Bita Nejat


Exploring Joint Neural Model for Sentence Level Discourse Parsing and Sentiment Analysis
Bita Nejat | Giuseppe Carenini | Raymond Ng
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

Discourse Parsing and Sentiment Analysis are two fundamental tasks in Natural Language Processing that have been shown to be mutually beneficial. In this work, we design and compare two Neural Based models for jointly learning both tasks. In the proposed approach, we first create a vector representation for all the text segments in the input sentence. Next, we apply three different Recursive Neural Net models: one for discourse structure prediction, one for discourse relation prediction and one for sentiment analysis. Finally, we combine these Neural Nets in two different joint models: Multi-tasking and Pre-training. Our results on two standard corpora indicate that both methods result in improvements in each task but Multi-tasking has a bigger impact than Pre-training. Specifically for Discourse Parsing, we see improvements in the prediction of the set of contrastive relations.


Abstractive Summarization of Product Reviews Using Discourse Structure
Shima Gerani | Yashar Mehdad | Giuseppe Carenini | Raymond T. Ng | Bita Nejat
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)