A Language-Independent Hybrid Approach for Multi-Word Expression Extraction

Failing to identify multi-word expression (MWE) may cause serious problems for many Natural Language Processing (NLP) tasks. Previous approaches heavily depend on language specific knowledge and pre-existing natural language processing (NLP) tools. However, many languages (including Chinese language) have less such resources and tools compared to English. An automatically learn effective features from corpus, without relying on language specific resources is needed. In this paper, we develop a hybrid approach that combines Bidirectional long short-term memory (Bi-LSTM), word correlation degree calculation and weakly supervised K-means cluster to capture both sequence information and correlation degree of phrase from specific contexts, and use them to train a multi-word expression detector for multiple languages without any manually encoded features. Experiment result shows that the extraction results of Chinese and English multi-word expression using this hybrid approach is better than that of baseline algorithm, which verified that the hybrid approach is effective.