Nagiza Samatova

Also published as: Nagiza F. Samatova


Multilevel Heuristics for Rationale-Based Entity Relation Classification in Sentences
Shiou Tian Hsu | Mandar Chaudhary | Nagiza Samatova
Proceedings of the 27th International Conference on Computational Linguistics

Rationale-based models provide a unique way to provide justifiable results for relation classification models by identifying rationales (key words and phrases that a person can use to justify the relation in the sentence) during the process. However, existing generative networks used to extract rationales come with a trade-off between extracting diversified rationales and achieving good classification results. In this paper, we propose a multilevel heuristic approach to regulate rationale extraction to avoid extracting monotonous rationales without compromising classification performance. In our model, rationale selection is regularized by a semi-supervised process and features from different levels: word, syntax, sentence, and corpus. We evaluate our approach on the SemEval 2010 dataset that includes 19 relation classes and the quality of extracted rationales with our manually-labeled rationales. Experiments show a significant improvement in classification performance and a 20% gain in rationale interpretability compared to state-of-the-art approaches.


A Hybrid CNN-RNN Alignment Model for Phrase-Aware Sentence Classification
Shiou Tian Hsu | Changsung Moon | Paul Jones | Nagiza Samatova
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

The success of sentence classification often depends on understanding both the syntactic and semantic properties of word-phrases. Recent progress on this task has been based on exploiting the grammatical structure of sentences but often this structure is difficult to parse and noisy. In this paper, we propose a structure-independent ‘Gated Representation Alignment’ (GRA) model that blends a phrase-focused Convolutional Neural Network (CNN) approach with sequence-oriented Recurrent Neural Network (RNN). Our novel alignment mechanism allows the RNN to selectively include phrase information in a word-by-word sentence representation, and to do this without awareness of the syntactic structure. An empirical evaluation of GRA shows higher prediction accuracy (up to 4.6%) of fine-grained sentiment ratings, when compared to other structure-independent baselines. We also show comparable results to several structure-dependent methods. Finally, we analyzed the effect of our alignment mechanism and found that this is critical to the effectiveness of the CNN-RNN hybrid.


PackPlay: Mining Semantic Data in Collaborative Games
Nathan Green | Paul Breimyer | Vinay Kumar | Nagiza Samatova
Proceedings of the Fourth Linguistic Annotation Workshop


WebBANC: Building Semantically-Rich Annotated Corpora from Web User Annotations of Minority Languages
Nathan Green | Paul Breimyer | Vinay Kumar | Nagiza F. Samatova
Proceedings of the 17th Nordic Conference of Computational Linguistics (NODALIDA 2009)