Search Results for author: Sudeshna Sarkar

Found 21 papers, 1 papers with code

A Graph Convolution Network-based System for Technical Domain Identification

no code implementations ICON 2020 Alapan Kuila, Ayan Das, Sudeshna Sarkar

This paper presents the IITKGP contribution at the Technical DOmain Identification (TechDOfication) shared task at ICON 2020.

Classification POS +1

A little perturbation makes a difference: Treebank augmentation by perturbation improves transfer parsing

no code implementations ICON 2019 Ayan Das, Sudeshna Sarkar

We present an approach for cross-lingual transfer of dependency parser so that the parser trained on a single source language can more effectively cater to diverse target languages.

Cross-Lingual Transfer

ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument Aggregation

1 code implementation ACL (CASE) 2021 Debanjana Kar, Sudeshna Sarkar, Pawan Goyal

Most of the existing information extraction frameworks (Wadden et al., 2019; Veysehet al., 2020) focus on sentence-level tasks and are hardly able to capture the consolidated information from a given document.

Active Learning Event Argument Extraction

Event Argument Extraction using Causal Knowledge Structures

no code implementations ICON 2020 Debanjana Kar, Sudeshna Sarkar, Pawan Goyal

We develop a causal network for our event-annotated dataset by extracting relevant event causal structures from ConceptNet and phrases from Wikipedia.

Event Argument Extraction

A Robust Fuel Optimization Strategy For Hybrid Electric Vehicles: A Deep Reinforcement Learning Based Continuous Time Design Approach

no code implementations1 Jan 2021 Nilanjan Mukherjee, Sudeshna Sarkar

This is followed by the design of a deep reinforcement learning based optimal control law for the non-linear system (i. e., hybrid electric vehicles) such that the actual states and the control policy remain close to the optimal trajectory and optimal policy even in the presence of external disturbances, modeling errors, uncertainties and noise.

reinforcement-learning

Medical Entity Linking using Triplet Network

no code implementations WS 2019 Ishani Mondal, Sukannya Purkayastha, Sudeshna Sarkar, Pawan Goyal, Jitesh Pillai, Amitava Bhattacharyya, Mahanandeeshwar Gattu

Entity linking (or Normalization) is an essential task in text mining that maps the entity mentions in the medical text to standard entities in a given Knowledge Base (KB).

Entity Linking

Improving cross-lingual model transfer by chunking

no code implementations27 Feb 2020 Ayan Das, Sudeshna Sarkar

We present a shallow parser guided cross-lingual model transfer approach in order to address the syntactic differences between source and target languages more effectively.

Chunking

Development of a Bengali parser by cross-lingual transfer from Hindi

no code implementations WS 2016 Ayan Das, Agnivo Saha, Sudeshna Sarkar

A parser is trained and applied to the Hindi sentences of the parallel corpus and the parse trees are projected to construct probable parse trees of the corresponding Bengali sentences.

Chunking Cross-Lingual Transfer

Query Translation for Cross-Language Information Retrieval using Multilingual Word Clusters

no code implementations WS 2016 Paheli Bhattacharya, Pawan Goyal, Sudeshna Sarkar

In Cross-Language Information Retrieval, finding the appropriate translation of the source language query has always been a difficult problem to solve.

Community Detection Information Retrieval +3

UsingWord Embeddings for Query Translation for Hindi to English Cross Language Information Retrieval

no code implementations4 Aug 2016 Paheli Bhattacharya, Pawan Goyal, Sudeshna Sarkar

In this paper, we propose an approach based on word embeddings, a method that captures contextual clues for a particular word in the source language and gives those words as translations that occur in a similar context in the target language.

Information Retrieval Translation +2

Cannot find the paper you are looking for? You can Submit a new open access paper.