52 results about "Proper noun" patented technology

The invention relates to the technical field of infrastructure operation and maintenance, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a code review method, device and equipment based on knowledge base assistance and a medium. Constructing a domain knowledge base based on domain proper nouns and code entity naming specifications; constructing an architecture knowledge base based on the code framework specification; obtaining a to-be-reviewed code change; component calling, naming specifications and structure specifications in the to-be-examined code change are examined, and corresponding examination results are generated; and generating a code review report according to the check result. By constructing the component knowledge base, the field knowledge base and the architecture knowledge base, multi-dimensional joint inspection of code changes from component calling and naming specifications to architecture specifications is achieved, the automation level of code inspection can be improved, manual dependence is reduced, and code quality and normalization are enhanced.

The invention discloses a proper noun speech recognition processing method based on instant messaging and a computer device. The method comprises the following steps: firstly, constructing annotation data sets of different scenes and a user-specific custom word list, and dynamically obtaining related data and a hot word list according to a to-be-recognized voice scene; afterwards, a training voice recognition model is subjected to fine tuning by using the annotation data set to obtain a first model, and after a recognition instruction is received, a hot word list is loaded for recognition to obtain a voice initial recognition text; and after the initial recognition text is obtained, dynamic optimization is carried out by utilizing a user-specific custom word list, and recognition errors are corrected. According to the method, matching data can be dynamically obtained, the model is combined with a scene to understand proper nouns, and the recognition difficulty caused by pronunciation and meaning complexity is reduced; in combination with targeted data and hot word information training, the proper noun recognition accuracy is improved, the defects of an existing model error correction mechanism are overcome, the accuracy of a final recognition result is remarkably improved, and powerful support is provided for speech recognition and subsequent application.

The invention provides a context adaptive speech recognition method integrating entity replication and vector retrieval, and belongs to the technical field of artificial intelligence. The method comprises the following steps: converting input voice into acoustic feature representation; converting the external entity dictionary into vector representation; screening out candidate entities through an index technology, and determining to generate output or copy entities from a standard word list according to a matching degree calculated by an attention mechanism; jointly optimizing the model by comprehensively optimizing the objective function, wherein acoustic modeling, language modeling, entity copying accuracy and entity distinguishing capability are included; continuously updating the index during training; in reasoning, in combination with beam search and confidence threshold mechanisms, selection is made between generation of outputs from a standard word list and copying of entities. In the aspect of recognition performance, a dynamic copy-generation fusion mechanism and a contrast learning strategy are adopted in the method, the accuracy of named entity recognition is effectively improved, particularly, the method is excellent in performance when proper nouns such as personal names and place names are processed, and meanwhile the distinguishing capacity for entities similar in pronunciation is enhanced.

The embodiment of the invention discloses a wrongly written character processing method, device and equipment, and the method comprises the steps: receiving text data of a target domain input by a user, the text data comprising special words of the target domain; performing word segmentation processing on the text data through a preset word segmentation strategy aiming at a target field, and performing identification processing aiming at special words of the target field on segmented words corresponding to the obtained text data to obtain target special words in the segmented words corresponding to the text data; carrying out wrongly written character recognition processing on the target proprietary word through a proprietary word error recognition model for the target field to obtain a first recognition result, and carrying out wrongly written character recognition processing on segmented words, except the target proprietary word, in the segmented words corresponding to the text data through a general word error recognition model to obtain a second recognition result; and based on the first recognition result and the second recognition result, determining wrongly written character information contained in the text data through fusion processing of the two different recognition results.

The present disclosure provides a text translation method, a server, a storage medium, and a program product. The method relates to the field of artificial intelligence, and comprises: acquiring an original text to be translated and a target language; searching a translation database for a translation text pair matching the original text, the translation text pair comprising a source language text and a target language text; and inputting the original text, the target language, and the translation text pair into a text translation model, translating, by means of the text translation model, a source language text appearing in the original text into a corresponding target language text, and generating a translated text in the target language corresponding to the original text. By performing external knowledge intervention on a text translation model on the basis of a pre-customized translation database, incorrect translations of custom vocabularies are effectively avoided without needing to retrain parameters of the text translation model, and the accuracy of translation of the text translation model for custom vocabularies such as proper nouns, terminology, and new terms can be improved, thereby improving the accuracy of text translation results.

Various embodiments contemplate systems and methods for performing automatic speech recognition (ASR) and natural language understanding (NLU) that enable high accuracy recognition and understanding of freely spoken utterances which may contain proper names and similar entities. The proper name entities may contain or be comprised wholly of words that are not present in the vocabularies of these systems as normally constituted. Recognition of the other words in the utterances in question, e.g. words that are not part of the proper name entities, may occur at regular, high recognition accuracy. Various embodiments provide as output not only accurately transcribed running text of the complete utterance, but also a symbolic representation of the meaning of the input, including appropriate symbolic representations of proper name entities, adequate to allow a computer system to respond appropriately to the spoken request without further analysis of the user's input.

The invention discloses a railway signal fault text enhancement method, system and device based on improved EDA, and the method comprises the following steps: constructing a railway field entity dictionary, carrying out the proper noun recognition of a preprocessed to-be-enhanced railway signal fault text, and outputting a structured text; on the basis of a railway signal fault related text data set, a similar word bank of railway signal fault text related vocabularies is generated by adopting a Word2Vec model and used for replacing non-professional vocabularies in railway signal fault text statements, and enhancement operation is performed on the basis of a structured text and the similar word bank of the railway signal fault text related vocabularies by adopting an EDA improvement method. Enhancing the railway signal fault text to be enhanced; and screening the enhanced railway signal fault text. According to the method, the integrity and accuracy of domain terms are ensured through an entity fixing mechanism, an enhanced sample conforming to a real scene is generated in combination with a semantic constraint editing strategy, and the problem of unbalanced data distribution is effectively solved.

The invention provides a translation method and a translation system. According to the translation method, firstly, a predefined text knowledge base is adopted to preprocess text segments needing to be precisely processed in a to-be-translated text, so that a prompt of an AI large language model contains a consistent text comparison table customized for the to-be-translated text; wherein fixed translation of specific text segments (such as proper nouns, phrases or sentences) in a to-be-translated text in a target language is defined; and translating the preprocessed text by adopting the AI large language model. The long text such as the novel can be segmented into chapters and sections by adopting an automatic text segmentation technology before translation, and the translated texts of all the chapters and sections are recombined into a target file. According to the technical scheme, the automatic text segmentation technology and a cooperation mechanism of text consistency preprocessing and comparison table guiding type Prompt guidance are combined, the problems of key expression inconsistency, semantic drift and the like occurring in long text translation are effectively avoided, and the continuity and readability of translated texts are improved.

The invention provides a speech translation method, a server, a storage medium and a program product. The method relates to the field of artificial intelligence. The method comprises the following steps: acquiring original voice of a to-be-translated original language and a to-be-translated target language; and searching translation knowledge matched with the original voice in a translation knowledge base, inputting the original voice and the translation knowledge matched with the original voice into a voice translation model, translating the original language voice in the translation knowledge appearing in the original voice into a corresponding target language text through the voice translation model, and generating a translation text of the target language. On the basis of a cross-modal translation knowledge base, external knowledge intervention is carried out on the speech translation model, translation errors of customized vocabularies are effectively avoided on the premise that parameters of the speech translation model do not need to be retrained, the translation accuracy of the speech translation model to customized vocabularies such as proper nouns, terminologies and new words is improved, and the translation efficiency is improved. Therefore, the accuracy of the speech translation result is improved.

The present application relates to the field of place name translation technology and discloses a place name translation method that combines multilingual syllable segmentation with adaptive learning. The method performs word segmentation and common name translation annotation on the source language place name address based on a general dictionary, converts the unannotated proper noun portion into an IPA phoneme sequence, and then performs syllable segmentation and translation matching on the IPA phoneme sequence of each word segment. Multiple possible candidate transliteration results are matched for each word segment. The candidate transliteration results of the proper noun segment are then combined with the common name translation result to construct a candidate set of place name and address target language translation results. The candidate set is then adaptively learned and aggregated using a deep learning algorithm to comprehensively consider the semantic information and contextual coherence of multiple transliteration versions to generate the final place name and address target language translation. The present application can solve the problems of inaccurate, stiff, and ambiguous transliterations in traditional place name and address translation methods, thereby improving translation efficiency and quality.

This invention relates to a prompt-based machine translation method, belonging to the field of natural language processing technology. It solves the problems of inaccurate, omitted, and mistranslated nouns and proper nouns in existing machine translation models. By constructing a set of nouns and their translations from the text to be translated, the input text and adjustment matrix of the translation model are obtained. The translation model is then used to translate the input text, and the attention calculation of the model is adjusted using the adjustment matrix M, ultimately outputting the translation. Based on the input data containing noun translation prompts and the adjustment matrix, the accuracy of the noun translation by the translation model is guaranteed to a certain extent, solving the problems of omitted and mistranslated nouns, and improving the accuracy of noun translation in machine translation models.

The invention discloses an electric power material supply chain material semantic retrieval method based on large language model seed problem expansion, and provides a seed problem text expansion technology in an electric power material supply chain material low-resource scene by utilizing semantic understanding and text generation capability of a large language model. Intelligent rewriting and expansion are performed by using a very small amount of seed problems, so that the problem of difficulty in semantic retrieval of complex proper nouns and domain terms is solved, and the adaptability of the system to specific fields such as the power industry is enhanced; according to the method, the text training data is expanded, manual intervention and manual labeling are avoided, and the automation level of the system is improved.

The system according to the embodiment comprises a reception unit, an analysis unit, a suggestion unit, and a selection unit. The reception unit is configured to receive a user input. The analysis unit is configured to analyze context based on the input received by the reception unit. The suggestion unit is configured to suggest proper nouns based on the context analyzed by the analysis unit. The selection unit is configured to allow the user to select the proper noun suggested by the suggestion unit.

The invention discloses a polyphone disambiguation method based on a pointer network and context adaptive modeling, and relates to the technical field of natural language processing and intelligent text processing. According to the method, context range labels and category labels associated with polyphones are predicted at the same time through a pointer network, so that self-adaptive recognition of context granularity required by decision making is achieved. According to different category labels, a local disambiguation process, a global disambiguation process and a proper noun processing process are respectively constructed according to the method: the local process realizes pronunciation judgment of adjacent word levels based on dictionary retrieval and semantic similar word acquisition; the global process is combined with a large language model and a hierarchical semantic dictionary to realize sentence-level semantic-driven pronunciation generation; and the name and place name categories are deduced by directly utilizing entity knowledge of the language model. In addition, the invention further provides a paraphrase enhancement module oriented to an ancient language scene, and through combined modeling of comparative learning and example learning of sentences of the same pronunciation, the model can stably distinguish different pronunciations in an ancient language text and generate corresponding paraphrases. According to the method, the optimal disambiguation path can be automatically selected according to the context dependence characteristics of different polyphones, the accuracy and generalization ability of polyphone pronunciation prediction are effectively improved, and the method is suitable for various text scenes such as modern Chinese and ancient Chinese.

The invention discloses a voice recognition method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, and the main technical scheme comprises the steps: obtaining a to-be-recognized audio, and obtaining a first proper noun in a first recognition result of the recognized audio; searching at least one second proper noun associated with the first proper noun in a pre-established proper noun library; and updating the searched at least one second proper noun in a preset hot word set. Compared with the prior art, the preset hot word set is actively updated by updating the at least one second proper noun in the preset hot word set, the identified audio and the to-be-identified audio have relevance, so that the at least one second proper noun also has relevance with the identification result of the to-be-identified audio, and the identification accuracy of the to-be-identified audio is improved. The effect that the updated preset hot word set has the hot word enhancement effect on the recognition result of the to-be-recognized audio is achieved, and then the speech recognition accuracy is improved.

A sentence processing device according to the present disclosure comprises: a mask means for masking a proper noun included in a text sentence; a prediction means for predicting, by using a pre-trained model for natural language processing of the text sentence, a word to be pseudonymized into a masked portion; a replacement means for replacing the proper noun with the predicted word; an execution means for executing, by using a specialized-type trained model obtained by specializing the pre-trained model for a specific task, natural language processing of the text sentence in which the proper noun has been replaced; a restoration means for restoring, to the original proper noun, the word that has been replaced according to the processing result; and an output means for causing the restored processing result to be outputted.

The invention discloses a document-level machine translation method based on multi-granularity knowledge enhancement and a related device, and relates to the technical field of document-level machine translation.The method comprises the steps that a to-be-translated source document using a source language is segmented to obtain a plurality of sub-documents, multi-granularity knowledge is generated through a large language model, and the multi-granularity knowledge is obtained; comprising global knowledge including a global source language abstract, a global target language abstract, a global proper noun set and global topic description, and local knowledge including a core topic and a transition prompt of each sub-document, and translating each sub-document by using a large language model under multi-granularity knowledge enhancement, and performing sentence alignment on the sub-documents and the translated sub-documents, and if the sentence alignment succeeds, removing overlapped sentences in each translated sub-document by using a large language model and then performing splicing to obtain a translated target document using a target language. The translation quality can be improved.

The application relates to an artificial intelligence technology and discloses a question expansion method and device, equipment and a storage medium. The method comprises the following steps: extracting an inquiry mode word and an entity noun in a to-be-expanded question; extracting a standard question containing the inquiry mode word and a synonymous question with the same meaning as the standard question in a question and answer library, and marking the inquiry mode word and the inquiry mode word contained in the synonymous question as a keyword; performing word segmentation and part-of-speech tagging on the standard question and the synonymous question to obtain a sentence structure; extracting a front adjacent substantive word and a rear adjacent substantive word of the keyword in the standard question and the synonymous question according to the sentence structure; extracting the inquiry mode word in the keyword, and extracting the front adjacent substantive word and the rear adjacent substantive word; and according to the keyword, the front adjacent substantive word, the rear adjacent substantive word and the proper noun, composing a preset number of expansion questions in a preset grammar format. The application can automatically generate expansion questions according to an input question.

The invention relates to the technical field of artificial intelligence, in particular to a dynamic dictionary generation and hot update method and system based on conference contexts.In the method, conference context information is automatically obtained, the context information is preprocessed, domain terms related to a conference theme are obtained through domain term extraction; the domain terms and the selectable corresponding weights are constructed into a structured session-level dynamic dictionary, the dynamic dictionary is loaded to an ASR engine at the beginning of a conference or in the real-time transcription process, and recognition and transcription are conducted on the conference real-time voice stream based on the ASR engine subjected to hot updating. By applying the method, efficient and accurate identification of proper nouns and domain terms in a specific conference scene can be realized, the problem of low accuracy is solved, and personalized identification optimization of each conference is realized.

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.

The invention discloses an intelligent text error correction and term extraction system based on multi-model fusion, and belongs to the technical field of natural language processing. The technical problems that multi-source text coding compatibility is poor, professional term recognition accuracy is low, and error correction quality and efficiency are difficult to balance are solved. According to the technical principle, text standardization is achieved through multi-code compatible preprocessing; a proper noun library containing standard terms, error variants and semantic description is constructed, and millisecond-level retrieval is achieved through vectorization indexing; dynamically injecting a context by adopting an RAG enhancement mechanism; and performing dual optimization by weighting and fusing output results of the three pre-training models and combining model output consistency constraint and performance boundary control. And efficient and accurate text processing is realized through multi-module cooperation and multi-model fusion.

The invention discloses a part positioning method, system and equipment based on large model keyword reasoning, which is characterized in that thinking chain reasoning is changed into reasoning based on keywords in medical and physical examination processes, and the method is mainly used for building training data. The construction of the training data comprises the following processes: constructing a proper noun library, forming a keyword reasoning process and splicing a result; wherein the construction of the proper noun library is that proper nouns are classified and stored according to region names, part names and pathological names according to priori knowledge; the keyword reasoning forming process is based on diagnosis of proper nouns related to recall semantics, and the result and the proper nouns are embedded into cue words to enable the large model to form a keyword-based reasoning process; the splicing result is obtained by splicing the reasoning process based on the keyword to form training data. By means of the method, training data can be verified more easily, reasoning and training cost is lower, and the training process is more stable; and part positioning is more accurate and efficient.

The application provides a question answering method and device based on a proper noun, which can be used in the financial field or other technical fields. The method comprises the following steps: receiving an inquiry request sent by a client, wherein the inquiry request comprises a consultation question; if it is determined that the consultation question comprises a proper noun based on a proper noun list, querying corresponding related information according to the proper noun comprised in the consultation question as reply information of the consultation question; wherein the proper noun list is obtained in advance; and returning the reply information of the consultation question to the client. The device is used for executing the above method. The question answering method and device based on a proper noun provided in the embodiment of the application improve the accuracy of the reply to the consultation question.

1. Name of the product in this design: Original audio translation and subtitle editing module for a video creation graphical user interface of an electronic device. 2. Application of this design: This design is intended for use in an electronic device. 3. The key design features of this product are the graphical user interface portion for which protection is sought. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Uses of the graphical user interface: Overall use: video creation interaction; Local use: enabling users to edit AI-translated subtitles and translated subtitles, generate and preview AI dubbing results, and add proper nouns and translations through changes in user operations. 6. Other situations requiring explanation are illustrated in the following figures: This design protects the original audio translation subtitle editing module; as shown in the main view, the left side is the text editing area, and the right side is the video preview area, supporting operations such as original audio subtitle translation editing, generating dubbing, and previewing; clicking the "Proper Nouns" option will bring up a pop-up window, as shown in the changing state diagram, where commonly used nouns and their corresponding translations can be entered, modified, added, or deleted; the usage state reference diagram shows the effect of displaying this user interface, and the main view and changing state diagram correspond to usage state reference diagrams 1-2 respectively.

This invention discloses a hierarchical collaborative method and system for secure reasoning using a large language model, belonging to the field of secure reasoning technology for large models. The method includes: on the client side, firstly, named entity recognition is performed on the original document, and sensitive proper nouns are replaced with secure generalized descriptors based on entity type and differential privacy mechanisms, generating an intermediate document and entity type metadata; subsequently, the intermediate document undergoes lexical-level differential privacy semantic perturbation to generate a perturbed document; the perturbed document is sent to a cloud-based large language model to obtain the initial generated text; finally, on the client side, a local lightweight model is used to generate high-quality final text based on entity type metadata, context, and the initial text returned from the cloud. This invention, through a hierarchical collaborative privacy protection framework and under provable differential privacy protection, solves the problem of balancing proper noun protection and generation quality, and is suitable for secure reasoning scenarios involving sensitive texts in finance, healthcare, and other fields.

The invention discloses a domain knowledge response result generation method and device, a medium and a terminal, relates to the technical field of artificial intelligence, the financial field and the medical field, and mainly aims to improve the problems that the response time delay is increased due to huge information amount of domain knowledge and the response time delay is increased due to high similarity of related proper nouns of the domain knowledge in the prior art. The problem that the accuracy of response results is reduced due to the fact that characters are similar and semantics are different in the prior art is solved. Comprising the following steps: firstly, matching a plurality of domain knowledge vectors from a database according to similarity scores with question vectors, then reordering and splicing a plurality of text blocks according to semantic similarities between the text blocks of the matched domain knowledge vectors and question texts, and finally, calling a large model according to the question texts and spliced paragraphs, so as to obtain the question texts. And generating a response result.

This application proposes a task processing method, apparatus, electronic device, chip, and storage medium, relating to the field of artificial intelligence. The method includes: hierarchically marking the long text based on its text type to obtain multi-level hierarchical marking information; generating first contextual information based on the multi-level hierarchical marking information; the first contextual information indicating the text structure and logical relationships of the long text; splitting the long text based on the first contextual information to obtain at least one sub-text; performing a processing task on the sub-text according to the multi-level hierarchical marking information and the first contextual information to obtain the processing result of the sub-text; and fusing the processing results of each sub-text to generate the processing result of the processing task. This not only avoids problems such as referential breaks, logical misalignments, or proper noun fragmentation caused by forced truncation, but also enhances the understanding of cross-paragraph dependencies and global semantic structure.

The invention belongs to the technical field of document identification, and particularly relates to an intelligent document identification method and computer equipment. When a body text is processed, after a text detection model is used for recognizing the content of the body text, the step of semantic disambiguation processing is added, specifically, polysemy words and / or proper nouns in the content of the body text are / is recognized firstly, then the most possible word meanings of the polysemy words and / or proper nouns are determined through a parameter estimation method, and the word meanings of the polysemy words and / or proper nouns are determined. Further determining the word meaning of the polysemous word and / or the proper noun by using the trained neural network after a better result cannot be matched by the parameter estimation method (that is, the probability, estimated by the parameter estimation method, of the polysemous word and / or the proper noun belonging to the most probable word meaning is lower than a set probability threshold value); according to the method, the understanding deviation of polysemy words in different contexts is eliminated, the clear meaning of proper nouns is clear, analysis and extraction of key information of the document are achieved, a user can understand the obtained knowledge conveniently, and the document recognition effect is improved.

The present application relates to the field of intelligent translation technology, and specifically discloses a method for intelligent place name and address translation based on multilingual syllable segmentation, which introduces a deep learning algorithm to make adaptive translation strategy decisions for each vocabulary unit in the source language place name and address text, so as to automatically distinguish between proper nouns that should be transliterated and common vocabulary that should be translated using a dictionary, and performs fine-grained syllable-level segmentation and target language syllable mapping on proper nouns marked as transliterated to achieve accurate transliteration of proper nouns; at the same time, for common vocabulary marked as dictionary translation, a dictionary matching algorithm is used for accurate translation, and then the transliteration results of proper nouns and the translation results of common vocabulary are standardized and reorganized to obtain the place name and address translation results. The present application can effectively improve the translation effect of proper place names when mixed with common words, and enhance the translation accuracy and flexibility of place names and addresses in a multilingual environment.

Embodiments of the present application provide a kind of identification method of procurement category, computer equipment and storage medium, the method comprises: obtaining first procurement demand text, and procurement proper noun set;Based on the first procurement demand text, the corresponding stop word set is obtained;Based on the procurement proper noun set and the stop word set, the first procurement demand text is carried out word segmentation operation, and the first keyword set is obtained;The first keyword set is vectorized representation, and the first keyword set after vectorization is input into the target category identification model pre-trained, and the category identification result is obtained.The embodiments of the present application aim to identify procurement demand text based on target category identification model, to obtain the identification result with higher accuracy.Especially for the category identification of medical consumables in the procurement process, the category identification efficiency of medical consumables and the accuracy of category identification result can be improved.