12 results about "Single sentence" patented technology

The present disclosure provides a natural language processing apparatus and a natural language processing method that may determine whether a user's speech command is a compound sentence or a complex sentence based on an output of a natural language understanding module, and when the user's speech command is a compound sentence or a complex sentence, recursively call the natural language understanding module, thereby executing all of the plurality of functions expressed as a single sentence.

This application discloses a corpus processing method and system for a large-scale model search engine, relating to the field of data processing technology. The corpus processing method for a large-scale model search engine includes: obtaining a target word segmentation result based on a first sentence; obtaining the semantic contribution degree corresponding to each word element based on the target word segmentation result; filtering out word elements in the target word segmentation result whose semantic contribution degree is lower than a first preset value to obtain an optimized word segmentation result; obtaining the priority of the first sentence based on the optimized word segmentation result; and determining whether to input the first sentence as valid corpus into the large-scale model based on the priority. This application overcomes the limitations of traditional word segmentation and filtering, accurately identifying high-quality, low-frequency corpus with reliable sources and core semantics, preventing it from being overwhelmed by high-frequency, low-quality information, and simultaneously eliminating false, low-quality corpus based on keyword stuffing from the source, thereby improving the quality of the input corpus for the large-scale model.

The invention discloses a multi-subject semantic attribute binding image generation method based on a diffusion model. The method comprises the steps of improving independent coding of a single sentence into segmentation of the sentence into a plurality of sub-sentences and independent coding of the sub-sentences; improving a denoising process and improving a cross attention mechanism; clear semantic representation is obtained by independently coding clauses, a denoising process is divided into a reconstruction branch and a generation branch, intermediate potential representation is spliced and fused to improve consistency and visual quality, and meanwhile, independent attention guidance is realized by combining an improved cross attention mechanism and mask division, so that feature aliasing is avoided, and the accuracy and the reliability of the system are improved. And the local precision and the space decoupling capability are enhanced. According to the multi-entity semantic modeling and image-text alignment method, a finer and more stable solution is provided while high efficiency and universality are ensured, and the potential in multi-entity semantic modeling and image-text alignment is shown.

The application provides a false keyword detection method, device, storage medium and program product. The method comprises: based on a semantic boundary, performing splitting processing on a to-be-detected text to obtain a plurality of sentence units; performing feature extraction processing on each sentence unit to obtain a single sentence feature vector corresponding to each sentence unit; based on the single sentence feature vector corresponding to each sentence unit, determining a sentence sequence level hidden feature corresponding to each sentence unit and a full-text global feature of the to-be-detected text; determining a contradiction degree feature vector corresponding to each sentence unit according to the sentence sequence level hidden feature of each sentence unit; and determining a false keyword corresponding to the to-be-detected text according to the full-text global feature of the to-be-detected text and the contradiction degree feature vector corresponding to each sentence unit. The method improves the reliability of long text false keyword detection.

This application discloses a data processing method, apparatus, device, storage medium, and program product, relating to the field of artificial intelligence technology. The data processing method includes acquiring service dialogue data between customer service representatives and users, wherein the service dialogue data includes at least two consecutive messages from the same role in both the customer service representative and the user role; inputting the service dialogue data and a first prompt word into a first large language model to obtain a standard dialogue corpus output by the first large language model; using the first prompt word to constrain the first large language model to semantically integrate the at least two consecutive messages from the same role, forming a single-sentence dialogue text of alternating question-and-answer between the two roles; and constructing a training dialogue corpus based on the results of business topic verification and semantic structure verification in the standard dialogue corpus. This enables the rapid construction of a training dialogue corpus, solving the problem of low training efficiency.

The application provides a fake news detection method and related equipment, and belongs to the technical field of natural language processing and information authenticity detection. The method comprises the following steps: preprocessing and segmenting an original input text to obtain a sentence set and score a candidate claim set obtained; converting the sentence into a single sentence declarative expression to obtain a normalized claim, and structurally constructing the normalized claim; simultaneously performing information retrieval according to the structural construction mode to obtain a candidate evidence, performing multidimensional consistency verification on the structured claim and the candidate evidence, and scoring to obtain a total verification score; and correcting the total verification score by using the matching degree of a news site and an IP address, and outputting a verification result. The application prepositions noise processing and fact positioning, adopts a structured claim for fine-grained verification, and corrects through cross-information source matching of an IP address and a news site, so that the accuracy, interpretability and identification ability for numerical / time tampering of fake news detection are improved.

Embodiments of the present specification provide a question sentence generation method and device, wherein the question sentence generation method comprises: obtaining a to-be-processed text and a target answer corresponding to the to-be-processed text; marking a phrase in the to-be-processed text to obtain phrase marking information and a phrase structure diagram; inputting the to-be-processed text, the target answer, the phrase marking information and the phrase structure diagram into a question sentence generation model, wherein the question sentence generation model is used to generate a question sentence corresponding to the to-be-processed text and the target answer; and obtaining a target question sentence output by the question sentence generation model. The unstructured text data is processed, so that the input source is no longer only a single sentence or a dialogue flow, the phrase for generating the target question sentence is determined according to the phrase marking information and the phrase structure diagram, and on the basis of ensuring the accuracy of generating the target question sentence, the diversity of generating the target question sentence is improved.

The invention provides a campus spoofing behavior identification method and apparatus, and an electronic device. The method comprises the following steps: acquiring original audio data of a campus environment; segmenting the original audio data to obtain a plurality of single-sentence audio clips divided according to natural sentences and timestamp information corresponding to the single-sentence audio clips; performing multi-dimensional feature extraction on each single-sentence audio clip to obtain multi-dimensional feature data of each single-sentence audio clip; according to the timestamp information of each single sentence audio clip, integrating the multi-dimensional feature data into a time-sequenced dialogue representation text; and according to the dialogue representation text, performing campus spoofing behavior identification and strategy analysis to generate a campus spoofing behavior identification result and a corresponding behavior intervention strategy. According to the technical scheme, the audio dialogue is converted into the dialogue representation text fusing the emotion and the side language features, high-precision recognition and interpretable analysis of the campus spoofing behavior can be achieved based on deep reasoning of the dialogue representation text, and operation guidance is provided for educators.

The application discloses a kind of knowledge graph construction methods based on fine-grained retrieval and reverse restoration self-error correction, specifically: first, standard reference case library is constructed, and standard reference vector is calculated.Then, the long text S to be measured is disassembled into sentences to be processed;Each sentence is vectorized, and the similarity score of its standard reference vector is calculated, and the first standard reference case of each sentence is screened;Through large language model, the non-standard logical relationship contained in each sentence is extracted and vectorized, the cosine similarity is calculated, and the candidate mapping is obtained by descending arrangement, after summarizing, double-feature reordering is carried out using multi-round greedy reordering algorithm, and the first standard reference case is screened out, and the single sentence is spliced into large language model, and triple extraction is carried out;Finally, the relationship triple set of all sentences constitutes knowledge graph.The application improves the precision and recall rate of triple extraction under the premise of avoiding redundancy.

The application belongs to the technical field of generative artificial intelligence, and provides a data debiasing and alignment enhancement method for a large language model, an electronic device and a storage medium, the method comprising: general text collection, single sentence disassembly, template matching, sentiment labeling, object conflict group division, bias text and neutral text division, neutral dialogue model construction, bias model construction, synchronous answer generation, double model output difference calculation, answer review and neutral answer output; the application converts complex cross-bias detection into object and sentiment comparison statistical analysis through template matching, sentiment labeling and conflict group division, reduces the complexity of debiasing, and improves the recognition efficiency of biased text; by constructing a neutral dialogue model and a bias model, an error reference is provided, the process of collecting a large number of personnel evaluations for traditional human preference alignment is avoided, and the training and review process of the neutral dialogue model during work are optimized.

Embodiments of the present specification provide a method and device for malicious query intention recognition of a large model, the method comprising: obtaining a query sequence of a target user interacting with a large model, wherein the query sequence comprises a plurality of query sentences; for each query sentence, determining a single-sentence jump degree indicator according to a first similarity with an adjacent query sentence and a maximum similarity with the query sequence; determining a first indicator value reflecting logical coherence of the query sequence according to the jump degree indicator of each query sentence; dividing the query sequence into a plurality of topic clusters, any topic cluster comprising a plurality of continuous query sentences; determining a second indicator value reflecting an average follow-up depth of the query sequence according to at least the cluster size of each topic cluster; and determining whether the query sequence has a malicious query intention for the large model according to the first indicator value and the second indicator value. The effectiveness of malicious query intention recognition for a large model can be improved.

The application discloses a kind of extraction methods, equipment and medium of central event, comprising the following steps: by determining the similarity of each single sentence included in the text to be extracted with the title of the text to be extracted, the first weight of trigger word included in each single sentence and the second weight of network security entity included in each single sentence, the central sentence is determined according to the similarity, the first weight and the second weight of each single sentence, the trigger word contained in the central sentence is determined, the event type pointed by the central sentence is determined based on the trigger word, the central event is obtained by calculating the central sentence and event type through BiLSTM model and CRF model, the central sentence is determined by three dimensions, the extraction range is reduced, the interference of secondary event on the extraction of central sentence is reduced, the error existing in the mode of pipeline extraction of central event is reduced by BiLSTM model and CRF model, so as to improve the convenience and effectiveness of central event extraction task.