442 results about "Phrase" patented technology

Techniques for document recommendation based on conversational log for real time assistance are described. A first machine learning module identifies key phrases of a conversational log in real time. The first machine learning module executes multiple machine learning models trained to determine a probability that a portion of a conversation includes a key phrase. A second machine learning module identifies assistance pertaining to the identified key phrases of the conversational log. The second machine learning module executes a machine learning model trained to identify semantic similarity and word matching features of embedding representations of the key phrases and a knowledge base of assistance. The assistance is provided to a user during a conversation in real time.

Systems and methods are described for obtaining accurate responses from large language models (LLMs) and chatbots, including for question and answering, exposition, and summarization. These systems and methods accomplish these objectives via use of noun phrase avoiding processes such as a noun phrase collision detection process, a query splitting process, and a topical splitting process as well as by use of formatted facts, formatted fact model correction interfaces (FF MCIs), bounded-scope deterministic (BSD) neural networks, processes and methods, and intelligent storage and retrieval (ISAR) systems and methods. These systems and methods avoid and bypass noun phrase collisions and correct for errors caused by noun phrase collisions so that hallucinations are eliminated from LLM responses.

A system and method for generating question-answer pairs is disclosed. The system and method can receive a document. A sentence and / or a further sentence in the document may be identified. A syntactic map for the sentence and / or the further sentence may be generated. Noun phrases and prepositional phrases may be identified based on the syntactic map. Sentence level questions may be generated based on phrases identified using natural language processing (NLP) techniques. Document level questions can also be generated based on syntactic maps generated and NLP techniques.

Disclosed herein are apparatus, system, method, and computer-readable medium aspects for extracting themes from textual data. Textual data is initially cleansed from its submitted form into a simplified form for improved accuracy of topic extraction. From the cleansed text, phrases are extracted. Embeddings of the phrases are then determined so that similarities can be identified between different phrases within the text. Using these embodiments, clustering is performed on the embeddings to reveal the topics included within the text submission, as well as their frequency and relationship to one another. This clustering processing can be repeated at multiple levels of granularity for improved accuracy. Based on an analysis of the resulting clusters, a graphical representation of the clusters at the various levels is generated to provide an easy-to-understand indication of the body of text and the topics and themes included therein.

The invention relates to the technical field of natural language processing, in particular to an AI reading management and control method and system based on artificial intelligence, and the method comprises the following steps: carrying out text word segmentation processing on an input original text, segmenting the text into independent sentences, recognizing basic word units in each sentence, analyzing semantic adjacency relationships and syntactic structure features among vocabularies, and carrying out word segmentation processing on the word units; screening and extracting potential phrases representing paragraph meanings, and establishing a candidate semantic unit set; according to the method, through word segmentation, syntactic structure recognition and semantic adjacency analysis of the original text, potential phrases capable of representing paragraph significance are extracted, the candidate semantic unit set is constructed, and modeling of the semantic structure in the text is achieved. And associating the set with the reading fixation duration and the playback action of the user sentence by sentence to obtain a reading behavior response of each semantic fragment, and executing semantic weighting and reading behavior cross analysis according to the reading behavior response. Through the linkage mode, the semantic focus actually focused by the user at present can be recognized.

There is provided an automated system for extracting data from a set of documents corresponding to a vendor, contractor, or product. Documents may be converted to text-based documents and text may be extracted therefrom based on keywords and phrases. Extracted sentences may be mapped to requirements for an assessment. The documents may be processed in batches to determine whether the extracted text is sufficient to perform the assessment. The assessment may be performed and output a score indicating the degree to which the vendor, contractor or product complies with requirements. Processing of documents, as well as processing of evaluations, and processing of multiple assessments, may be performed in parallel.

Electronic discovery using predictive filtering is disclosed herein. An example method includes receiving a plurality of documents. A selection of a filter value is received, where the filter value comprises a field value or a set of field values for the plurality of documents. A prediction of responsive phrases, responsive concepts, or other meta-data is generated. The plurality of documents is evaluated based on a new filter value. Then, a prediction of other responsive phrases or other responsive concepts is generated. A predictive value is generated. Filter criteria is generated based on the predictive value for each of the other responsive phrases, the other predicted responsive concepts, or other predictive meta-data. A selection is then received of the filter criteria. A filter is built and applied based on the selection, and documents are generated from at least a sub-portion of the received plurality of documents.

The invention belongs to the technical field of large model application, and particularly relates to a semantic enhancement text matching method and system based on an achievement transformation large model. The semantic enhancement text matching method based on the achievement transformation large model comprises five parts, namely preparation of data and knowledge, enhancement of multi-level semantic knowledge, learning of semantic difference, intelligent weight distribution of semantic information and prediction of text similarity. The preparation of data and knowledge is used for constructing basic data and knowledge resources required by a text matching task through text preprocessing and knowledge base construction. And enhancement of multi-level semantic knowledge: enriching semantic representation of the text by injecting external knowledge at three levels of word level, phrase level and sentence pattern level. And semantic difference learning: learning fine-grained semantic features by comparing a learning framework, and improving the recognition capability of the model on the text semantic difference. Intelligent weight distribution of semantic information is carried out, and dynamic balance and optimal combination of different source information are realized by evaluating feature importance and dynamically adjusting weights. And predicting the text similarity, calculating the similarity score of the text pair by integrating the feature information of each stage, and providing an interpretable prediction result. The complete text matching method and system are designed based on the achievement transformation large model, the problems of insufficient knowledge utilization, incomplete semantic representation, fixed feature weight and the like of the large model in a text matching task are effectively solved, the text matching effect of the large model in an actual application scene is improved, and the text matching efficiency is improved. And a feasible solution is provided for achievement transformation of a large model technology.

To effectively resolve the issue of inappropriate handovers during communication sessions, methods, systems, and machine-readable mediums which utilize speech features captured by microphones in the original wireless peripheral device and / or the wireless peripheral device to which the communication session is to be handed over to determine if a handover should proceed or should be reversed. Speech features refer to the composite attributes of spoken language that encompass both acoustic and linguistic features. Acoustic features characterize the sound properties of speech and include, but are not limited to, timbre, pitch, intonation, speaking rate, articulation, prosody, melody, spectral features, formant frequencies, and the like. Linguistic features pertain to the actual content conveyed, comprising words, phrases, syntax, and semantics. This includes the analysis of words, phrases, syntax, and semantics to understand the context and continuity of the conversation.

Systems and methods are described for obtaining accurate responses from large language models (LLMs) and chatbots, including for question and answering, exposition, and summarization. These systems and methods accomplish these objectives via use of noun phrase avoiding processes such as a noun phrase collision detection process, a query splitting process, and a topical splitting process as well as by use of formatted facts, formatted fact model correction interfaces (FF MCIs), bounded-scope deterministic (BSD) neural networks, processes and methods, and intelligent storage and retrieval (ISAR) systems and methods. These systems and methods avoid and bypass noun phrase collisions and correct for errors caused by noun phrase collisions so that hallucinations are eliminated from LLM responses.

For semantic data leak prevention, one or more semantic interpretations of an input text are obtained with a generative pre-trained transformer, GPT, configured for interpreting the input text with reference to a target context, and it is determined whether the input text comprises a hidden data leakage by checking the semantic interpretations of the input text with a data leak prevention, DLP, system configured for detecting a data leakage based on a collection of words and phrases of interest. As a result, enabled is a prevention of hidden data leaks when a sensitive information is hidden within a text, for example, by using advanced language techniques.

The invention discloses a question answer determination method and device and electronic equipment. The method comprises the steps that a to-be-retrieved question input by a target object is obtained, a target document associated with the to-be-retrieved question is obtained, and the target document comprises an internal document in an internal database and real-time knowledge in an external knowledge base; determining a first semantic vector and a first keyword corresponding to the to-be-retrieved question, and determining a second semantic vector and a second keyword corresponding to the target document; according to the first semantic vector, the second semantic vector, the first keyword and the second keyword, determining a candidate phrase with the highest association degree with the to-be-retrieved question from the target document; and analyzing the candidate phrases by adopting a generative language model to obtain an answer to the to-be-retrieved question. According to the method and the device, the technical problems of low retrieval efficiency and inaccurate semantic matching in problem retrieval in related technologies are solved.

The invention discloses a large language model reasoning acceleration method and device based on two-stage speculative decoding and a storage medium, and the method comprises the steps: constructing and initializing a Trie tree, and inserting a historical corpus, and phrase sequences in a document library or a code library into the Trie tree one by one; in the reasoning process, longest prefix matching is carried out based on a Trie tree, and a candidate draft sequence is generated by adopting branch backtracking and recursive search; performing confidence evaluation on the candidate draft sequence, calculating a joint confidence score of the sequence through probability multiplication and a Top-K screening mechanism, and judging whether the joint confidence score reaches a confidence threshold; if the accumulated confidence of the candidate sequence reaches a threshold value, skipping a small model generation stage, and directly entering large model verification; otherwise, entering a small model draft completion stage; and the final large model takes the replaced and updated draft sequence as final output. According to the method, adaptive acceleration of the decoding process can be realized, and the long text reasoning delay of the large language model is remarkably reduced while the generation quality is ensured.

In embodiment, a method of displaying an answer of a question-answer pair in response to a natural language search query includes receiving, from a Bidirectional Encoder Representations from Transformers (BERT) model, an array of attention matrices for the question-answer pair, where each attention matrix of the array of attention matrices includes an array of attribution values, generating a total attribution value for each word of an answer of the question-answer pair from the array of attention matrices, and displaying the answer on an electronic display, wherein one or more words of the answer is highlighted based on the total attribution values for each word.

Computer-based generation of information-gathering questions in response to a user query can include parsing the user query using natural language processing and extracting from the user query one or more phrases corresponding to a predetermined category. A knowledge database can be accessed, and entities semantically related to each phrase can be extracted therefrom. A query sub-graph representing a relationship between each of the one or more phrases and the entities extracted from the knowledge database can be generated. An expanded user query can be generated by traversing the query sub-graph. Passages semantically related to the expanded user query can be retrieved from one or more passages databases and ranked. A neural question generator can generate a set of information-gathering questions based on the expanded user query and a group of select passages selected from the plurality of passages in accordance with each selected passage's ranking.

A server generates a user interface for allowing a user to rewrite portions of text for an electronic document to mitigate detected issues. For an input document, the server generates one or more indications over the one or more phrases in the sentence. An indication for a phrase may be generated based on a respective category associated with the phrase. Responsive to receiving an indication from the user to rewrite the sentence, the server generates a prompt to a machine-learned language model. The server receives a response generated by executing the machine-learned language model on the prompt. The server generates a pane user element to present the candidate sentence and an evaluation of the candidate sentence to the user, and responsive to receiving a selection of a candidate sentence, replacing the sentence in the editor with the selected sentence on the user interface.

The invention provides a retrieval method and device based on graph structure retrieval enhancement, electronic equipment and a storage medium, and the method comprises the steps: obtaining original data, screening the original data based on a preset screening rule to obtain a plurality of candidate phrases, calculating the similarity between the plurality of candidate phrases and a preset central semantic vector of each layer, the multiple candidate phrases are layered according to the similarity, feature vectors are constructed, levels and feature vectors corresponding to the multiple candidate phrases are obtained, graph entities are obtained based on the candidate phrases, context fragments of the graph entities in original data are extracted, and relation types between the graph entities are obtained based on the context fragments; according to the relationship type between the graph entities and the hierarchy of the graph entities, a multi-hierarchy knowledge graph is obtained, and retrieval is performed based on the multi-hierarchy knowledge graph and the corresponding associated information block, so that the technical problems of result redundancy and logic fracture of a retrieval enhancement generation method are solved through the method.

The invention relates to the technical field of data identification, in particular to a data security identification method covering physics, semantics and sensitive points, and the method comprises the following steps: processing an obtained first industry review document set and a second industry review document set to generate an industry semantic feature lexicon; performing semantic analysis on each sensitive point information sentence to generate a sensitive point information phrase sequence, taking the sensitive point information phrase sequence as input, performing training through a professional field document to generate a word vector model, and performing similarity calculation on the word vector model to generate a sensitive feature lexicon; based on the industry semantic feature lexicon and a word segmentation tool, keyword segmentation is carried out on the target document, and a semantic security identifier is generated; and performing word segmentation on a target sentence in the target document based on the inactive word bank and the industry semantic feature word bank, and generating a sensitive security identifier in combination with the sensitive feature word bank. By applying the method, effective identification can still be realized under the condition of data adjustment, and semantic representation of the body is enhanced.

A method for extracting key phrases based on extracting both informative and readable phrases from documents. The method includes separating documents into sentences to extract sentence-level phrases and separating the documents into segments of a fixed length to extract segment-level phrases. The method further includes using a PMI and entropy model to also extract readable phrases from the documents. The method includes comparing the sentence-level phrases, the segment-level phrases, and the readable phrases to generate key phrases for the documents.

A computer-implemented process is programmed to detect a type or category of document that is being drafted and to suggest one or more phrases or sentences to add to the original and / or substitute for the original, the suggested text being potentially more personable and sincere than the writer's original text. Suggested text phrases are selected from a large corpus of previously manually drafted sentences and phrases. Selected text phrases are ranked and filtered to result in suggesting a manageable set of text phrases. With this approach, adding specially chosen content to existing content can change the warmth or tone of the text while preserving its meaning. Unlike prior approaches, in an embodiment, the process is programmed to artificially understand the intent of the original text as a basis of suggesting other content to add. Furthermore, embodiments may interoperate with a visual or graphical user interface that is programmed to enable users to see what the change to the text will be and whether they want it before they engage with the suggestion.

The invention discloses a teaching aid book intelligent correction and editing system, which comprises a knowledge graph construction module used for collecting authoritative teaching materials, academic literatures, teaching programs and other data, constructing a knowledge graph covering knowledge points of each subject by applying a natural language processing technology and a knowledge extraction algorithm, and establishing an association relationship among the knowledge points, and a text analysis module used for analyzing the association relationship among the knowledge points. An input teaching aid book text can be split into sentences and paragraphs, the technologies of part-of-speech tagging, syntactic analysis and the like are utilized, information such as keywords and key phrases is extracted, the grammatical structure and semantic information of the text are analyzed, and the intelligent proofreading module is used for conducting intelligent proofreading on the basis of a knowledge graph constructed by the knowledge graph construction module and an analysis result of the text analysis module. The knowledge points in the text are compared with the standard knowledge points in the knowledge graph, omissions possibly caused by manual correction and editing are effectively avoided, the correction and editing quality is improved, the workload of manual correction and editing is greatly reduced, and the correction and editing speed is increased.

Disclosed herein are system, method, and computer program product aspects for textual data extraction, generation, and evaluation. Text is input into a first fine-tuned large language model (LLM) to generate an atom (e.g., a textual phrase in a particular category). The atom is input into a second LLM that has been fine-tuned for structured output corresponding to the particular category of information. A logical structure is generated based on a structured output of the second LLM, wherein the logical structure represents the textual phrase of the atom and contains a contextual attribute associated with the textual phrase. The embodiment then stores the logical structure into a knowledge graph as a modifier node having a time-variant attribute (e.g., a timestamp associated with the textual phrase and / or the contextual attribute).

Disclosed are systems, apparatuses, processes, and computer-readable media for processing one or more images. For example, an apparatus comprising one or more processors and configured to: receive a natural language response from a first machine-learning model; segment the natural language response into a set of phrases; classify each phrase in the set of phrases based on at least one corresponding phrase in at least one ground truth response; remove a first subset of phrases from the set of phrases based on respective classifications of the first subset of phrases, wherein the first subset of phrases are not verified in the at least one ground truth response; and compute a metric associated with the first machine-learning model based on respective classifications of a second subset of phrases from the set of phrases, wherein the second subset of phrases are verified in the at least one ground truth response.

A system and method of identifying occurrence of a semantic variation of a phrase in a passage by at least one processor may include calculating a phrase embedding vector, representing a semantic meaning of the phrase; extracting, from a textual representation of the passage, at least one hierarchical set of nested sequences of words; for each sequence, calculating a corresponding sequence embedding vector, representing a semantic meaning of the sequence; for one or more sequence embedding vectors, calculating a corresponding vector similarity value, representing similarity of the sequence embedding vectors to the phrase embedding vector, identifying a sequence corresponding to a maximal vector similarity value of the one or more vector similarity values; and determining the identified sequence as a semantic variation of the phrase, based on the maximal vector similarity value.

The invention discloses a text dialogue method and device suitable for multiple languages, terminal equipment and a storage medium, after a source text input by a user in a current dialogue is obtained, a target language corresponding to the source text can be accurately recognized based on a phrase structure and a sentence structure of the source text, and therefore the user can select the target language based on the recognized target language. And a corresponding grammar rule can be selected to generate a subsequent reply text. In addition, the user intention and context information can be deeply understood based on the extracted local features, global features and dialogue features, so that the reply text conforming to the language expected by the user is generated, and the generated reply text is correct in grammar based on the language of the current input text and the grammar rule of the language. And the expression is natural and smooth, the stiff or incoherent condition is avoided, the accuracy and naturalness of the reply text in the dialogue process are improved, the multilingual text dialogue is realized, and the dialogue experience of the user is optimized.

A tailored interactive language learning system that teaches an individualized set of vocabulary words to users through interactive avatars and stories. The interaction is modeled through probabilistic rules in a semantic network and neural network having objects and relations. Dialog and narration is generated dynamically based on the state of the interactive story model using phrasal rewrite rules and neural network implementiung a four-valued logic system in which truth values of the objects and relations are encoded as true, false, defined, and undefined in a single memory array.

A method and a server for generating a training set of data for training a translation machine-learning model are provided. The method comprises: receiving: (i) a source language corpus of texts in the source language; and (ii) a corresponding target language corpus of texts in the target language; generating a first variant of translation of a given source language phrase from the source language corpus into the target language; and a first confidence score thereof; generating a second variant of translation of the given source language phrase into the target language; and a second confidence score thereof; and in response to one of the first and second confidence scores being greater than a base confidence score associated with a respective target language phrase from the corresponding target language corpus, replacing the respective target language phrase with a respective one of the first and second variants of translation.

A method including applying a language model to datasets to generate topics assigned to the datasets. Each of the topics includes at least one of a natural language text word and a natural language phrase. The method also includes applying an encoding model to the topics to generate a corresponding vector data structures storing embedded topics. Each embedded topic of the embedded topics is associated with one corresponding vector in the vector data structures. The method also includes applying a clustering model to the vector data structures to generate a cluster including a subset of the vector data structures. The subset includes a reduced number of the vector data structures. The method also includes modifying, according to the cluster, the datasets.

Method, software, and apparatus for improved Augmentative and Alternative Communication (AAC) solutions. In one aspect, a user interface is provided with a set of suggestions comprising text, phrases, etc., and navigation buttons that enable users to select words and phrases to add to be written and / or spoken in a manner that reduces the number of user inputs. The suggestions are displayed in alphabetical order in rows with navigation buttons adjacent to the rows, with activation of a navigation button resulting in generation of updated suggestions having alphabetical ranges that are bounded by suggestions in associated rows. This approach may be combined with predictive text means to enable users to easily formulate text and / or speech content.

Systems and methods for natural language processing are described. Embodiments of the present disclosure receive an input phrase including an aspect term; generate a complement phrase based on the input phrase using a language generator model, wherein the complement phrase includes different words than the input phrase; combine a representation of the input phrase and a representation of the complement phrase to obtain an augmented representation of the input phrase; and generate sentiment information corresponding to the aspect term based on the augmented representation.