9 results about "Coreference" patented technology

The invention discloses an anaphora disambiguation method and system based on big language model enhanced text and structured query language generation. The method comprises the following steps: receiving a natural language question of a user, and analyzing and generating structured table field information through mode information; identifying and rewriting fuzzy time expression in the problem; extracting ambiguous entities, and replacing the ambiguous entities with database standard values through semantic matching and fuzzy matching; synthesizing the information to generate a structured query language statement and executing the structured query language statement; and if the execution result is null, automatically triggering an anaphora disambiguation process, performing error correction and re-matching on field values in the statement through a dual matching mechanism, and generating and executing a corrected query statement. According to the method, the problems of fuzzy anaphora, indefinite time expression, low matching accuracy, resource waste and the like in a traditional SQL system are effectively solved through a strategy of combining pre-processing and post-processing, and the accuracy, robustness and execution efficiency of complex query are remarkably improved.

This invention relates to the field of natural language processing technology and discloses a method for resolving referential inconsistencies across long texts in cross-dialogue contexts. The method includes: segmenting the long text into blocks and vectorizing it to form a queryable vector index set; constructing a hybrid label and semantic index by combining a business entity dictionary and historical weakly supervised signals to generate a candidate entity set; inputting the candidate entity set and block summaries into a dual-channel scoring model, where the first channel uses a zero-shot common-reference classifier to generate a probability score, and the second channel is calibrated based on business rules; weighting and fusing the two to generate the final matching score for candidate entity pairs; constructing a weighted entity graph and applying the maximum spanning tree algorithm to solve for the global preliminary referential chain structure; correcting and filling in conflicting entities appearing in the global preliminary referential chain structure by combining entity type, block summary, and context vector information to form a global referential chain set. This invention has the advantages of improving global consistency and accuracy.

The application discloses a text analysis method and device, a storage medium and a program product, relates to the technical field of artificial intelligence, and comprises the following steps: performing character mention (character proper noun, third person pronoun and character generic phrase) detection on a target text, performing coreference resolution on the detected character mention to determine character mentions that point to the same role; for each quotation in the target text, identifying whether the quotation belongs to dialogue content based on a text sequence containing the quotation and context before and after the quotation, and identifying the speaker and the listener of the quotation when the quotation belongs to the dialogue content; if the quotation belongs to the dialogue content, identifying the first person pronoun and the second person pronoun in the quotation, pointing the identified first person pronoun to the same role as the speaker of the quotation, and pointing the identified second person pronoun to the same role as the listener of the quotation. The application improves the accuracy of role identification.

This invention discloses a method for generating multi-dimensional event profiles across multiple texts. For a given event type, it searches for relevant texts describing that type of event and segments each text into sentence blocks, identifying text blocks such as basic information, event sequence, causes and effects, and comments from various parties. Next, it extracts events from the text blocks describing basic information, obtaining basic elements such as event type, occurrence / end time, location, and actors. Then, it identifies sub-events from the text blocks describing the event sequence and sorts them chronologically to form an event timeline. Finally, it performs coreference resolution on events from different texts to form a complete event profile. This method can correlate and fuse event information distributed across multiple texts, extract complex elements such as causes and effects, and comments from various parties, and can discover sub-events such as the early developments, main processes, and subsequent actions of an event, enabling the analysis of various elements and the evolution of major events.

A coreference resolution method, and a method and apparatus for training a coreference resolution model are provided. The coreference resolution method includes: acquiring a current utterance to be processed; inputting the current utterance into a coreference resolution detection sub-model to obtain a predicted insertion position where there is a semantic absence of the current utterance and / or a predicted deletion position of a word to be replaced; and inputting the predicted insertion position and / or the predicted deletion position of the current utterance, and a historical conversation corresponding to the current utterance into a resolution completion sub-model of the coreference resolution model to obtain a predicted position in the historical utterance of a word corresponding to the semantic absence at the predicted insertion position and / or a predicted position in the historical utterance of a replacement word corresponding to the word to be replaced at the predicted deletion position.

Systems and techniques for linguistically-driven automated text formatting are described herein. Data representing the linguistic structure of input text may be received from Natural Language Processing (NLP) Services, including but not limited to constituents, dependencies, and coreference relationships. A text model of the input text may be built using the linguistic components and relationships. Cascade rules may be applied to the text model to generate a cascaded text data structure. Cascaded data may be displayed on a range of media, including a phone, tablet, laptop, monitor, VR / AR devices. Cascaded data may be presented in dual screen formats to promote more accurate and efficient reading comprehension, greater ease in teaching native and foreign language grammatical structures, and tools for remediation of reading-related disabilities.

The application discloses a kind of reference resolution method and device, it is related to natural semantic processing technical field.The specific embodiment of the method includes obtaining the current sentence containing target pronoun, constructs the first matrix corresponding to current sentence;At least one previous sentence of current sentence is obtained, and the second matrix corresponding to at least one previous sentence is constructed;According to the first matrix and the second matrix, the feature matrix corresponding to current sentence is generated, and the feature matrix is used to represent the correlation degree of each first word in current sentence and second word in previous sentence;Feature matrix is input into semantic segmentation model, and the semantic segmentation result corresponding to feature matrix is obtained;According to semantic segmentation result, determine the reference word corresponding to target pronoun.The embodiment can accurately carry out reference resolution, effectively improve the fluency of man-machine interactive dialogue.

The invention discloses a context anaphora resolution method and system based on dialogue structure perception, and relates to the field of natural language processing, and the method comprises the steps: obtaining multiple rounds of dialogue corpora, storing dialogue entities in a shared memory pool and a private memory pool respectively, clearly distinguishing entities with effective individual cognition and global common-known entities, and obtaining an entity with effective individual cognition and a global common-known entity; therefore, anaphora ambiguity caused by confusion of knowledge ranges of different speakers is eliminated; when anaphora resolution is carried out, through multi-layer structure signal fusion such as a significance recursion updating mechanism driven by a dialogue structure, semantic role consistency judgment and round correlation attenuation modeling, an interpretable cognitive compatibility scoring system is established, and an entity selection mechanism jointly constrained by multi-dimensional information is formed. Therefore, by introducing a dialogue structure perceived double-domain entity memory modeling mechanism, pronoun forepointing accuracy and robustness in complex scenes such as long dialogue, cross-speaker and multi-round interaction are effectively improved.

The present application relates to the field of intelligent dialogue, and relates to a multi-task learning dialogue method supporting anaphora resolution and polysemy, wherein an anaphora resolution model, a polysemy model and a multi-task learning model are trained through training corpus, historical dialogue and current dialogue are spliced into context representation text, anaphors, zero pronouns and anaphora in the text are labeled through the anaphora resolution model, the anaphora probability of the anaphors and the anaphora probability of the zero pronouns and the anaphora are calculated, and whether the anaphora can be resolved is judged according to a set threshold, if the anaphora can be resolved, the anaphors or the zero pronouns are replaced or supplemented into corresponding anaphora to form a replacement text, the intent key components in the replacement text are extracted through the polysemy model, the domain, intent and slot of each intent key component are obtained through the multi-task learning model, and are sent into a dialogue management module to obtain the dialogue action at this moment, so that the intent can be accurately understood.