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20172025
most citedApplying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation

12 citations · 34 across the 14 of their papers we have counts for

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19 papers · 1 filter

cs.CL20221 cited

KnowGL: Knowledge Generation and Linking from Text

Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Nandana Mihindukulasooriya +2

We propose KnowGL, a tool that allows converting text into structured relational data represented as a set of ABox assertions compliant with the TBox of a given Knowledge Graph (KG…

cs.CL20228 cited

End-to-End Table Question Answering via Retrieval-Augmented Generation

Feifei Pan, Mustafa Canim, Michael Glass +2

Most existing end-to-end Table Question Answering (Table QA) models consist of a two-stage framework with a retriever to select relevant table candidates from a corpus and a reader…

cs.CL20222 cited

A Generative Model for Relation Extraction and Classification

Jian Ni, Gaetano Rossiello, Alfio Gliozzo +1

Relation extraction (RE) is an important information extraction task which provides essential information to many NLP applications such as knowledge base population and question an…

cs.CL202212 cited

Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation

Md Faisal Mahbub Chowdhury, Gaetano Rossiello, Michael Glass +2

In recent years, a number of keyphrase generation (KPG) approaches were proposed consisting of complex model architectures, dedicated training paradigms and decoding strategies. In…

cs.CL2022

Semantic Answer Type and Relation Prediction Task (SMART 2021)

Nandana Mihindukulasooriya, Mohnish Dubey, Alfio Gliozzo +5

Each year the International Semantic Web Conference organizes a set of Semantic Web Challenges to establish competitions that will advance state-of-the-art solutions in some proble…

cs.CL2021

Topic Transferable Table Question Answering

Saneem Ahmed Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj +5

Weakly-supervised table question-answering(TableQA) models have achieved state-of-art performance by using pre-trained BERT transformer to jointly encoding a question and a table t…