CN109408812A - A method of the sequence labelling joint based on attention mechanism extracts entity relationship - Google Patents

A method of the sequence labelling joint based on attention mechanism extracts entity relationship Download PDF

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CN109408812A
CN109408812A CN201811157788.1A CN201811157788A CN109408812A CN 109408812 A CN109408812 A CN 109408812A CN 201811157788 A CN201811157788 A CN 201811157788A CN 109408812 A CN109408812 A CN 109408812A
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sentence
indicate
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entity
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刘博�
张佳慧
史超
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Beijing University of Technology
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    • 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
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
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Abstract

The method that the sequence labelling joint based on attention mechanism that the invention discloses a kind of extracts entity relationship, first carry out the Chinese sentence corpus of magnanimity the pretreatment such as to denoise, then it is segmented, being converted into vector to single word indicates, the input as two-way length memory network in short-term in this way encodes individual character.Using two-way length, memory network can not only learn long-term and short-term Dependency Specification in short-term, the data of input layer can also be calculated by forwardly and rearwardly both direction simultaneously, to learn past contextual information and following contextual information, this is very useful to the sequence labelling of sentence.Then attention mechanism is introduced in decoding layer, so that decoding generates the information vector of available front coding stage each character hidden layer when annotated sequence, the information for making full use of list entries to carry.The entity tag probability of each word is calculated finally by softmax, can effectively obtain final annotated sequence and carries out the combination of entity and its corresponding relationship.

Description

A method of the sequence labelling joint based on attention mechanism extracts entity relationship
Technical field
The invention belongs to natural language processing technique field more particularly to the attentions of some sequence labellings and deep learning Mechanism joint extracts the entity relationship in non-structural text.
Background technique
With the arriving of big data era, various information are flooded with our life, and most of is all rambling Data or non-structural natural language text, Yao Congzhong extract useful information and are just particularly important.Information extraction one As include two subtasks being closely connected i.e. Entity recognition and Relation extraction, basic goal be exactly from structureless network or The semantic relation between name entity and entity is excavated in the text of field, is extracted information and is carried out structured storage, so as to Intuitively understand in people, receive information.Entity relation extraction is also a vital task in natural language processing simultaneously, it It is all related in multiple fields, for example, the building of domain knowledge map, information retrieval, machine translation, automatic question answering etc., all have There is stronger supporting role, thus there is biggish researching value and research significance.
According to the degree of dependence to labeled data, entity relation extraction method can be divided into supervised learning method, semi-supervised Learning method, unsupervised learning method and open abstracting method.There is the learning method of supervision to regard Relation extraction task to divide Class problem designs effective feature according to training data, to learn various disaggregated models, then uses trained classifier Projected relationship.Existing supervised learning Relation extraction method has been achieved for preferable effect, but their heavy dependence parts of speech The natural language processings marks such as mark, syntax parsing provide characteristic of division.And natural language processing annotation tool often exists greatly Mistake is measured, these mistakes will constantly propagate amplification, the final effect for influencing Relation extraction in Relation extraction system.Recently, Many researchers start the technical application by deep learning into Relation extraction.Rink et al. extracts entity first, then Identify the relationship between them, frame of this separation is easily handled two tasks all and more flexible.Socher etc. People proposes to solve the problems, such as Relation extraction using recurrent neural network and syntactic structure.This method is first by recurrent neural network Syntax parsing first is carried out to sentence and constructs a syntax tree, merges according to the syntactic structure iteration of sentence, finally obtains this The vector of sentence indicates.This method can effectively consider the syntactic structure information of sentence, but can not consider two realities well Position and semantic information of the body in sentence.Zeng et al. proposes to carry out entity relation extraction using convolutional neural networks.They Using the position vector of word and term vector as the input of convolutional neural networks, and in feature be added entity position vector and its His relevant vocabulary, enables the entity information in sentence to be preferably applied in Relation extraction.It will but this above The method that Entity recognition and Relation extraction are implemented separately ignores inner link between the two.Zheng et al. proposes one kind Entity relationship combines abstracting method end to end, and joint is extracted and is converted into sequence labelling problem, passes through long memory network in short-term Question sentence is coded and decoded, and adds biasing loss and finally obtains annotated sequence.This algorithm takes full advantage of context Information, but apply on English data set, it is very different with Chinese corpus, and the model when list entries is very long It is difficult to acquire reasonable vector expression, all contextual informations not distinguished, which limits the performances of model, lead to mould The effect of type is poor.
Summary of the invention
The technical problem to be solved by the present invention is to propose that a kind of sequence labelling joint based on attention mechanism (ATT) is taken out The method for taking entity relationship, the new dimension model proposed first according to Zheng et al., using natural language sentence as Seq2Seq List entries, word, which is converted into vector, by embedding layers indicates, and uses two-way length memory network (LSTM) in short-term It is encoded, the mark to relationship is added on the basis of former dimension model, is then equally using long short-term memory net Network exports addition attention mechanism, the sequence marked finally by softmax layers when being decoded, and thus may be used To obtain the annotated sequence to entire sentence, convenient for extracting entity relationship by the identification of sequence.
The present invention proposes a kind of method that joint extracts entity relationship towards magnanimity Chinese sentence corpus.First by magnanimity Chinese sentence corpus carry out the pretreatment such as denoising, then segmented, to single word be converted into vector indicate, in this way may be used Individual character is encoded using the input as two-way length memory network in short-term.Using two-way length, memory network can not only be learned in short-term It practises for a long time with short-term Dependency Specification, can also simultaneously calculate the data of input layer by forwardly and rearwardly both direction, thus Learn past contextual information and following contextual information, this is very useful to the sequence labelling of sentence.Then exist Decoding layer introduces attention mechanism, so that decoding generates available front coding stage each character hidden layer when annotated sequence Information vector, make full use of list entries carry information.The entity tagization for calculating each word finally by softmax is general Rate can effectively obtain final annotated sequence and carry out the combination of entity and its corresponding relationship.
To achieve the goals above, the invention adopts the following technical scheme: extracting entity to combine by annotated sequence Relationship realizes the conversion from statement sequence to annotated sequence using Seq2Seq model joint Attention mechanism.It first passes around The embedding layers of high dimension vector by the expression word in higher dimensional space (dimension in space is usually the size of dictionary) is mapped to low Vector in dimension (tens dimensions) continuous space, i.e., sentence is converted into vector indicates.Then using obtained term vector as two-way The input of long memory network in short-term is encoded, including two parallel LSTM layers: positive LSTM layers and LSTM layers reversed, difference Calculate forward and calculate backward.Finally by decoding layer length in short-term memory network by the vector generated before export at mark Sequence is infused, and attention mechanism is added wherein, allows the mark of generation that can only instead of not pay close attention to global semantic coding vector, It increases one " attention range ", indicates to pay close attention to which of list entries part when next output mark, Then next output is generated according to the region of concern.Finally decoding obtained annotated sequence should include that this word is in entity In position, corresponding relationship and the role in relationship combine to obtain entity relationship according to annotated sequence.This has just obtained one It is a that abstracting method is combined based on the entity relationship of sequence labelling and LSTM attention mechanism.
The technical solution that this method uses extracts entity relationship for a kind of sequence labelling joint based on attention mechanism Method, method includes the following steps:
Step 1, the entity relationship data set for obtaining Opening field are simultaneously pre-processed, and pretreated process is by the data Collection is divided into training set and test set two parts, this two parts all includes sentence to be processed, is divided sentence wherein included Word processing, so that sentence is converted into individual word.
The each word obtained in sentence after pretreatment is converted into vector expression by embeding layer, and is input to double by step 2 It is encoded into the long coding layer of memory network in short-term.
Memory network decodes in short-term by the length with attention mechanism for step 3, the output for obtaining coding layer, and wherein Attention mechanism is added.
Step 4 exports entity tag probability, completion and composite entity based on mark predicted vector by softmax layers And relationship, obtain triple.
Preferably, step 1 specifically includes the following steps:
Step 1.1, the entity relationship data set for obtaining Opening field, and all data concentrated to data carry out at denoising Reason, including the useless blank character of removal, capitalization are converted into small letter etc.；
Data set is divided into training set and test set by step 1.2；
Step 1.3 establishes user's Custom Dictionaries, such as long word and proper noun, and using at Harbin Institute of Technology's natural language Science and engineering tool LTP segments sentence；
Preferably, step 2 specifically includes the following steps:
Step 2.1, the corpus training Word2Vec term vector model using wikipedia, the dimension of term vector is 300；
Step 2.2, the term vector mapping matrix generated by Word2Vec, obtain the corresponding term vector of each word, whole The term vector of a sentence is expressed as { w1,w2,......wn, wnIndicate that the term vector of n-th of word indicates；
The term vector that embeding layer is converted is input to two-way LSTM coding layer by step 2.3, it includes LSTM layers of forward direction, instead To LSTM layers and articulamentum；
Step 2.4 encodes contextual information and semantic information by two-way LSTM coding layer, and positive LSTM is from w1 To wn, reversed LSTM is from wnTo w1, and the coding vector of entire sentence, calculation formula such as formula (1) are exported in the hidden layer of neuron (2) shown in (3) (4) (5) (6).
it=δ (Wwiwt+Whiht-1+Wcict-1+bi) (1)
ft=δ (Wwfwt+Whfht-1+Wcfct-1+bf) (2)
zt=tanh (Wwcwt+Whcht-1+bc) (3)
ct=ftct-1+itzt (4)
ot=δ (Wwowt+Whoht-1+Wcoct+bo) (5)
ht=ot tanh(ct) (6)
I, f, z, o of formula (1) (2) (3) (5) are respectively input gate, forget door, update door, out gate, the c in formula (4)t Indicate the cell state of t moment, the h in formula (6)tIndicate the output of t moment, W indicates relevant parameter, the W in formula (3)wcAnd Whc The parameter of the parameter of the cell state of word and the cell state of output is respectively indicated, the W in (1) (2) (5)wx、WhxAnd WcxRespectively Indicate the parameter of x words, the parameter of output and the parameter of cell state, wtIndicate that t-th of word, b indicate biasing loss, δ table Show sigmoid activation primitive.
Preferably, step 3 specifically includes the following steps:
Output after two-way LSTM coding is input to LSTM decoding by step 3.1；
Attention mechanism is added in step 3.2, makes position of the model learning context in entity and in relationship Role, the result y that final decoded result is predicted by last momentt-1, the input s at this momenttOn relevant with to this moment Hereafter annotated sequence cseqtIt obtains, shown in calculation formula such as formula (7), (8).
p(yt|y1,y2,......,yt-1, cseq) and=g (yt-1,st,cseqt) (7)
L in formula (8)xIndicate the length of sentence, aseqtjIndicate the Automobile driving of j-th of word mark in read statement Coefficient, hjIndicate the semantic coding of j-th of word, cseqtIndicate the relevant context annotated sequence of t moment.
Step 3.3, the decoded sequence of output.
Opposite with the prior art, the present invention has following clear superiority:
For the present invention when joint extracts entity relationship, will extract Task Switching is sequence labelling problem, using based on two-way The Seq2Seq model of long memory network in short-term, and entity and relationship are marked word-based attention mechanism is wherein added Note.Opposite other methods make to carry out joint in this way and extract to avoid first to extract entity and extract error caused by relationship again Accumulation problem has deepened the inner link between entity and relationship, and the Feature Engineering for not needing complexity can learn to length Short-term Dependency Specification.In addition, attention mechanism is introduced in decoding layer, can reduce the computation burden of processing higher-dimension list entries, By the subset of the selection input of structuring, data dimension is reduced, while allowing task processing system to focus more on and finding input sequence Significant useful information relevant to currently exporting in column, to improve the quality of output.In addition, sequence is added in attention mechanism In decoding process in column mark task, the accuracy of sequence labelling can be effectively improved, improves whole efficiency.To sum up institute It states, it is proposed in this paper to be had based on the entity relationship of sequence labelling and attention mechanism joint abstracting method towards magnanimity Chinese Material, the advantage that entity relationship connection is close, accuracy rate is high.
Detailed description of the invention
Fig. 1 is the flow chart of method involved in the present invention.
Fig. 2 is the Seq2Seq model structure of sequence labelling task in the present invention.
Fig. 3 is the structure chart for the attention model that present invention decoding uses.
Fig. 4 is the present invention finally to the example of sentence annotated sequence.
Specific embodiment
Yi Xiajiehejutishishili,Bing Canzhaofutu,Dui Benfamingjinyibuxiangxishuoming.
Hardware device used in the present invention has PC machine 1；
As shown in Figure 1, the present invention provides a kind of side of sequence labelling joint extraction entity relationship based on attention mechanism Method, specifically includes the following steps:
Step 1, the entity relationship data set for obtaining Opening field are simultaneously pre-processed, and pretreated process is by the data Collection is divided into training set and test set two parts, this two parts all includes sentence to be processed, is divided sentence wherein included Word processing, so that sentence is converted into individual word.
Step 1.1, the entity relationship data set of Opening field is obtained, and denoising is carried out to these data, including go Except useless blank character, capitalization are converted into small letter etc.；
Step 1.2, data set is divided into training set and test set；
Step 1.3, user's Custom Dictionaries, such as long word and proper noun are established, and using at Harbin Institute of Technology's natural language Science and engineering tool LTP segments sentence；
As shown in Fig. 2, obtaining word segmentation result after to Chinese sentence " founder of Apple Inc. is Qiao Busi " pretreatment " founder of Apple Inc. is Qiao Busi ".
Step 2, each word in sentence after pretreatment is converted into vector by embeding layer indicates, and is input to two-way length It is encoded in short-term memory network, the structure of whole Seq2Seq model is as shown in Figure 2.
Step 2.1, using the corpus training Word2Vec term vector model of wikipedia, the dimension of term vector is 300, will The word segmentation result that step 1 obtains inputs in trained term vector model；
Step 2.2, the term vector mapping matrix generated by Word2Vec, obtains the corresponding term vector of each word, word Vector can be expressed as { w1,w2,......,wn}；
Step 2.3, the term vector that embeding layer is converted is input to two-way LSTM coding layer, it includes LSTM layers of forward direction, instead To LSTM layers and articulamentum；
Step 2.4, contextual information and semantic information are encoded by two-way LSTM coding layer, positive LSTM is from w1 To wn, reversed LSTM is from wnTo w1, and the coding vector of entire sentence, calculation formula such as formula (1) are exported in the hidden layer of neuron (2) shown in (3) (4) (5) (6).
it=δ (Wwiwt+Whiht-1+Wcict-1+bi) (1)
ft=δ (Wwfwt+Whfht-1+Wcfct-1+bf) (2)
zt=tanh (Wwcwt+Whcht-1+bc) (3)
ct=ftct-1+itzt (4)
ot=δ (Wwowt+Whoht-1+Wcoct+bo) (5)
ht=ot tanh(ct) (6)
I, f, z, o of formula (1) (2) (3) (5) are respectively input gate, forget door, update door, out gate, the c in (4)tTable Show the cell state of t moment, the h in (6)tIndicate the output of t moment, W indicates relevant parameter, the W in (3)wcAnd WhcTable respectively Show the parameter of the parameter of the cell state of word and the cell state of output, the W in (1) (2) (5)wx、WhxAnd WcxRespectively indicate x Word parameter, the parameter of output and the parameter of cell state, wtIndicate that t-th of word, b indicate biasing loss, δ is indicated Sigmoid activation primitive.
Step 3, by the length with attention mechanism, memory network decodes vector coding layer obtained in short-term, and wherein Attention mechanism is added.
Step 3.1, the output after two-way LSTM coding is input to LSTM decoding；
As shown in figure 3, contextual information when by sequence labelling is imagined as being by a series of<Key, Value>word is to structure At given some to be marked words Query is obtained by calculating the similitude or correlation of Query and each Key at this time Each Key corresponds to the weight coefficient of Value, is then weighted summation to Value to get final Attention number has been arrived Value.
Attention mechanism is added in step 3.2, makes position of the model learning context in entity and in relationship Role, the result y that final decoded result is predicted by last momentt-1, the input s at this momenttOn relevant with to this moment Hereafter annotated sequence cseqtIt obtains, shown in calculation formula such as formula (7), (8).
p(yt|y1,y2,......,yt-1, cseq) and=g (yt-1,st,cseqt) (7)
L in formula (8)xIndicate the length of sentence, aseqtjIndicate the Automobile driving of j-th of word mark in read statement Coefficient, hjIndicate the semantic coding of j-th of word, cseqtIndicate the relevant context annotated sequence of t moment.
Step 3.3, decoded sequence is exported.
Step 4, entity tag probability is exported based on mark predicted vector by softmax layers.
As shown in figure 4, Seq2Seq model generates the annotated sequence of a sentence, and according to mark completion and composite entity And relationship, finally obtain entity relationship triple.
Above embodiments are only exemplary embodiment of the present invention, are not used in the limitation present invention, protection scope of the present invention It is defined by the claims.Those skilled in the art can within the spirit and scope of the present invention make respectively the present invention Kind modification or equivalent replacement, this modification or equivalent replacement also should be regarded as being within the scope of the present invention.

Claims (4)

1. a kind of method that the sequence labelling joint based on attention mechanism extracts entity relationship, it is characterised in that:
Method includes the following steps:
Step 1, the entity relationship data set for obtaining Opening field are simultaneously pre-processed, and pretreated process is by the data set point For training set and test set two parts, this two parts all includes sentence to be processed, is carried out at participle to sentence wherein included Reason, so that sentence is converted into individual word；
The each word obtained in sentence after pretreatment is converted into vector expression by embeding layer, and is input to two-way length by step 2 It is encoded in the coding layer of short-term memory network；
Memory network decodes in short-term by the length with attention mechanism for step 3, the output for obtaining coding layer, and is added wherein Attention mechanism；
Step 4 exports entity tag probability, completion and composite entity and pass based on mark predicted vector by softmax layers System, obtains triple.
2. the method that a kind of sequence labelling joint based on attention mechanism according to claim 1 extracts entity relationship, It is characterized by:
Step 1 specifically includes the following steps:
Step 1.1, the entity relationship data set for obtaining Opening field, and denoising is carried out to all data that data are concentrated, Including removing useless blank character, capitalization is converted into small letter；
Data set is divided into training set and test set by step 1.2；
Step 1.3 establishes user's Custom Dictionaries, such as long word and proper noun, and utilizes Harbin Institute of Technology's natural language processing work Tool LTP segments sentence.
3. the method that a kind of sequence labelling joint based on attention mechanism according to claim 1 extracts entity relationship, It is characterized by: step 2 specifically includes the following steps:
Step 2.1, the corpus training Word2Vec term vector model using wikipedia, the dimension of term vector is 300；
Step 2.2, the term vector mapping matrix generated by Word2Vec, obtain the corresponding term vector of each word, entire language The term vector of sentence is expressed as { w1,w2,......wn, wnIndicate that the term vector of n-th of word indicates；
The term vector that embeding layer is converted is input to two-way LSTM coding layer by step 2.3, it includes LSTM layers of forward direction, reversely LSTM layers and articulamentum；
Step 2.4 encodes contextual information and semantic information by two-way LSTM coding layer, and positive LSTM is from w1To wn, Reversed LSTM is from wnTo w1, and the coding vector of entire sentence, calculation formula such as formula (1) (2) are exported in the hidden layer of neuron (3) shown in (4) (5) (6)；
it=δ (Wwiwt+Whiht-1+Wcict-1+bi) (1)
ft=δ (Wwfwt+Whfht-1+Wcfct-1+bf) (2)
zt=tanh (Wwcwt+Whcht-1+bc) (3)
ct=ftct-1+itzt (4)
ot=δ (Wwowt+Whoht-1+Wcoct+bo) (5)
ht=ot tanh(ct) (6)
I, f, z, o of formula (1) (2) (3) (5) are respectively input gate, forget door, update door, out gate, the c in formula (4)tIndicate t The cell state at moment, the h in (6)tIndicate the output of t moment, W indicates relevant parameter, the W in formula (3)wcAnd WhcIt respectively indicates The parameter of the cell state of the parameter and output of the cell state of word, the W in formula (1) (2) (5)wx、WhxAnd WcxRespectively indicate x Word parameter, the parameter of output and the parameter of cell state, wtIndicate that t-th of word, b indicate biasing loss, δ is indicated Sigmoid activation primitive.
4. the method that a kind of sequence labelling joint based on attention mechanism according to claim 1 extracts entity relationship, It is characterized by: step 3 specifically includes the following steps:
Output after two-way LSTM coding is input to LSTM decoding by step 3.1；
Attention mechanism is added in step 3.2, makes position of the model learning context in entity and the role in relationship, The result y that final decoded result is predicted by last momentt-1, the input s at this momenttWith context relevant to this moment Annotated sequence cseqtIt obtains, shown in calculation formula such as formula (7), (8)；
p(yt|y1,y2,......,yt-1, cseq) and=g (yt-1,st,cseqt)(7)
L in formula (8)xIndicate the length of sentence, aseqtjIndicate the Automobile driving coefficient of j-th of word mark in read statement, hjIndicate the semantic coding of j-th of word, cseqtIndicate the relevant context annotated sequence of t moment；
Step 3.3, the decoded sequence of output.
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