30 results about "Sentence clustering" patented technology

A method is provided for forming discrete segment clusters of one or more sequential sentences from a corpus of communication transcripts of transactional communications that comprises dividing the communication transcripts of the corpus into a first set of sentences spoken by a caller and a second set of sentences spoken by a responder; generating a set of sentence clusters by grouping the first and second sets of sentences according to a measure of lexical similarity using an unsupervised partitional clustering method; generating a collection of sequences of sentence types by assigning a distinct sentence type to each sentence cluster and representing each sentence of each communication transcript of the corpus with the sentence type assigned to the sentence cluster into which the sentence is grouped; and generating a specified number of discrete segment clusters by successively merging sentence clusters according to a proximity-based measure between the sentence types assigned to the sentence clusters within sequences of the collection.

A corpus generation device and method, the device comprising: a segmentation module, connected to at least one monolingual parallel corpus for segmenting a sentence into words and processing the segmented words by a knowledge-driven approach; a classification module, for classifying sentences having different tag sequences but the same meaning into the same sentence cluster; a mapping module, for determining the categories of sentence structures of all the sentences in the sentence cluster, recording and storing a mapping mode for transforming tags between sentence structures when different categories of sentence structures in the same sentence cluster are transformed; a sentence structure generation module, for generating sentence structures according to a first mapping mode between a first category of sentence structures in one of the sentence clusters and other categories of sentence structures in the same sentence cluster; and a corpus generation module, for nesting a word corresponding to a sequence tag to generate a new monolingual parallel corpus.

The invention provides a method for clustering news sentences based on semantic similarity, the method comprising the following steps: preprocessing the news sentences of the corpus to extract available words; using the available words to train the continuous bag-of-words model, Obtain the initial word vector of each available word; Utilize the initial sentence vector of each news sentence and the initial word vector of the left and right adjacent available words of a certain available word in this news sentence to iteratively train the described continuous word bag model, obtain the The current word vector of each available word in the news sentence and the final sentence vector of the news sentence; the average value of the word vectors of all available words of each news sentence, the one-hot vector of high-frequency words and the final sentence vector are merged, The semantic vector of the news sentence is obtained; the distance between the semantic vectors is calculated to obtain the semantic similarity between different news sentences, and the news sentences of the corpus are clustered accordingly. The invention also provides an electronic device and a computer-readable storage medium.

The invention discloses an information processing method and system for knowledge servers. The method comprises the following steps: obtaining all or part of the knowledge points as a knowledge point set; determining the semantic information of each knowledge point in the knowledge point set; determining a sentence cluster set corresponding to the knowledge points according to semantic information; determining corresponding chapter information according to the sentence cluster set; and determining corresponding digital resources according to the chapter information. According to the method, the semantic information of the knowledge points is comprehensively considered, and the manner of correlating the corresponding knowledges through the keywords input by the users is not used, so that the method more fits the real demands of the users and is capable of correlating the corresponding knowledges mostly fitting the user demands according to the semantic information of the knowledge points, so that the organization of the knowledges in the field in a knowledge point manner is really realized and the user experience is improved.

The invention relates to a sentence cluster extract method and device based on object knowledge points; the method comprises the following steps: obtaining knowledge point accuracy attributes; using the accuracy attribute to extract attribute of the knowledge point from to-be processed digit resources; using the accuracy attribute and fuzzy attribute to do sentence cluster hitching of the knowledge points in the to-be processed digit resources; obtaining the knowledge point sentence cluster. The accuracy attribute and fuzzy attribute of the knowledge points are added so as to improve knowledge point sentence cluster extract accuracy.

The invention discloses a key sentence extraction method, system, and computer-readable storage medium, wherein the key sentence extraction method includes the following steps: obtaining a target question and a target answer; performing sentence processing on the target answer to obtain several answer sentences; calculating each answer The correlation between the sentence and the target question is obtained to obtain the corresponding correlation score; the answer sentences are combined in pairs to obtain a number of answer pairs, and the coherence between the two answer sentences in the answer pair is calculated to obtain the corresponding coherence score; Based on the coherence score, each answer sentence is clustered to obtain several sets of sentence clusters; the correlation score corresponding to each answer sentence in the sentence cluster is extracted, and the relationship between the sentence cluster and the target question is calculated based on the extracted correlation score. Correlation degree: extract each answer sentence in the sentence cluster with the greatest correlation degree, and obtain the corresponding key sentence. The key sentences extracted by the present invention take both coherence and relevance into consideration, and can accurately express the central content of the target answer.

The present invention relates to a method for structured processing of a Chinese pathological text. The method comprises the following steps: extracting template information corresponding to each sample from a hierarchical stricture of a sample of text data of a pathological report text data and indicator; extracting the template information comprising short sentence segmentation and indicator name extraction; classifying the short sentences; with respect to each sample, in combination with a classification result cluster and a short sentence cluster, calculating a TF value, an IDF value and a C-value of each indicator name in an indicator name list in a short sentence language material, and screening out an indicator name whose TF value, IDF value and C-value satisfy a threshold, and using the obtained indicator name as a component of the final template. According to the present invention, a non-structured Chinese pathological text can be structured.

The present invention provides a text data viewpoint summary mining method that combines topic attributes and emotional information, including: preprocessing the text corpus of the topic; inputting the topic corpus and the background corpus; extracting the topic attributes of the topic corpus; and obtaining Add emotional polarity to the topic attribute of the sentence and vectorize the sentence; use the obtained topic attribute as the evaluation object to obtain the emotional attribute characteristics contained in the sentence, and use the topic attribute and sentiment analysis method to perform feature vectorization on a sentence; use the obtained topic The attribute set and text sentence feature vector set S construct a three-layer graph structure to cluster all text sentences; select sentences from clusters to form opinion summaries, and select sentences with high scores to form opinion summaries. The invention makes the topic attributes extracted by the method of extracting topic attributes more accurate, and also makes it not only applicable to the field of Chinese microblog, but also applicable to the fields of website news and commodity reviews.

The invention discloses a text cutting method based on subject information. The specific operation is as follows: preprocessing the input text and the training set to obtain a sentence composed of a series of words; then performing feature extraction to obtain its feature vector; and then according to its implication The semantic information of the input text is clustered to obtain a series of sentence clusters, and a numerical label is assigned to each cluster in order to obtain a series of single sentences with numerical labels; each sentence is assigned an existing sentence in the training set Topic tags, so that the existing topic tags in the training set are assigned to all sentences in the text; use the digital tag labeling results and the topic tag labeling results to make corrections to obtain text fragments with topic tags, and assign topic tags to the cut text In this way, the topics described in the sentences are clearly visible, and the position in the text describing the topic can be easily located according to the topic, making retrieval more convenient.

A method for identifying concepts in a plurality of interactions comprising: filtering, on a processor, the interactions based on intervals; creating, on the processor, a plurality of sentences from the filtered interactions; computing the prominence of each of said statements; deleting statements with low salience on said processor so as to produce a set of informative sentences; aggregating said set of informative sentences on said processor statement to generate a plurality of statement clusters, each of said clusters corresponding to one of said concepts; computing the salience of each of said clusters on said processor; and Each of the clusters is named on the processor.