Overview of the NLPCC-ICCPOL 2016 Shared Task: Chinese Word Similarity Measurement

2016 ◽  
pp. 828-839 ◽  
Author(s):  
Yunfang Wu ◽  
Wei Li
Keyword(s):  
Similarity Measurement ◽  
Chinese Word ◽  
Shared Task ◽  
Word Similarity

scholarly journals Implanting Rational Knowledge into Distributed Representation at Morpheme Level

2019 ◽  
Vol 33 ◽  
pp. 2954-2961
Author(s):  
Zi Lin ◽  
Yang Liu
Keyword(s):  
Word Formation ◽  
Similarity Measurement ◽  
Distributed Representation ◽  
Word Similarity ◽  
Data Sparseness ◽  
Percentage Points ◽  
Novel Approach ◽  
Classical Models ◽  
Semantic Ontology ◽  
Rational Knowledge

Previously, researchers paid no attention to the creation of unambiguous morpheme embeddings independent from the corpus, while such information plays an important role in expressing the exact meanings of words for parataxis languages like Chinese. In this paper, after constructing the Chinese lexical and semantic ontology based on word-formation, we propose a novel approach to implanting the structured rational knowledge into distributed representation at morpheme level, naturally avoiding heavy disambiguation in the corpus. We design a template to create the instances as pseudo-sentences merely from the pieces of knowledge of morphemes built in the lexicon. To exploit hierarchical information and tackle the data sparseness problem, the instance proliferation technique is applied based on similarity to expand the collection of pseudo-sentences. The distributed representation for morphemes can then be trained on these pseudo-sentences using word2vec. For evaluation, we validate the paradigmatic and syntagmatic relations of morpheme embeddings, and apply the obtained embeddings to word similarity measurement, achieving significant improvements over the classical models by more than 5 Spearman scores or 8 percentage points, which shows very promising prospects for adoption of the new source of knowledge.

scholarly journals Modeling multi-prototype Chinese word representation learning for word similarity

2021 ◽  
Author(s):  
Fulian Yin ◽  
Yanyan Wang ◽  
Jianbo Liu ◽  
Marco Tosato
Keyword(s):  
Knowledge Representation ◽  
Language Processing ◽  
Representation Learning ◽  
Word Knowledge ◽  
Data Sets ◽  
Chinese Word ◽  
Word Similarity ◽  
Public Data ◽  
Word Representation ◽  
The Stability

AbstractThe word similarity task is used to calculate the similarity of any pair of words, and is a basic technology of natural language processing (NLP). The existing method is based on word embedding, which fails to capture polysemy and is greatly influenced by the quality of the corpus. In this paper, we propose a multi-prototype Chinese word representation model (MP-CWR) for word similarity based on synonym knowledge base, including knowledge representation module and word similarity module. For the first module, we propose a dual attention to combine semantic information for jointly learning word knowledge representation. The MP-CWR model utilizes the synonyms as prior knowledge to supplement the relationship between words, which is helpful to solve the challenge of semantic expression due to insufficient data. As for the word similarity module, we propose a multi-prototype representation for each word. Then we calculate and fuse the conceptual similarity of two words to obtain the final result. Finally, we verify the effectiveness of our model on three public data sets with other baseline models. In addition, the experiments also prove the stability and scalability of our MP-CWR model under different corpora.

Similarity measurement using term negative weight and its application to word similarity

2000 ◽  
Vol 36 (5) ◽  
pp. 717-736 ◽  
Author(s):  
El-Sayed Atlam ◽  
Masao Fuketa ◽  
Kazuhiro Morita ◽  
Jun-ichi Aoe
Keyword(s):  
Similarity Measurement ◽  
Negative Weight ◽  
Word Similarity

scholarly journals Pronunciation-Enhanced Chinese Word Embedding

2021 ◽  
Author(s):  
Qinjuan Yang ◽  
Haoran Xie ◽  
Gary Cheng ◽  
Fu Lee Wang ◽  
Yanghui Rao
Keyword(s):  
Sentiment Analysis ◽  
Text Classification ◽  
Semantic Information ◽  
Word Embedding ◽  
Chinese Characters ◽  
Learning Method ◽  
Word Embeddings ◽  
Chinese Word ◽  
Word Similarity ◽  
Meaning Structure

AbstractChinese word embeddings have recently garnered considerable attention. Chinese characters and their sub-character components, which contain rich semantic information, are incorporated to learn Chinese word embeddings. Chinese characters can represent a combination of meaning, structure, and pronunciation. However, existing embedding learning methods focus on the structure and meaning of Chinese characters. In this study, we aim to develop an embedding learning method that can make complete use of the information represented by Chinese characters, including phonology, morphology, and semantics. Specifically, we propose a pronunciation-enhanced Chinese word embedding learning method, where the pronunciations of context characters and target characters are simultaneously encoded into the embeddings. Evaluation of word similarity, word analogy reasoning, text classification, and sentiment analysis validate the effectiveness of our proposed method.

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