[go: up one dir, main page]

Liu et al., 2014 - Google Patents

Computing semantic relatedness using a word-text mutual guidance model

Liu et al., 2014

View PDF
Document ID
10543859458064164756
Author
Liu B
Feng J
Liu M
Liu F
Wang X
Li P
Publication year
Publication venue
CCF International Conference on Natural Language Processing and Chinese Computing

External Links

Snippet

The computation of relatedness between two fragments of text or two words is a challenging task in many fields. In this study, we propose a novel method for measuring semantic relatedness between word units and between text units using an iterative process, which we …
Continue reading at tcci.ccf.org.cn (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30634—Querying
    • G06F17/30657—Query processing
    • G06F17/30675—Query execution
    • G06F17/30684—Query execution using natural language analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30634—Querying
    • G06F17/30657—Query processing
    • G06F17/3066—Query translation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30861—Retrieval from the Internet, e.g. browsers
    • G06F17/30864—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
    • G06F17/30867—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30613—Indexing
    • G06F17/30619—Indexing indexing structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30705—Clustering or classification
    • G06F17/3071—Clustering or classification including class or cluster creation or modification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30705—Clustering or classification
    • G06F17/30707—Clustering or classification into predefined classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20—Handling natural language data
    • G06F17/27—Automatic analysis, e.g. parsing
    • G06F17/2705—Parsing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20—Handling natural language data
    • G06F17/27—Automatic analysis, e.g. parsing
    • G06F17/2765—Recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20—Handling natural language data
    • G06F17/21—Text processing
    • G06F17/22—Manipulating or registering by use of codes, e.g. in sequence of text characters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run

Similar Documents

Publication Publication Date Title
Gugnani et al. Implicit skills extraction using document embedding and its use in job recommendation
De Gemmis et al. Integrating tags in a semantic content-based recommender
Sunilkumar et al. A survey on semantic similarity
US20190260694A1 (en) System and method for chat community question answering
Voskarides et al. Learning to explain entity relationships in knowledge graphs
Xiong et al. Towards better text understanding and retrieval through kernel entity salience modeling
Bhatia A comparative study of opinion summarization techniques
Fried et al. Higher-order lexical semantic models for non-factoid answer reranking
Anjum et al. PaRe: A paper-reviewer matching approach using a common topic space
Tsakalidis et al. An ensemble model for cross-domain polarity classification on twitter
Buscaldi et al. Lipn-core: Semantic text similarity using n-grams, wordnet, syntactic analysis, esa and information retrieval based features
Rudrapal et al. A survey on automatic twitter event summarization
Li et al. CIST@ CLSciSumm-17: Multiple Features Based Citation Linkage, Classification and Summarization.
Sharma et al. BioAMA: towards an end to end biomedical question answering system
Yu et al. Data-driven contextual valence shifter quantification for multi-theme sentiment analysis
Godoy et al. Leveraging semantic similarity for folksonomy-based recommendation
Liu et al. Computing semantic relatedness using a word-text mutual guidance model
Wang et al. CMU OAQA at TREC 2015 LiveQA: Discovering the right answer with clues
Wenchao et al. A modified approach to keyword extraction based on word-similarity
Nguyen et al. Learning to estimate the importance of sentences for multi-document summarization
Duong et al. Evaluating semantic relatedness between concepts
Zhi et al. Aspects opinion mining based on word embedding and dependency parsing
Ramesh et al. Extractive Text Summarization Using Graph Based Ranking Algorithm and Mean Shift Clustering
Xiang et al. Multidimensional scaling based knowledge provision for new questions in community Question Answering systems
Wang et al. Aggregating user-centric and post-centric sentiments from social media for topical stance prediction