We explore how features based on syntactic dependency relations can be utilized to improve performance on opinion mining. Using a transformation of dependency relation triples, we convert them into "composite back-off features" that generalize better than the regular lexicalized dependency relation features. Experiments comparing our approach with several other approaches that generalize dependency features or ngrams demonstrate the utility of composite back-off features.

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This paper presents a case study of using distributed word representations, word2vec in particular, for improving performance of Named Entity Recognition for the eCommerce domain. We also demonstrate that distributed word representations trained on a smaller amount of in-domain data are more effective than word vectors trained on very large amount of out-of-domain data, and that their combination gives the best results.

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