265 research outputs found
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
A Hybrid Model for Sense Guessing of Chinese Unknown Words
PACLIC 23 / City University of Hong Kong / 3-5 December 200
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