11 results about "Sentence extraction" patented technology

The invention relates to the technical field of text abstract generation, in particular to an audio text abstract generation method, which comprises the following steps of: performing multi-scale segmentation on acquired audio, performing text generation by using acoustic characteristic data of audio clips subjected to multi-scale segmentation, and performing text abstract generation by adopting a text-time alignment optimization mechanism based on attention weight. According to the method, the generated text is aligned and adjusted, the aligned and adjusted text is obtained, keyword extraction is performed on the aligned and adjusted text, target sentence extraction is performed according to the extracted keywords, abstract text generation is performed by utilizing the extracted keywords and the target sentences, and the abstract text generation accuracy is improved.

The application provides a structured outline driven scientific report automatic generation method and system, and relates to the technical field of natural language processing, the method comprises the following steps: performing semantic analysis and matching on a user input research topic to generate a first-level structure node, extracting and dividing research problems from multiple standard scientific literatures to construct a second-level structure node, performing opinion sentence extraction and refinement to generate a third-level structure node, generating a structured outline according to the first-level, second-level and third-level structure nodes and corresponding explicit binding relationships, performing task analysis, generating a task set according to the hierarchical order of the structured outline, generating a target scientific report and outputting the target scientific report by performing text generation, assembling and integrating. The technical problems of unstable structure, easy divergence of theme, difficulty in corresponding paragraphs and evidence, and easy repetition of chapter content in the existing scientific report automatic generation are solved. The technical effect of stable structure, focused theme and traceable content of the scientific report automatic generation is achieved.

This invention relates to a method for analyzing routine passenger complaints in urban rail transit, comprising: S1, multi-source data collection; S2, passenger complaint analysis; and S3, early warning mechanism construction and automatic departmental push notifications. This invention also relates to a system for analyzing routine passenger complaints in urban rail transit, comprising a data collection module, a passenger complaint analysis module, and an early warning module. This invention addresses the problem of insufficient data dimensions in existing systems, significantly improving the comprehensiveness of analysis results; based on large language models and deep semantic analysis technology, it can accurately identify the true content and sentiment of passenger complaints, achieving high-precision classification and key sentence extraction; it improves the efficiency of operating units in handling complaints; and it presents overall complaint trends through visual charts, providing real-time decision-making basis for operation management.

The invention discloses a space-time mapping knowledge domain construction method based on a large language model and Beidou grid coding. The method comprises the steps that semantic partitioning is conducted on an input text through the large language model LLM, fact declarative sentence extraction is conducted on each block, and triple extraction, time entity extraction and space geographic entity extraction are conducted on the fact declarative sentences respectively; performing time attribute coding on the time entity; constructing a gridded Beidou satellite geographic map and obtaining a Beidou grid coding data set, and performing associated grid matching coding on spatial geographic entities to form corresponding spatial attributes; and collecting the time entity containing the time attribute, the space geographic entity containing the space attribute and the triple to construct the space-time knowledge graph. According to the method, attribute coding is carried out on the time entities and the space geographic entities, the coded time entities, the coded space geographic entities and the triple are collected and constructed to obtain the space-time knowledge graph, space-time information in the entities can be deeply mined, and the space-time reasoning capability is improved.

The invention discloses an information retrieval and management method for construction of a land management policy knowledge graph. The method comprises the following steps: performing clause and fact declarative sentence extraction on policy clauses in a structured land policy database by utilizing a large language model, and taking a time entity and a spatial geographic entity as attributes of the policy clauses; constructing a time entity containing a time attribute, a space geographic entity containing a space attribute and a space-time knowledge graph database of a triple; performing semantic partitioning on the query data by using a large language model to obtain a query combination; and creating a plurality of parallel retrieval paths matched with the query combination by utilizing a space-time retrieval planner, summarizing search results, and then calling and summarizing the retrieval results containing a plurality of policy terms from the structured land policy database. According to the method, the precision and generalization of complex space-time semantic understanding are improved, multi-dimensional collaborative retrieval optimization is realized, and the method has remarkable effects on policy query, execution feedback and management efficiency.

The application provides a premium collection script extraction and analysis method, device, equipment and medium, text data of premium collection telephone recording is obtained, and the text data is classified to screen out conversation text of no renewal willingness category; each sentence in the conversation text is subject classified to analyze theme missing rate and sequence difference between conversation texts of each seat with different success rates; meaningless sentences are deleted, keywords of each sentence are extracted to analyze word difference of keywords in the same theme in conversation texts of each seat with different success rates, and / or conversation texts of each seat with different success rates under each theme are scored to summarize high-score conversation texts. The application can more easily locate excellent scripts and words in the renewal retention business, thereby improving the success rate of seat telephone renewal.

It makes it easy to use content including text and images as input for chatbot systems that utilize RAG. [Solution] In the extraction device (10), a separation unit (15b) separates content to be processed into sentence objects and image objects and acquires position information for each of the sentence objects and image objects. A sentence extraction unit (15c) extracts text data from the sentence objects. An image extraction unit (15d) extracts text data from the image objects. A generation unit (15e) generates structured sentences including text data arranged in order according to the position information for each of the sentence objects and image objects.

The invention belongs to the technical field of computer information processing, and discloses a file similarity detection method based on Simhash fusion keyword and key sentence extraction, which comprises the following steps of: firstly, carrying out text preprocessing and dual-granularity feature extraction to respectively obtain keywords and key sentences with weights; secondly, providing a weight fusion strategy, and contributing keyword weights to key sentences according to the occurrence frequency to form fusion weights; secondly, optimized SimHash feature coding is carried out, 64-bit fingerprints are independently generated for keywords and key sentences by adopting grouping hash functions based on different seeds, and the 64-bit fingerprints and the key sentences are spliced into a 128-bit final feature fingerprint; and finally, the similarity is judged by calculating the Hamming distance between fingerprints of different files. According to the method, through word and sentence dual-granularity feature fusion and grouped Hash coding, the detection precision and robustness of an algorithm on synonymous replacement, word order adjustment and paragraph recombination are remarkably improved.

The invention discloses an entity spatio-temporal information and topological relation retrieval method based on a spatio-temporal knowledge graph, and the method comprises the steps: constructing a knowledge source database, and carrying out spatio-temporal knowledge graph processing to obtain a spatio-temporal knowledge graph database, the spatio-temporal knowledge graph comprising a time entity, a spatial geographic entity and a triple; the method comprises the following steps of: carrying out semantic partitioning on input query data by utilizing a large language model (LLM), carrying out fact declarative sentence extraction on each block, and extracting a query combination comprising a triple, a time entity and a spatial geographic entity from the fact declarative sentences; and a space-time retrieval planner is utilized to create a multi-path parallel retrieval path matched with the query combination, the multi-path parallel retrieval path comprises triple retrieval, time retrieval and spatial geographic retrieval, and the multi-path parallel retrieval path is utilized to perform multi-path parallel search in the space-time knowledge graph database and summarize and output retrieval results in a layered manner. According to the method, the precision and generalization of complex space-time semantic understanding are improved, and multi-dimensional collaborative retrieval optimization is realized.

This invention relates to a summary reordering method and system combining extractive and generative methods, comprising the following steps: obtaining a first article containing multiple first sentences and multiple original summary sentences; extracting each first sentence to obtain multiple first extracted sentences, the first extracted sentences being iconic sentences in the first article; determining a first summary sentence corresponding to each first extracted sentence based on each first extracted sentence and each original summary sentence, the first summary sentence being a summary predicted based on the first extracted sentence; determining a token corresponding to each first summary sentence based on each first summary sentence, the token being a character or a word; and determining a first target summary based on each token. This application achieves higher accuracy in the generated summaries.

An electronic apparatus for comparing a similarity between texts on the basis of sub-sentence extraction, according to the present disclosure, may comprise: a memory storing at least one instruction; and at least one processor that executes the at least one instruction. The at least one processor may: perform analysis of dependence between a plurality of morphemes included in at least two pieces of text data; generate a dependent relationship graph for the plurality of morphemes, on the basis of the dependence analysis; extract at least one sub-sentence by using the dependent relationship graph; and calculate a similarity between the at least two pieces of text data by cross-comparing the at least one sub-sentence.