Abstract
The technique to identify and extract collocations in a given sentence is very important to sentence understanding, analysing and translating. So we propose a sentence level collocation identification and extraction method which follows the traditional two phase collocation extraction model. In candidate generating phase, we use the dependency parsing results directly, while in the filtering phase, we propose to use the latest model of distributional semantics - word embedding based similarity to filter the noises. For each candidate, three word embedding based similarity rankings will be obtained and accordingly to decide if it is a real collocation. The experimental results show that the proposed filtering method performs better than the traditional well-known association measures. The comparison with the baseline system shows that the proposed method can retrieve more collocations with higher precision than the baseline, which is of significance to sentence related natural language processing tasks.
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Notes
- 1.
The corpus is compiled specially for Chinese-English collocation pair extraction and the sentences are from on-line dictionaries, bilingual language learning websites, governmental websites that provide bilingual texts, etc.
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This work was supported by National Natural Science Foundation of China under Grant No. 61672127.
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Liu, X., Huang, D. (2017). Translation Oriented Sentence Level Collocation Identification and Extraction. In: Wong, D., Xiong, D. (eds) Machine Translation. CWMT 2017. Communications in Computer and Information Science, vol 787. Springer, Singapore. https://doi.org/10.1007/978-981-10-7134-8_8
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