Component-Enhanced Chinese Character Embeddings

Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on English. In this work, we innovatively develop two component-enhanced Chinese character embedding models and their bigram extensions. Distinguished from English word embeddings, our models explore the compositions of Chinese characters, which often serve as semantic indictors inherently. The evaluations on both word similarity and text classification demonstrate the effectiveness of our models.Comment: 6 pages, 2 figures, conference, EMNLP 201

An Improved Semantic Similarity Algorithm on Hownet

Semantic similarity algorithm is one of the basic researches in the field of natural language processing. This algorithm is widely used in information retrieval, machine translation based on examples and other fields. In this paper, based on the basis of HowNet lexical semantic similarity algorithm, introduced the concept of fuzzy mathematics degree of membership, the fixed weighting factor assigned into a coefficient of variation based on statistics through experimental verification of the results of this improved contribution

A Step toward Compositional Semantics: E-HowNet a Lexical Semantic Representation System

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Modality and Modal Sense Representation in E-HowNet

PACLIC 21 / Seoul National University, Seoul, Korea / November 1-3, 200