Gaurav Singh Tomar

Also published as: Gaurav Singh


2019

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Relation Extraction using Explicit Context Conditioning
Gaurav Singh | Parminder Bhatia
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Relation extraction (RE) aims to label relations between groups of marked entities in raw text. Most current RE models learn context-aware representations of the target entities that are then used to establish relation between them. This works well for intra-sentence RE, and we call them first-order relations. However, this methodology can sometimes fail to capture complex and long dependencies. To address this, we hypothesize that at times the target entities can be connected via a context token. We refer to such indirect relations as second-order relations, and describe an efficient implementation for computing them. These second-order relation scores are then combined with first-order relation scores to obtain final relation scores. Our empirical results show that the proposed method leads to state-of-the-art performance over two biomedical datasets.

2018

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Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding
Gaurav Singh | James Thomas | Iain Marshall | John Shawe-Taylor | Byron C. Wallace
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i.e., an ontology). We treat this as a special case of sequence-to-sequence learning in which the decoder begins at the root node of an ontological tree and recursively elects to expand child nodes as a function of the input text, the current node, and the latent decoder state. We demonstrate that this method yields state-of-the-art results on the important task of assigning MeSH terms to biomedical abstracts.

2017

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Neural Paraphrase Identification of Questions with Noisy Pretraining
Gaurav Singh Tomar | Thyago Duque | Oscar Täckström | Jakob Uszkoreit | Dipanjan Das
Proceedings of the First Workshop on Subword and Character Level Models in NLP

We present a solution to the problem of paraphrase identification of questions. We focus on a recent dataset of question pairs annotated with binary paraphrase labels and show that a variant of the decomposable attention model (replacing the word embeddings of the decomposable attention model of Parikh et al. 2016 with character n-gram representations) results in accurate performance on this task, while being far simpler than many competing neural architectures. Furthermore, when the model is pretrained on a noisy dataset of automatically collected question paraphrases, it obtains the best reported performance on the dataset.