Minh-Quoc Nghiem

Vinh D. Van, Thien Thai, Minh-Quoc Nghiem

This paper addresses the problem of sentence-level sentiment analysis. In recent years, Convolution and Recursive Neural Networks have been proven to be effective network architecture for sentence-level sentiment analysis. Nevertheless, each of them has their own potential drawbacks. For alleviating their weaknesses, we combined Convolution and Recursive Neural Networks into a new network architecture. In addition, we employed transfer learning from a large document-level labeled sentiment dataset to improve the word embedding in our models. The resulting models outperform all recent Convolution and Recursive Neural Networks. Beyond that, our models achieve comparable performance with state-of-the-art systems on Stanford Sentiment Treebank.

Minh-Quoc Nghiem, Giovanni Yoko Kristianto, Goran Topic et al.

Mathematical content is a valuable information source and retrieving this content has become an important issue. This paper compares two searching strategies for math expressions: presentation-based and content-based approaches. Presentation-based search uses state-of-the-art math search system while content-based search uses semantic enrichment of math expressions to convert math expressions into their content forms and searching is done using these content-based expressions. By considering the meaning of math expressions, the quality of search system is improved over presentation-based systems.

Minh-Quoc Nghiem, Giovanni Yoko Kristianto, Goran Topic et al.

In this paper, we present a new approach to the semantic enrichment of mathematical expression problem. Our approach is a combination of statistical machine translation and disambiguation which makes use of surrounding text of the mathematical expressions. We first use Support Vector Machine classifier to disambiguate mathematical terms using both their presentation form and surrounding text. We then use the disambiguation result to enhance the semantic enrichment of a statistical-machine-translation-based system. Experimental results show that our system archives improvements over prior systems.