting types: adjective is supplied, and adverbial word modification connects, direct object side by side, infinitive modification, indirect object,
Nominal subject, passive nominal subject, participle modification, preposition modification, preposition subordinate clause modification, phrasal verb particle, purpose from
Sentence modification, relative clause modification, time modification, open subordinate clause are supplied, the control subject of open subordinate clause；Will filtering after it is resulting according to
The set for depositing tuple, the interdependent constraint set as ambiguity verb；
Step 3: being each meaning of a word of ambiguity verb, successively extracting synset, antisense word set, hypernym according to semantic dictionary
Collect and represents word set as the meaning of a word of the corresponding meaning of a word；
Step 4: representing word set according to interdependent knowledge base and the meaning of a word, each meaning of a word of ambiguity verb is successively calculated in interdependent constraint
The posterior probability of set；
When calculating posterior probability of the meaning of a word in interdependent constraint set, specifically:
Step 4-1) it successively calculates each meaning of a word and represents posterior probability of the word under each interdependent constraint condition, specifically:
It the meaning of a word is represented into a certain meaning of a word in word set represents word and be denoted asA certain interdependent constraint tuple is denoted as r'jAnd it indicates are as follows:
rj(w1,w2)；
If ambiguity verb is the governing word in interdependent constraint tuple, this posterior probability is calculated by formula (1)；
Wherein, c (rj,wsi,w2) expression dependency relationship type be rj, governing word beDependent is w2Interdependent tuple number
Amount；c(rj,*,w2) expression dependency relationship type be rj, dependent w2Interdependent tuple quantity；M indicates to wrap in semantic dictionary
The sum of the verB forms contained；
If ambiguity verb is the dependent in interdependent constraint tuple, this posterior probability is calculated by formula (2)；
Wherein,Expression dependency relationship type is rj, governing word w1, dependent beInterdependent tuple quantity；
c(rj,w1, *) expression dependency relationship type be rj, governing word w1Interdependent tuple quantity；M is indicated
VerB forms sum；
Step 4-2) posterior probability of each meaning of a word under the conditions of interdependent constraint set is successively calculated, specifically:
It is assumed that conditional sampling each other between each interdependent constraint tuple, then this posterior probability can be calculated by formula (3)；
Wherein, siIndicate that a certain meaning of a word, R indicate interdependent constraint set,Indicate that the meaning of a word represents word set, r 'jIndicate a certain interdependent
Tuple is constrained,Indicate that a certain meaning of a word represents word；
Step 5: selecting the maximum meaning of a word of posterior probability as the correct meaning of a word of ambiguity verb according to the calculated result of step 4；
If multiple meaning of a word obtain equal maximum a posteriori probability simultaneously, therefrom select the highest meaning of a word of word frequency as ambiguity verb just
The true meaning of a word；
The interdependent tuple is triple form, including dependency relationship type, governing word, dependent；Wherein governing word includes branch
Original shape and part-of-speech information with word, dependent include the original shape and part-of-speech information of dependent.
2. the verb Word sense disambiguation method according to claim 1 based on interdependent constraint and knowledge, which is characterized in that described
In step 1, when constructing interdependent knowledge base, specifically:
Step 1-1) to each document in Large Scale Corpus, interdependent syntactic analysis and lemmatization processing are successively carried out, is received
Collect the interdependent tuple wherein contained, and records the frequency of occurrence of each interdependent tuple；
Step 1-2) summarize the interdependent tuple-set for including in each document and frequency information, obtain interdependent knowledge base.
3. the verb Word sense disambiguation method according to claim 1 based on interdependent constraint and knowledge, which is characterized in that described
Interdependent syntactic analysis is carried out to the sentence where ambiguity verb in step 2 and lemmatization is handled, it is dynamic that collection is directed to ambiguity
The interdependent tuple of word.
4. the verb Word sense disambiguation method according to claim 1 based on interdependent constraint and knowledge, which is characterized in that described
In step 3, when extracting the meaning of a word of each meaning of a word and representing word set, using WordNet as semantic dictionary, specifically:
Step 3-1) synset of the current meaning of a word is obtained according to the Synonyms relationship of WordNet；
Step 3-2) the antisense word set of the current meaning of a word is obtained according to the Antonym relationship of WordNet；
Step 3-3) the upper word set of the current meaning of a word is obtained according to the Hypernym relationship of WordNet；
Step 3-4) above-mentioned three classes word set is merged, phrase and ambiguity verb are rejected behind, the meaning of a word generation as the current meaning of a word
Table word set.
5. a kind of verb word sense disambiguation device based on interdependent constraint and knowledge, which is characterized in that for realizing claim 1-4
Any one of based on it is interdependent constraint and knowledge verb Word sense disambiguation method device, which includes interdependent construction of knowledge base
The interdependent constraint set extraction unit of unit, ambiguity word, the ambiguity word meaning of a word represents word extraction unit, meaning of a word posterior probability calculates list
Member, ambiguity word meaning transference unit, in which:
Interdependent construction of knowledge base unit collects resulting interdependent tuple simultaneously for carrying out interdependent syntactic analysis to large-scale corpus
Its frequency is counted, interdependent knowledge base is constructed；
Extraction unit is gathered in the interdependent constraint of ambiguity word, for carrying out interdependent syntactic analysis to sentence where ambiguity verb, to collection
To interdependent tuple be filtered, only retain that governing word and dependent are notional word and dependence is following setting type
Tuple: adjective is supplied, and adverbial word modification connects, direct object side by side, infinitive modification, indirect object, nominal subject, quilt
Gerund subject, participle modification, preposition modification, preposition subordinate clause modification, phrasal verb particle, purpose subordinate clause modification, relationship from
Sentence modification, time modification, open subordinate clause are supplied, the control subject of open subordinate clause, the interdependent constraint set as ambiguity verb；
The ambiguity word meaning of a word represents word set extraction unit, for being each meaning of a word of ambiguity verb, successively extracting according to semantic dictionary
Synset, antisense word set, upper word set represent word set as the meaning of a word of the corresponding meaning of a word；
Meaning of a word posterior probability computing unit successively calculates ambiguity verb for representing word set according to interdependent knowledge base and the meaning of a word
Posterior probability of each meaning of a word in interdependent constraint set；
Ambiguity word meaning transference unit selects posterior probability most for the output data according to meaning of a word posterior probability computing unit
The correct meaning of a word of the big meaning of a word as ambiguity verb；If multiple meaning of a word obtain equal maximum a posteriori probability simultaneously, therefrom select
Select the correct meaning of a word of the highest meaning of a word of word frequency as ambiguity verb；
The interdependent tuple is triple form, including dependency relationship type, governing word, dependent；Wherein governing word includes branch
Original shape and part-of-speech information with word, dependent include the original shape and part-of-speech information of dependent.
6. the verb word sense disambiguation device according to claim 5 based on interdependent constraint and knowledge, which is characterized in that described
Interdependent construction of knowledge base unit further include:
The interdependent processing unit of single document, for each document in Large Scale Corpus, successively carry out interdependent syntactic analysis and
Lemmatization processing, collects the interdependent tuple wherein contained, and record the frequency of occurrence of each interdependent tuple；
Interdependent knowledge Merging unit obtains interdependent knowing for summarizing the interdependent tuple-set for including in each document and frequency information
Know library.
7. the verb word sense disambiguation device according to claim 5 based on interdependent constraint and knowledge, which is characterized in that described
Extraction unit is gathered in the interdependent constraint of ambiguity word further include:
The interdependent processing unit of ambiguity sentences, for being carried out at interdependent syntactic analysis and lemmatization to the sentence where ambiguity verb
Reason collects the interdependent tuple for being directed to ambiguity verb；
Interdependent tuple filter element, for being filtered to the interdependent tuple being collected into；
Collector unit is gathered in interdependent constraint, for after filter resulting interdependent tuple gather as ambiguity verb it is interdependent about
Constriction closes.
8. the verb word sense disambiguation device according to claim 5 based on interdependent constraint and knowledge, which is characterized in that described
The ambiguity word meaning of a word represents word set extraction unit further include:
It is synonymous to represent word extraction unit, the synset of the current meaning of a word is obtained for the Synonyms relationship according to WordNet；
Antisense represents word extraction unit, and the antisense word set of the current meaning of a word is obtained for the Antonym relationship according to WordNet；
It is upper to represent word extraction unit, the upper word set of the current meaning of a word is obtained for the Hypernym relationship according to WordNet；
The meaning of a word represents conflation of words unit, for by synset, antisense word set, upper set of words, simultaneously, rejecting phrase and ambiguity to be dynamic
For word behind, the meaning of a word as the current meaning of a word represents word set.
9. the verb word sense disambiguation device according to claim 5 based on interdependent constraint and knowledge, which is characterized in that described
Meaning of a word posterior probability computing unit further include:
The meaning of a word represents word posterior probability computing unit, for calculating after the specific meaning of a word represents word under specific interdependent constraint condition
Test probability；
Posterior probability computing unit of meaning of a word under the conditions of interdependent constraint set, gathers for calculating the specific meaning of a word in interdependent constraint
Under the conditions of posterior probability.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610496860.8A CN106202036B (en) | 2016-06-29 | 2016-06-29 | A kind of verb Word sense disambiguation method and device based on interdependent constraint and knowledge |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610496860.8A CN106202036B (en) | 2016-06-29 | 2016-06-29 | A kind of verb Word sense disambiguation method and device based on interdependent constraint and knowledge |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106202036A CN106202036A (en) | 2016-12-07 |
| CN106202036B true CN106202036B (en) | 2019-05-21 |
Family
ID=57463309
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610496860.8A Active CN106202036B (en) | 2016-06-29 | 2016-06-29 | A kind of verb Word sense disambiguation method and device based on interdependent constraint and knowledge |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106202036B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106202034B (en) * | 2016-06-29 | 2019-05-28 | 齐鲁工业大学 | A kind of adjective word sense disambiguation method and device based on interdependent constraint and knowledge |
| CN109726385A (en) * | 2017-10-31 | 2019-05-07 | 株式会社Ntt都科摩 | Word sense disambiguation method and equipment, meaning of a word extended method and device |
| CN109614620B (en) * | 2018-12-10 | 2023-01-17 | 齐鲁工业大学 | HowNet-based graph model word sense disambiguation method and system |
| CN109614624B (en) * | 2018-12-12 | 2023-07-25 | 广东小天才科技有限公司 | English sentence recognition method and electronic equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1617133A (en) * | 2003-11-14 | 2005-05-18 | 高庆狮 | Forming method for sentence meaning expression machine translation and electronic dictionary |
| CN1991819A (en) * | 2005-12-30 | 2007-07-04 | 北京法国电信研发中心有限公司 | Language morphological analyzer |
| CN101013421A (en) * | 2007-02-02 | 2007-08-08 | 清华大学 | Rule-based automatic analysis method of Chinese basic block |
-
2016
- 2016-06-29 CN CN201610496860.8A patent/CN106202036B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1617133A (en) * | 2003-11-14 | 2005-05-18 | 高庆狮 | Forming method for sentence meaning expression machine translation and electronic dictionary |
| CN1991819A (en) * | 2005-12-30 | 2007-07-04 | 北京法国电信研发中心有限公司 | Language morphological analyzer |
| CN101013421A (en) * | 2007-02-02 | 2007-08-08 | 清华大学 | Rule-based automatic analysis method of Chinese basic block |
Non-Patent Citations (2)
| Title |
|---|
| 基于依存和领域知识的词义消歧方法研究;鹿文鹏;《中国博士学位论文全文数据库 信息科技辑》;20150415(第4期);第2.5、4.3部分 |
| 多分类器集成的汉语词义消歧研究;吴云芳 等;《计算机研究与发展》;20080831(第8期);第3.1部分 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106202036A (en) | 2016-12-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106202034B (en) | A kind of adjective word sense disambiguation method and device based on interdependent constraint and knowledge | |
| CN110059311B (en) | Judicial text data-oriented keyword extraction method and system | |
| CN107220295B (en) | Searching and mediating strategy recommendation method for human-human contradiction mediating case | |
| US9613024B1 (en) | System and methods for creating datasets representing words and objects | |
| CN104933027B (en) | A kind of open Chinese entity relation extraction method of utilization dependency analysis | |
| EP3096246A1 (en) | Method, system and storage medium for realizing intelligent answering of questions | |
| CN108763213A (en) | Theme feature text key word extracting method | |
| Lindqvist et al. | A new approach to measuring lexical sophistication in L2 oral production | |
| CN107122413A (en) | A kind of keyword extracting method and device based on graph model | |
| CN106202036B (en) | A kind of verb Word sense disambiguation method and device based on interdependent constraint and knowledge | |
| CN106844331A (en) | A kind of sentence similarity computational methods and system | |
| CN105718585B (en) | Document and label word justice correlating method and its device | |
| CN104881402A (en) | Method and device for analyzing semantic orientation of Chinese network topic comment text | |
| CN109101551B (en) | Question-answer knowledge base construction method and device | |
| CN105095430A (en) | Method and device for setting up word network and extracting keywords | |
| CN109033320A (en) | A kind of bilingual news Aggreagation method and system | |
| CN107832467A (en) | A kind of microblog topic detecting method based on improved Single pass clustering algorithms | |
| CN106126501B (en) | A kind of noun Word sense disambiguation method and device based on interdependent constraint and knowledge | |
| Agrawal et al. | Semantic analysis of natural language queries using domain ontology for information access from database | |
| CN107180087A (en) | A kind of searching method and device | |
| CN105740225B (en) | A kind of Word sense disambiguation method merging sentence local context and document realm information | |
| CN106202033B (en) | A kind of adverbial word Word sense disambiguation method and device based on interdependent constraint and knowledge | |
| Yıldız et al. | Acquisition of Turkish meronym based on classification of patterns | |
| CN107818078B (en) | Semantic association and matching method for Chinese natural language dialogue | |
| CN109977235A (en) | A kind of determination method and apparatus of trigger word |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20201028 Address after: 250101 3 / F, block B, Yinhe building, 2008 Xinluo street, hi tech Zone, Jinan City, Shandong Province Patentee after: SHANDONG ECLOUD INFORMATION TECHNOLOGY Co.,Ltd. Address before: 250353 Qilu Industrial University, 3501 University Road, Science Park, Xincheng University, Ji'nan, Shandong Patentee before: Qilu University of Technology |