Papers by Chiranjib Bhattacharyya
Word2Sense: Sparse Interpretable Word Embeddings (P19-1)
Copied to clipboard
| Challenge: | Word2Sense embeddings are interpretable, but they are sparse and fast to compute . a unitary rotation can be applied to many of these embeddables retaining their utility for computational tasks while changing the values of individual coordinates. |
| Approach: | They propose an unsupervised method to generate Word2Sense word embeddings that are interpretable. |
| Outcome: | The proposed method compares well with other unsupervised word embeddings on NLP tasks. |
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks (P19-1)
Copied to clipboard
Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar
| Challenge: | Existing word embedding methods utilize sequential context of a word to learn its embeddment, but such methods result in an explosion of the vocabulary size. |
| Approach: | They propose a flexible Graph Convolution based method for learning word embeddings that utilizes the dependency context of a word without increasing the vocabulary size. |
| Outcome: | The proposed model outperforms existing methods on intrinsic and extrinsic tasks and provides an advantage when used with ELMo. |
RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information (D18-1)
Copied to clipboard
| Challenge: | Distantly-supervised Relation Extraction (RE) methods ignore readily available side information. |
| Approach: | They propose a distantly-supervised neural relation extraction method which uses additional side information from KBs to train an extractor. |
| Outcome: | The proposed method improves performance even when limited side information is available. |