[go: up one dir, main page]

Agirre et al., 2008 - Google Patents

Improving parsing and PP attachment performance with sense information

Agirre et al., 2008

View PDF
Document ID
16274224068027977924
Author
Agirre E
Baldwin T
Martinez D
Publication year
Publication venue
proceedings of ACL-08: HLT

External Links

Snippet

To date, parsers have made limited use of semantic information, but there is evidence to suggest that semantic features can enhance parse disambiguation. This paper shows that semantic classes help to obtain significant improvement in both parsing and PP attachment …
Continue reading at aclanthology.org (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
    • G06F17/20—Handling natural language data
    • G06F17/27—Automatic analysis, e.g. parsing
    • G06F17/2705—Parsing
    • G06F17/271—Syntactic parsing, e.g. based on context-free grammar [CFG], unification grammars
    • 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
    • G06F17/277—Lexical analysis, e.g. tokenisation, collocates
    • 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
    • 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
    • G06F17/274—Grammatical analysis; Style critique
    • 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
    • G06F17/2785—Semantic 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
    • G06F17/20—Handling natural language data
    • G06F17/28—Processing or translating of natural language
    • G06F17/2809—Data driven 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
    • G06F17/20—Handling natural language data
    • G06F17/21—Text processing
    • 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
    • G06F17/50—Computer-aided design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
    • G06K9/6807—Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries
    • G06K9/6842—Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries according to the linguistic properties, e.g. English, German

Similar Documents

Publication Publication Date Title
Agirre et al. Improving parsing and PP attachment performance with sense information
Navigli Meaningful clustering of senses helps boost word sense disambiguation performance
Turmo et al. Adaptive information extraction
Hatzivassiloglou et al. Simfinder: A flexible clustering tool for summarization
Chen et al. An empirical study of the behavior of active learning for word sense disambiguation
Pedersen Unsupervised corpus-based methods for WSD
Ng et al. Exploiting category-specific information for multi-document summarization
Pennacchiotti et al. Automatic induction of FrameNet lexical units
Dang Investigations into the role of lexical semantics in word sense disambiguation
Nissim et al. Modeling the internal variability of multiword expressions through a pattern-based method
Baldwin Bootstrapping deep lexical resources: Resources for courses
Esteche et al. Automatic definition extraction and crossword generation from spanish news text
Qiu et al. Combining contextual and structural information for supersense tagging of Chinese unknown words
Ruppenhofer et al. In search of missing arguments: A linguistic approach
MacKinlay et al. The effects of semantic annotations on precision parse ranking
Krisnawati Plagiarism detection for Indonesian texts
Cao et al. Automated question answering from lecture videos: NLP vs. pattern matching
Villavicencio Verb-particle constructions in the world wide web
Vicedo et al. University of Alicante at TREC-10
Bethard Finding event, temporal and causal structure in text: A machine learning approach
Baldwin General-purpose lexical acquisition: Procedures, questions and results
Tambouratzis et al. Discriminating the registers and styles in the Modern Greek language
Gamon et al. Search right and thou shalt find... using web queries for learner error detection
Ogrodniczuk et al. Rule-based coreference resolution module for Polish
Tian et al. Measuring the similarity of short texts by word similarity and tree kernels