# US 2021/0279419 A1 · Method and system of extracting vocabulary for imagery of product > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0279419 A1 - Document ID: US20210279419A1 - Status: Published application - Kind: A1 - Application number: 17/035,457 - Filed: 2020-09-28 - Published: 2021-09-09 - Earliest claimed: 2020-03-09 - Assignees: China Academy of Art - Applicants: China Academy of Art - Inventors: Zheng LIU; Zhixuan CHEN; Yujing WANG; Yun WANG; Huijun HU - Web page: https://patentgrep.com/patent/US20210279419A1 ## CPC - G06F 40/30: Semantic analysis - G06F 16/35: Clustering; Classification - G06F 40/216: using statistical methods - G06F 40/253: Grammatical analysis; Style critique - G06F 40/279: Recognition of textual entities - G06Q 30/0201: Market modelling; Market analysis; Collecting market data - G06F 40/211: Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars - G06F 40/284: Lexical analysis, e.g. tokenisation or collocates - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract A method and a system for extracting vocabulary for imagery of a product, including: collecting a comment text data of a target product, segmenting the comment text data to obtain an evaluation vocabulary; extracting a high-frequency word used to evaluate an appearance as a central word from the evaluation vocabulary, and extracting an adjective from the evaluation vocabulary to obtain the adjectives; converting the evaluation vocabulary into a word vector, calculating a similarity between each adjectives and the central word based on the word vector, extracting a corresponding adjectives according to the similarity as an original vocabulary for imager; clustering the original vocabulary for imagery, exacting the corresponding original vocabulary for imagery according to a clustering result as a vocabulary for imagery, which can extract the vocabulary for imagery based on the comment text data, reduce the labor cost, and improve the extraction efficiency. ## Claims ### Claim 1 A method of extracting vocabulary for imagery of product comprising the steps of: collecting a comment text data of a target product, and segmenting the comment text data to obtain an evaluation vocabulary; extracting a high-frequency word used to evaluate an appearance as a central word from the evaluation vocabulary, and extracting an adjective from the evaluation vocabulary to obtain an adjectives; converting the evaluation vocabulary into a word vector, calculating a similarity between each adjectives and the central word based on the word vector, and extracting a corresponding adjectives according to the similarity as an original vocabulary for imagery; and clustering the original vocabulary for imagery, and exacting a corresponding original vocabulary for imagery according to a clustering result as a vocabulary for imagery. ### Claim 2 (depends on 1) The method of extracting vocabulary for imagery of product according to claim 1, wherein calculating a similarity between each adjectives and the central word based on the word vector, and extracting a corresponding adjectives according to the similarity as an original vocabulary for imagery comprises: calculating a cosine similarity between the word vector corresponding to the central word and the word vector corresponding to each adjectives, taking a calculation result as the similarity between the central word and the adjectives; extracting the adjectives having a similarity exceeding a preset similarity threshold as a related word, and obtaining a word frequency of each related word occurring in the evaluation vocabulary; merging the related words corresponding to each central word, extracting the related words with the word frequency exceeding a preset word frequency threshold, and obtaining the original vocabulary for imagery. ### Claim 3 (depends on 1) The method of extracting vocabulary for imagery of product according to claim 1, wherein clustering the original vocabulary for imagery, and exacting the corresponding original vocabulary for imagery according to a clustering result as a vocabulary for imagery comprising: calculating a clustering number based on the word vector of the original vocabulary for imagery; clustering the original vocabulary for imagery according to the word vector of the original vocabulary for imagery, obtaining a corresponding number of clusters and obtaining a cluster center of each cluster, extracting the original vocabulary for imagery closest to the cluster center of each cluster, and generating and outputting the vocabulary for imagery. ### Claim 4 (depends on 1) The method of extracting vocabulary for imagery of product according to claim 1, wherein extracting a high-frequency word used to evaluate an appearance as a central word from the evaluation vocabulary, and extracting an adjective from the evaluation vocabulary to obtain the adjectives comprising: classifying the evaluation vocabulary according to a part of speech, extracting the evaluation vocabulary with the part of speech as an adjective to obtain the adjectives, and extracting the evaluation vocabulary with the part of speech as an noun or a verb; eliminating a word referring to the target product from a extracted nouns and a extracted verbs and obtaining a basic word; counting a word frequency of each basic word in the evaluation vocabulary, extracting a corresponding basic word according to the word frequency to obtain high-frequency words, and selecting a word for evaluating appearance and filtering out from the high-frequency words as the central word. ### Claim 5 (depends on 1) The method of extracting vocabulary for imagery of product according to claim 1, wherein the evaluation vocabulary is converted into a word vector based on word2vec model. ### Claim 6 (depends on 1) The method of extracting vocabulary for imagery of product according to claim 1, wherein clustering the original vocabulary for imagery, and exacting the corresponding original vocabulary for imagery according to a clustering result as a vocabulary for imagery is followed by a visualization processing step, and the step comprises: performing dimension reduction processing on the word vector of the original vocabulary for imagery to obtain a corresponding coordinate point; mapping the coordinate point to a two-dimensional plane according to a clustering result, and generating and outputting a space map of a vocabulary for imagery. ### Claim 7 A system of extracting vocabulary for imagery of product, comprising: a corpus acquisition module is used for acquiring the evaluation vocabulary, collecting a comment text data of a target product, and segmenting the comment text data to obtain an evaluation vocabulary; a pre-extraction module for extracting a high-frequency word used to evaluate an appearance as a central word from the evaluation vocabulary, and extracting an adjective from the evaluation vocabulary to obtain the adjectives; converting the evaluation vocabulary into a word vector, calculating a similarity between each adjectives and the central word based on the word vector, and extracting a corresponding adjectives according to the similarity as an original vocabulary for imagery; a extraction module for clustering the original vocabulary for imagery, and exacting the corresponding original vocabulary for imagery according to a clustering result as a vocabulary for imagery. ### Claim 8 (depends on 7) The system for extracting vocabulary for imagery of product according to claim 7, wherein the pre-extraction module comprises a first vocabulary extraction unit and a second vocabulary extraction unit; the first vocabulary extraction unit is configured to: classifying the evaluation vocabulary according to a part of speech, extracting the evaluation vocabulary with the part of speech as an adjective to obtain the adjectives, and extracting the evaluation vocabulary with the part of speech as an noun or a verb; eliminating a word referring to the target product from a extracted nouns and a extracted verbs and obtaining a basic word; counting a word frequency of each basic word in the evaluation vocabulary, extracting a corresponding basic word according to the word frequency to obtain high-frequency words, and selecting a word for evaluating appearance and filtering out from the high-frequency words as the central word; the second vocabulary extraction unit is configured to: calculating a cosine similarity between the word vector corresponding to the central word and the word vector corresponding to each adjectives, taking a calculation result as the similarity between the central word and the adjectives; extracting the adjectives having a similarity exceeding a preset similarity threshold as a related word, and obtaining a word frequency of each related word occurring in the evaluation vocabulary; merging the related words corresponding to each central word, extracting the related words with the word frequency exceeding a preset word frequency threshold, and obtaining the original vocabulary for imagery. ### Claim 9 (depends on 7) The system for extracting vocabulary for imagery of product according to claim 7, wherein further comprises a space map generation module, the space map generation module is configured to: performing dimension reduction processing on the word vector of the original vocabulary for imagery to obtain a corresponding coordinate point; mapping the coordinate point to a two-dimensional plane according to a clustering result, and generating and outputting a space map of a vocabulary for imagery. ### Claim 10 (depends on 1) A computer readable storage medium storing a computer program, wherein the program is executed by a processor to implement the steps of the method of claim 1. ### Claim 11 (depends on 10) The computer readable storage medium storing a computer program according to claim 10, wherein calculating a similarity between each adjectives and the central word based on the word vector, and extracting a corresponding adjectives according to the similarity as an original vocabulary for imagery comprises: calculating a cosine similarity between the word vector corresponding to the central word and the word vector corresponding to each adjectives, taking a calculation result as the similarity between the central word and the adjectives; extracting the adjectives having a similarity exceeding a preset similarity threshold as a related word, and obtaining a word frequency of each related word occurring in the evaluation vocabulary; merging the related words corresponding to each central word, extracting the related words with the word frequency exceeding a preset word frequency threshold, and obtaining the original vocabulary for imagery. ### Claim 12 (depends on 10) The computer readable storage medium storing a computer program according to claim 10, wherein clustering the original vocabulary for imagery, and exacting the corresponding original vocabulary for imagery according to a clustering result as a vocabulary for imagery comprising: calculating a clustering number based on the word vector of the original vocabulary for imagery; clustering the original vocabulary for imagery according to the word vector of the original vocabulary for imagery, obtaining a corresponding number of clusters and obtaining a cluster center of each cluster, extracting the original vocabulary for imagery closest to the cluster center of each cluster, and generating and outputting the vocabulary for imagery. ### Claim 13 (depends on 10) The computer readable storage medium storing a computer program according to claim 10, wherein extracting a high-frequency word used to evaluate an appearance as a central word from the evaluation vocabulary, and extracting an adjective from the evaluation vocabulary to obtain the adjectives comprising: classifying the evaluation vocabulary according to a part of speech, extracting the evaluation vocabulary with the part of speech as an adjective to obtain the adjectives, and extracting the evaluation vocabulary with the part of speech as an noun or a verb; eliminating a word referring to the target product from a extracted nouns and a extracted verbs and obtaining a basic word; counting a word frequency of each basic word in the evaluation vocabulary, extracting a corresponding basic word according to the word frequency to obtain high-frequency words, and selecting a word for evaluating appearance and filtering out from the high-frequency words as the central word. ### Claim 14 (depends on 10) The computer readable storage medium storing a computer program according to claim 10, wherein the evaluation vocabulary is converted into a word vector based on word2vec model. ### Claim 15 (depends on 10) The computer readable storage medium storing a computer program according to claim 10, wherein clustering the original vocabulary for imagery, and exacting the corresponding original vocabulary for imagery according to a clustering result as a vocabulary for imagery is followed by a visualization processing step, and the step comprises: performing dimension reduction processing on the word vector of the original vocabulary for imagery to obtain a corresponding coordinate point; mapping the coordinate point to a two-dimensional plane according to a clustering result, and generating and outputting a space map of a vocabulary for imagery.