TAILIEUCHUNG - Báo cáo khoa học: "Word Sense Disambiguation using lexical cohesion in the context"

This paper designs a novel lexical hub to disambiguate word sense, using both syntagmatic and paradigmatic relations of words. It only employs the semantic network of WordNet to calculate word similarity, and the Edinburgh Association Thesaurus (EAT) to transform contextual space for computing syntagmatic and other domain relations with the target word. Without any back-off policy the result on the English lexical sample of SENSEVAL-21 shows that lexical cohesion based on edge-counting techniques is a good way of unsupervisedly disambiguating senses. . | Word Sense Disambiguation using lexical cohesion in the context Dongqiang Yang David . Powers School of Informatics and Engineering Flinders University of South Australia PO Box 2100 Adelaide Abstract This paper designs a novel lexical hub to disambiguate word sense using both syntagmatic and paradigmatic relations of words. It only employs the semantic network of WordNet to calculate word similarity and the Edinburgh Association Thesaurus EAT to transform contextual space for computing syntagmatic and other domain relations with the target word. Without any back-off policy the result on the English lexical sample of SENSEVAL-21 shows that lexical cohesion based on edge-counting techniques is a good way of unsupervisedly disambiguating senses. 1 Introduction Word Sense Disambiguation WSD is generally taken as an intermediate task like part-of-speech POS tagging in natural language processing but it has not so far achieved the sufficient precision for application as POS tagging for the history of WSD cf. Ide and Véronis 1998 . It is partly due to the nature of its complexity and difficulty and to the widespread disagreement and controversy on its necessity in language engineering and to the representation of the senses of words as well as to the validity of its evaluation Kilgarriff and Palmer 2000 . However the endeavour to automatically achieve WSD has been continuous since the earliest work of the 1950 s. In this paper we specifically investigate the role of semantic hierarchies of lexical knowledge on WSD using datasets and evaluation methods from SENSEVAL Kilgarriff and Rosenzweig 2000 as these are well known and accepted in the community of computational linguistics. With respect to whether or not they employ the training materials provided SENSEVAL roughly categorizes the participating systems into unsupervised systems and supervised systems . Those that don t use the training data are not usually truly .

• Báo cáo khoa học
• Word Sense Disambiguation
• lexical cohesion in the context
• Dongqiang Yang
• 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
• It Makes Sense
• A Wide Coverage Word Sense Disambiguation System
• Free Text
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• 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
• 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
• 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
• Relieving The Data Acquisition Bottleneck
• Mona Diab
• A Kernel PCA Method
• Superior Word Sense Disambiguation
• Exploiting Parallel Texts
• An Empirical Study
• Pairwise Alignment
• Koichi Yamashita Keiichi Yoshida Yukihiro Itoh