Papers by Rajdeep Sarkar
Unsupervised Deep Language and Dialect Identification for Short Texts (2020.coling-main)
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| Challenge: | Existing methods for identifying closely related short texts are unsupervised . however, performance is poor for unsupervised methods for short texts . |
| Approach: | They propose a method which can learn sentence embeddings and cluster assignments from short texts. |
| Outcome: | The proposed method outperforms state-of-the-art methods in supervised settings . it can learn sentence embeddings and cluster assignments from short texts . |
A supervised approach to taxonomy extraction using word embeddings (L18-1)
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| Challenge: | a recent evaluation of a method for organizing texts into a hierarchy showed that it did not outperform a baseline. |
| Approach: | They propose a method that uses supervised learning to combine multiple features with a support vector machine classifier including the baseline features. |
| Outcome: | The proposed method outperforms the baseline method and provides stronger method for identifying taxonomic relations than previous methods. |
Suggest me a movie for tonight: Leveraging Knowledge Graphs for Conversational Recommendation (2020.coling-main)
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| Challenge: | Recent studies show that knowledge graphs are incomplete since they do not contain all factual information present on the web. |
| Approach: | They propose to use knowledge graphs to improve the performance of conversational recommender systems by incorporating pre-trained embeddings from subgraphs and positional embeddments into their models. |
| Outcome: | The proposed method improves by 5.62% over the state-of-the-art method on multiple metrics on the recommendation task. |
A Hybrid Approach to Aspect Based Sentiment Analysis Using Transfer Learning (2024.lrec-main)
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| Challenge: | Aspect-Based Sentiment Analysis (ABSA) aims to identify terms or multiword expressions (MWEs) on which sentiments are expressed and the sentiment polarities associated with them. |
| Approach: | They propose a hybrid approach to Aspect-Based Sentiment Analysis using transfer learning . they exploit the strengths of large language models and traditional syntactic dependencies . |
| Outcome: | The proposed method exploits the strengths of large language models and traditional syntactic dependencies. |