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Found 19 papers, 6 papers with code
Towards Bengali WordNet Enrichment using Knowledge Graph Completion Techniques
Even though for languages like English, the existing WordNet is reasonably rich in terms of coverage, for resource-poor languages like Bengali, the WordNet is far from being reasonably sufficient in terms of coverage of vocabulary and relations between them.
Enriching Hindi WordNet Using Knowledge Graph Completion Approach
Even though the use of WordNet in the Natural Language Processing domain is unquestionable, creating and maintaining WordNet is a cumbersome job and it is even difficult for low resource languages like Hindi.
Sentiment Analysis For Bengali Using Transformer Based Models
Sentiment analysis is one of the key Natural Language Processing (NLP) tasks that has been attempted by researchers extensively for resource-rich languages like English.
Error Analysis of using BART for Multi-Document Summarization: A Study for English and German Language
Recent research using pre-trained language models for multi-document summarization task lacks deep investigation of potential erroneous cases and their possible application on other languages.
An Investigation towards Differentially Private Sequence Tagging in a Federated Framework
In this paper, we investigate the applicability of a privacy-preserving framework for sequence tagging tasks, specifically NER.
Pruning Literals for Highly Efficient Explainability at Word Level
Designing an explainable model becomes crucial now for Natural Language Processing(NLP) since most of the state-of-the-art machine learning models provide a limited explanation for the prediction.
CrowdCounter: A benchmark type-specific multi-target counterspeech dataset
We evaluate two frameworks for generating counterspeech responses - vanilla and type-controlled prompts - across four large language models.
On Zero-Shot Counterspeech Generation by LLMs
In terms of prompting, we find that our proposed strategies help in improving counter speech generation across all the models.
Natural Language Processing in the Legal Domain
To support our analysis, we construct and analyze a nearly complete corpus of more than six hundred NLP & Law related papers published over the past decade.
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English
Laws and their interpretations, legal arguments and agreements\ are typically expressed in writing, leading to the production of vast corpora of legal text.
Ranked #1 on
Natural Language Understanding
on LexGLUE
Using Distributional Thesaurus Embedding for Co-hyponymy Detection
Discriminating lexical relations among distributionally similar words has always been a challenge for natural language processing (NLP) community.
Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs
Specifically, we experiment with fusing embeddings obtained from knowledge graph with the state-of-the-art approaches for NLI task (ESIM model).
On the Compositionality Prediction of Noun Phrases using Poincar\'e Embeddings
In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincar{\'e} embeddings in addition to the distributional information to detect compositionality for noun phrases.
On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings
In particular, we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincar\'e embeddings in addition to the distributional information to detect compositionality for noun phrases.
Detecting Reliable Novel Word Senses: A Network-Centric Approach
The outlined method can therefore be used as a new post-hoc step to improve the precision of novel word sense detection in a robust and reliable way where the underlying framework uses a graph structure.
WikiRef: Wikilinks as a route to recommending appropriate references for scientific Wikipedia pages
In this paper, we propose a novel two step approach -- WikiRef -- that (i) leverages the wikilinks present in a scientific Wikipedia target page and, thereby, (ii) recommends highly relevant references to be included in that target page appropriately and automatically borrowed from the reference section of the wikilinks.
Can Network Embedding of Distributional Thesaurus be Combined with Word Vectors for Better Representation?
), we turn a distributional thesaurus network into dense word vectors and investigate the usefulness of distributional thesaurus embedding in improving overall word representation.
Network Features Based Co-hyponymy Detection
Distinguishing lexical relations has been a long term pursuit in natural language processing (NLP) domain.
