6 results about "Collocation" patented technology

The application relates to the technical field of natural language processing, and discloses a Chinese numeral quantifier correction method and system, computer equipment and a storage medium. The application constructs a dynamically expandable collocation word table and a numeral dictionary; performs sentence segmentation, word segmentation and part-of-speech tagging on input text, and identifies a three-element structure composed of numerals, quantifiers and nouns; excludes misjudgments on inherent expressions through a fixed phrase filtering mechanism; performs legality verification on the structure based on the collocation word table, and generates processing information for semantic correction if the verification fails; when collocation abnormalities are confirmed and the dictionary is insufficient, a large language model is triggered to replace the quantifier and adapt to the context based on the processing information, so that a final corrected sentence is generated. Through multi-layer cooperation of rule matching, word table verification and large model verification, the application effectively reduces the mis-correction rate while ensuring high-precision correction, and improves the accuracy and practicality of automatic correction of Chinese quantifiers.

The application discloses a subjective question intelligent marking method and system supporting multi-text mixing, and relates to the technical field of online education. Through multi-granularity alignment and nonlinear fusion strategy, cross-paragraph information fusion and logic are effectively captured, and the scoring accuracy of complex answers is greatly improved. Combined with differentiated quality evaluation of Chinese and English, accurate scoring of Chinese coherence and structural integrity is realized, and English sentence-by-sentence fine revision can accurately mark grammatical errors and optimize vocabulary collocation. At the same time, the output of explainable scoring evidence can generate personalized comments and optimize model texts, helping students to improve accurately. The calibration mechanism and artificial review dynamically update the parameters to ensure the reliability of scoring, which not only improves the marking efficiency and reduces the human bias, but also promotes the upgrading of intelligent marking from simple scoring to accurate evaluation and personalized guidance. The problems of insufficient processing of multi-text mixed answers, lack of personalized comments and optimized model texts, and absence of English sentence-by-sentence fine revision in existing systems are effectively solved.

The application provides a Chinese event detection method based on a part-of-speech attention mechanism, which is based on a public data set. First, the sentence is divided into words using a word segmentation tool on the data set, and then the part-of-speech sequence of the sentence is obtained using a part-of-speech tagging tool. The part-of-speech sequence of the sentence is input into a CBOW model to obtain a pre-trained part-of-speech vector, so as to learn the fixed collocation information between words, such as "suffered an injury" as a "verb + adverb + noun" structure. Then, the part-of-speech vector, the word vector and the character vector are used to extract the word-level information and the character-level information of the sentence, respectively. When extracting features, a convolutional neural network is used to extract features on the word matrix, the character matrix and the part-of-speech matrix of the sentence. Then, the part-of-speech features are used to calculate the attention score, which is used to assist the model to focus on the verb when calculating; the part-of-speech attention is provided, and after the part-of-speech features are added, the accuracy and efficiency of the model in the trigger word extraction and event type classification tasks are higher.

The application discloses a corpus expansion method, device and equipment for speech recognition and a storage medium. The method comprises the following steps: performing word segmentation on the text in the collected corpus to be expanded by using a maximum forward matching algorithm, determining the part of speech of the segmented words in the text, and determining the segmented words with the pre-marked part of speech as entities according to a preset context collocation rule; identifying the pre-built scene recognition model in the corpus to be expanded, and determining the text with two entities in the text as in-set text; establishing a child node in the parent node in the pre-built knowledge graph through in-set calculation, integrating the entities of the in-set text into the knowledge graph; and synchronizing the newly added nodes in the knowledge graph to the speech recognition model through a protocol generation rule, making the word slot effective, and serving as a speech bottom-up strategy. The corpus for speech recognition can be autonomously expanded, and the corpus expansion efficiency and precision are greatly improved.

The application discloses a construction network generation method based on word collocation analysis. The method first extracts the construction from the corpus by using a construction extraction tool based on a pre-trained model to obtain a corresponding construction library; secondly, the model fully utilizes the information of the access word of the construction through word collocation analysis, and obtains the linguistic characteristics of the construction according to the collocation strength of the access word; then, the algorithm of the pipeline hierarchical clustering is used to form the construction category and generate the first layer network; finally, the second layer and higher layer network are generated based on the construction category, and the maximum common item between the abstract degree of the construction and the construction category is taken as the feature. The method fully utilizes the construction related knowledge learned by the pre-trained model, can capture and construct the characteristics of the construction, simultaneously establishes the network relationship between the constructions, and generates the construction network.

This invention provides a method and system for essay correction based on collocation, rhetoric, and grammar. The invention constructs a conditional random field layer. The conditional random field layer is based on Markov decision chains and can reconnect the relationships between words in a sentence. After the output of the BERT encoding unit is input into the conditional random field layer, the improved Viterbi algorithm is run to decode the final grammar modification label. The output construction unit then constructs the correct sentence based on the grammar modification label.