TAILIEUCHUNG - Báo cáo khoa học: "Sense Disambiguation Using Semantic Relations and Adjacency Information"

This paper describes a heuristic-based approach t o word-sense disambiguation. The heuristics that are applied to disambiguate a word depend on its part of speech, and on its relationship to neighboring salient words in the text. Parts of speech are found through a tagger, and related neighboring words are identified by a phrase extractor operating on the tagged text. To suggest possible senses, each heuristic draws on semantic relations extracted from a Webster's dictionary and the semantic thesaurus WordNet. For a given word, all applicable heuristics are tried, and those senses that are rejected by all heuristics are discarded | Sense Disambiguation Using Semantic Relations and Adjacency Information Anil s. Chakravarthy MIT Media Laboratory 20 Ames Street E15-468a Cambridge MA 02139 anil @ Abstract This paper describes a heuristic-based approach to word-sense disambiguation. The heuristics that are applied to disambiguate a word depend on its part of speech and on its relationship to neighboring salient words in the text. Parts of speech are found through a tagger and related neighboring words are identified by a phrase extractor operating on the tagged text. To suggest possible senses each heuristic draws on semantic relations extracted from a Webster s dictionary and the semantic thesaurus WordNet. For a given word all applicable heuristics are tried and those senses that are rejected by all heuristics are discarded. In all the disam-biguator uses 39 heuristics based on 12 relationships. 1 Introduction Word-sense disambiguation has long been recognized as a difficult problem in computational linguistics. As early as 1960 Bar-Hillel 1 noted that a computer program would find it challenging to recognize the two different senses of the word pen in The pen is in the box and The box is in the pen. In recent years there has been a resurgence of interest in word-sense disambiguation due to the availability of linguistic resources like dictionaries and thesauri and due to the importance of disambiguation in applications like information retrieval and machine translation. The task of disambiguation is to assign a word to one or more senses in a reference by taking into account the context in which the word occurs. The reference can be a standard dictionary or thesaurus or a lexicon constructed specially for some application. The context is provided by the text unit paragraph sentence etc. in which the word occurs. The disambiguator described in this paper is based on two reference sources the Webster s Seventh Dictionary and the semantic thesaurus WordNet 12 . Before the .

• Báo cáo khoa học
• Sense Disambiguation
• Semantic Relations
• Adjacency Information
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• International Journal of Computer Science & Communication Networks
• Automatic word sense disambiguation using wikipedia link structure
• Automatic word sense disambiguation
• Wikipedia link structure
• Word sense disambiguation
• It Makes Sense
• A Wide Coverage Word Sense Disambiguation System
• Free Text
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Unsupervised Relation Discovery
• Limin Yao
• Word Sense Disambiguation Improves Information Retrieval
• Zhi Zhong and Hwee Tou Ng
• Long Papers
• Topic Models
• Token based Idiom Detection
• Knowledge rich Word Sense
• Disambiguation Rivaling Supervised Systems
• Simone Paolo Ponzetto
• Latent Semantic Word Sense Induction
• Disambiguation
• ParaSense
• How to Use Parallel Corpora
• Models and Training
• Unsupervised Preposition Sense Disambiguation
• Dirk Hovy and Ashish Vaswani
• Novel Semantic Features
• Verb Sense Disambiguation Dmitriy Dligach
• Combined One Sense Disambiguation of Abbreviations
• Yaakov HaCohen Kerner
• Estimating Class Priors
• Domain Adaptation
• Meaningful Clustering of Senses
• Helps Boost Word Sense
• Disambiguation Performance
• An Equivalent Pseudoword Solution
• Chinese Word Sense Disambiguation
• Zhimao Lu
• A Combination of Active Learning
• Semi supervised Learning Starting
• Positive and Unlabeled Examples for Word Sense Disambiguation An Empirical Study on Japanese Web Search Query
• lexical cohesion in the context
• Dongqiang Yang
• Improves Statistical
• Machine Translation
• Learning Expressive Models
• Lucia Specia
• Active Learning
• Statistical Machine Translation
• Marine CARPUAT
• Label Propagation Based Semi Supervised Learning
• Zheng Yu Niu
• Dong Hong Ji
• Domain Kernels
• Alfio Gliozzo and Claudio Giuliano and Carlo Strapparava
• Learning Semantic Classes
• Upali S
• Kohomban Wee Sun Lee
• SenseLearner
• All Words in Unrestricted Text
• TargetWord
• A Generalized Framework