PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction
Table 2: Comparison of F1 scores on the SemEval 2014/2015/2016 datasets. In t-tests, the marker ∗ refers to p-value < 0.05, the marker † refers to p-value < 0.01, and WDEmb Yin et al. (2016) is the compared method.
['[BOLD] Method', '[BOLD] D1', '[BOLD] D2', '[BOLD] D3', '[BOLD] D4']
From the table, we find that PoD with both Spuct and Sadd consistently outperform WDEmb which is one of the best embedding methods. The reasons are that (i) our model incorporates positional context as relative position encoding to help enhance word embeddings; (ii) the dependency context leverages the lexical dependency path capturing more specific lexical information such as words and POS tags (extracted using Stanford CoreNLP) than WDEmb. PoD also achieves comparable results with top systems which are based on hand-crafted features in all datasets, which shows that our learned embeddings are effective for aspect term extraction. The Spuct performs better than Sadd, which indicates that the product-based composition method is more capable in capturing the useful features in aspect term extraction. In terms of embedding-based baselines, DepEmb and WDEmb perform better than other baselines, which indicates that encoding syntactic knowledge into word embeddings is desirable for aspect term extraction.