38 results about "Dependency grammar" patented technology

The invention relates to a dependency semantic-based Chinese unsupervised open entity relationship extraction method. The method comprises the following steps of preprocessing an input text: performing Chinese word segmentation, part-of-speech tagging and dependency grammar analysis on the input text; performing named entity identification on the input text; arbitrarily selecting two entities from identified entities to form candidate entity pairs; searching for a dependency path between two entities in the candidate entity pairs; and analyzing whether a syntactic structure mapped by the dependency path is matched with a normal form of a dependency semantic normal form set or not, if yes, extracting words or phrases from the residual part of the input text according to the matched normal form to serve as relational words, forming a relational triple by the extracted relational words and the candidate entity pairs, and if not, performing normal form matching of a next group of the candidate entity pairs; and outputting the relational triple. Compared with the prior art, the method has the advantages that the calculation complexity is low; the extraction efficiency is high; distance position limitation is overcome; a simple sentence also can be extracted and the like.

The invention discloses a construction method and a construction system of an incremental-translation-oriented structured language model. The method comprises the following steps: step 1, performing dependency grammar analysis on incrementally generated translation segments to obtain dependency tree segment assembly; step 2, extracting a discriminant feature instance on the dependency tree segment assembly, and calculating a feature score of the discriminant feature instance by a discriminant dependency grammar model; step 3, performing pruning on the dependency tree segment assembly according to the feature score, taking a maximal value of the feature score as the score of the structured language model, reserving the segment having the highest score in the structured language model, and acquiring the optimized dependency tree segment assembly; and step 4, splicing the next translation segment onto the dependency tree segment assembly through a shift-specification operation, repeating the step 1, the step 2 and the step 3 until finishing the translation, and generating the complete dependency tree. According to the construction method and the construction system of the incremental-translation-oriented structured language model, the grammar information and the long-distance dependency information can be merged into the language model, the effective optimization algorithm is proposed for dynamic calculation of the structured language model in a decoding process, and the translation quality is improved.

The invention relates to a word sense disambiguation method fusing a sentence local context with document domain information, and belongs to the technical field of natural language processing. The word sense disambiguation method comprises the steps of: 1, carrying out dependency grammar analysis on a sentence where an ambiguous word is positioned, and obtaining sentence local context related words with a direct dependency relationship with the ambiguous word; 2, carrying out dependency grammar analysis on a domain document set, collecting all dependency tuples which the domain document set contains, and constructing a dependency tuple library; 3, carrying out statistic analysis on the dependency tuple library, and finding a group of domain related words with the closest relationship with the ambiguous word; 4, according to a dependency distribution similarity of the domain related words and word sense relevance between the domain related words and the local context, determining disambiguation weights of the domain related words; 5, merging the sentence local context related words with the domain related words, and constructing a related word set; and 6, according to weighted accumulation relevance of each word sense of the ambiguous word and the related word set, judging a correct word sense. According to the method disclosed by the invention, adaptability of a word sense disambiguation system on a specific domain can be improved, and disambiguation accuracy can be improved.

A method of grammar rule induction comprises obtaining a monolingual set of phrases from a bilingual corpus of translation pairs. For each of the monolingual phrases in turn, initialising, with inactive edges formed from headwords identified in the phrase, the agenda of a dependency grammar chart parser arranged to form packed edges in the chart. Running the chart parser and adding to the agenda, for each inactive edge removed from the agenda, one or more active edges created as if all possible grammar rules existed. When the agenda is empty, ascertaining the alternations of each edge in the packed edge corresponding to the complete phrase, and finding their respective highest frequencies. For the set of phrases, summing, for each alternation, its respective highest frequencies, and ranking the sums. Then, selecting alternations in rank order to form the required set of grammar rules until the required set has become sufficient such that for each monolingual phrase there exists at least one analysis corresponding to the required set of grammar rules.

The invention discloses an e-commerce comment sentiment analysis model based on part-of-speech features and viewpoint features in combination with a convolutional neural network. The method comprisesthe following steps: step 1, formulating rules by utilizing part-of-speech, dependency grammar analysis and semantic dependency analysis to extract viewpoint features; step 2, introducing part-of-speech features and viewpoint features by adopting a vector splicing method on the basis of word vector representation; and step 3, taking a word vector and an extended feature vector as two input channels of a convolutional neural network to perform sentiment analysis. The problem that comments are inconsistent with scores of the comments is solved, and then merchants are helped to improve the service quality and upgrade the product performance.

The invention relates to a semantic role tagging and semantic extracting method of an unrestricted path natural language. The method comprises the steps that firstly Chinese path natural language linguistic data under an unrestricted condition is collected and a Chinese path natural language linguistic database is built; secondly, automatic tagging of the path natural language linguistic data is achieved by using the semantic role tagging method based on chunk analysis and dependency grammar analysis; finally, according to a semantic role tagging result, path unit division is sequentially conducted, and navigational semantic information of path units is extracted. Path natural language semantic role tagging is conducted by using the semantic role tagging method based on chunk analysis and dependency grammar analysis, according to the extracted semantic role tagging result, path unit division is conducted, and finally the semantic information of the path units is extracted. By means of the method, path unit division can be sequentially and accurately conducted, the path semantic information can be accurately extracted, and accordingly the method can provide guidance for smooth implementation of asking for directions and navigation with a robot.

The invention discloses a user comment attribute extraction method based on bi-directional dependency syntactic tree representation. The method comprises the steps that 1) given user comment text is preprocessed, and a dependency syntactic tree is generated; 2) a bi-directional dependency syntactic tree representation network is established, and dependency characteristics among words are extracted; 3) the dependency characteristics are input into a bi-directional LSTM nerve network, sequence characteristics among the words are extracted on the basis of the dependency characteristics, and accordingly the dependency characteristics are effectively combined with the sequence characteristics; 4) the combined characteristics are coded by using a linear chain condition random field; 5) a Viterbialgorithm is used for conducting decoding to obtain comment attributes of all text. According to the user comment attribute extraction method, the aim is effectively achieved that in user comment attribute extraction tasks, the dependency characteristics of text syntax are extracted and efficiently combined with the sequence characteristics to achieve end-to-end training; the condition random field is used for coding the combined characteristics, the Viterbi algorithm is used for decoding the combined characteristics, and the good effect can be achieved in the user comment attribute extraction tasks.

An automatic correction method for English composition grammatical errors based on multiple features, including a grammatical error correction preprocessing module, a grammatical error correction model training module, and a grammatical error checking and correction module; wherein the grammatical error correction preprocessing module performs word part-of-speech tagging on the input training text set , sentence syntax analysis and word frequency statistics; the grammar error correction model training module extracts words and their part-of-speech context grammatical features, words and Its part-of-speech structure depends on grammatical features, words and grammatical features of their parts of speech, calculates the weight of word grammatical features, and outputs a grammatical error correction statistical model; the grammatical error checking and correction module uses the grammatical error correction statistical model and the grammatical error correction rule model to treat error-correcting compositions Grammatical error correction, output the correction results of grammatical errors in English compositions. This method realizes the automatic correction of 11 common English grammatical errors in English composition.

The invention provides a translation rule extraction method and a translation method based on a dependency grammar tree. A translation sequence adjusting relationship is directly expressed in the translation rule that a source end is used as a head word and a dependency grammar tree fragment and a target end consisting of modifiers of the head word are used as strings, and thus the translation rule can be used for definitely guiding the translation process. According to the translation rule extracted by the method, the performance of the translation method based on the dependency grammar tree can be improved. On a data set of 1.54 million of parallel bilingual corpus, the performance of a dependency grammar tree to a string translation model is improved by 1.68 BLEU (Bilingual Evaluation Understudy) points compared with that of a component tree to the string model.

The invention discloses a construction method and a construction system of an incremental-translation-oriented structured language model. The method comprises the following steps: step 1, performing dependency grammar analysis on incrementally generated translation segments to obtain dependency tree segment assembly; step 2, extracting a discriminant feature instance on the dependency tree segment assembly, and calculating a feature score of the discriminant feature instance by a discriminant dependency grammar model; step 3, performing pruning on the dependency tree segment assembly according to the feature score, taking a maximal value of the feature score as the score of the structured language model, reserving the segment having the highest score in the structured language model, and acquiring the optimized dependency tree segment assembly; and step 4, splicing the next translation segment onto the dependency tree segment assembly through a shift-specification operation, repeating the step 1, the step 2 and the step 3 until finishing the translation, and generating the complete dependency tree. According to the construction method and the construction system of the incremental-translation-oriented structured language model, the grammar information and the long-distance dependency information can be merged into the language model, the effective optimization algorithm is proposed for dynamic calculation of the structured language model in a decoding process, and the translation quality is improved.

The invention relates to a medical data collection and analysis method, comprising the following steps: 1. Uploading original data to a data platform; 2. Converting the original data into data in RDF format by using a semantic annotation algorithm based on a combination of conditional random fields and dependency grammar ; 3. Through the data mining algorithm, the RDF data of the same patient in the data processed in the previous step are associated and stored in the Hbase database based on the distributed file system; 4. Using statistical methods and machine learning methods to analyze the data in the Hbase database Analyze the data and draw the analysis conclusion; 5. Organize and classify the analysis conclusion, and construct the treatment plan knowledge base. The present invention collects the entire clinical diagnosis and treatment data of personnel in a targeted manner. By analyzing a large amount of data, it can make clinical auxiliary diagnosis, disease early warning and analyze the patient's behavior, which can help doctors formulate more effective, accurate and personalized customized treatment plan.

The invention relates to a multi-granularity semantic chunk based entity attribute and attribute value extracting method, and belongs to the technical field of Web mining and information extraction. The method comprises the following steps that a corpus set is constructed and free text extraction is performed; a corpus is subjected to word segmentation, part-of-speech tagging and phrase recognition; the corpus is subjected to semantic role labeling; the corpus is subjected to dependency grammar analysis; the corpus is subjected to semantic dependency analysis; candidate entities, attributes and attribute value triads based on three granularities of words, phrases and semantic roles are extracted; the candidate entities, attributes and attribute value triads are corrected and subjected to error classification by means of a trained classifier. Compared with the prior art, the entities, attributes and attribute value triads based on three granularities of words, phrases and semantic roles are automatically extracted from a free text, the entity attribute and attribute value extraction accuracy and efficiency are improved, and the wide application prospect is achieved in the fields of theme detection, information retrieval, automatic abstracting, question and answer systems and the like.

The invention relates to a word sense disambiguation method fusing a sentence local context with document domain information, and belongs to the technical field of natural language processing. The word sense disambiguation method comprises the steps of: 1, carrying out dependency grammar analysis on a sentence where an ambiguous word is positioned, and obtaining sentence local context related words with a direct dependency relationship with the ambiguous word; 2, carrying out dependency grammar analysis on a domain document set, collecting all dependency tuples which the domain document set contains, and constructing a dependency tuple library; 3, carrying out statistic analysis on the dependency tuple library, and finding a group of domain related words with the closest relationship with the ambiguous word; 4, according to a dependency distribution similarity of the domain related words and word sense relevance between the domain related words and the local context, determining disambiguation weights of the domain related words; 5, merging the sentence local context related words with the domain related words, and constructing a related word set; and 6, according to weighted accumulation relevance of each word sense of the ambiguous word and the related word set, judging a correct word sense. According to the method disclosed by the invention, adaptability of a word sense disambiguation system on a specific domain can be improved, and disambiguation accuracy can be improved.