no code implementations • ACL (CASE) 2021 • Parul Awasthy, Jian Ni, Ken Barker, Radu Florian
In this paper, we present the event detection models and systems we have developed for Multilingual Protest News Detection - Shared Task 1 at CASE 2021.
no code implementations • ACL (CASE) 2021 • Ken Barker, Parul Awasthy, Jian Ni, Radu Florian
The NLI reranker uses a textual representation of target types that allows it to score the strength with which a type is implied by a text, without requiring training data for the types.
no code implementations • FNP (LREC) 2022 • Anik Saha, Jian Ni, Oktie Hassanzadeh, Alex Gittens, Kavitha Srinivas, Bulent Yener
Causal information extraction is an important task in natural language processing, particularly in finance domain.
no code implementations • 14 Jan 2024 • Somin Wadhwa, Oktie Hassanzadeh, Debarun Bhattacharjya, Ken Barker, Jian Ni
In this work, we explore the use of Large Language Models (LLMs) to generate event sequences that can effectively be used for probabilistic event model construction.
1 code implementation • 29 Aug 2023 • Anik Saha, Oktie Hassanzadeh, Alex Gittens, Jian Ni, Kavitha Srinivas, Bulent Yener
Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercial solutions.
1 code implementation • 7 Aug 2023 • Anik Saha, Oktie Hassanzadeh, Alex Gittens, Jian Ni, Kavitha Srinivas, Bulent Yener
Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation.
no code implementations • 26 Feb 2022 • Jian Ni, Gaetano Rossiello, Alfio Gliozzo, Radu Florian
Relation extraction (RE) is an important information extraction task which provides essential information to many NLP applications such as knowledge base population and question answering.
no code implementations • 16 Oct 2020 • Jian Ni, Taesun Moon, Parul Awasthy, Radu Florian
Relation extraction (RE) is one of the most important tasks in information extraction, as it provides essential information for many NLP applications.
no code implementations • 15 Sep 2020 • Parul Awasthy, Tahira Naseem, Jian Ni, Taesun Moon, Radu Florian
The task of event detection and classification is central to most information retrieval applications.
no code implementations • 15 Sep 2020 • Parul Awasthy, Taesun Moon, Jian Ni, Radu Florian
Named Entity Recognition (NER) is an essential precursor task for many natural language applications, such as relation extraction or event extraction.
no code implementations • 19 Nov 2019 • Taesun Moon, Parul Awasthy, Jian Ni, Radu Florian
In this paper we investigate a single Named Entity Recognition model, based on a multilingual BERT, that is trained jointly on many languages simultaneously, and is able to decode these languages with better accuracy than models trained only on one language.
Ranked #1 on Cross-Lingual NER on CoNLL Dutch
no code implementations • IJCNLP 2019 • Jian Ni, Radu Florian
Relation extraction (RE) seeks to detect and classify semantic relationships between entities, which provides useful information for many NLP applications.
no code implementations • NeurIPS 2019 • Jian Ni, Shanghang Zhang, Haiyong Xie
In particular, the primal GAN learns to synthesize inter-class discriminative and semantics-preserving visual features from both the semantic representations of seen/unseen classes and the ones reconstructed by the dual GAN.
no code implementations • EMNLP 2016 • Jian Ni, Radu Florian
Experimental results show that the proposed approaches are effective in improving the accuracy of such systems on unseen entities, especially when a system is applied to a new domain or it is trained with little training data (up to 18. 3 F1 score improvement).
Multilingual Named Entity Recognition named-entity-recognition +3
no code implementations • ACL 2017 • Jian Ni, Georgiana Dinu, Radu Florian
However, annotating NER data by human is expensive and time-consuming, and can be quite difficult for a new language.
no code implementations • 25 Apr 2012 • Rui Wu, R. Srikant, Jian Ni
We consider the structure learning problem for graphical models that we call loosely connected Markov random fields, in which the number of short paths between any pair of nodes is small, and present a new conditional independence test based algorithm for learning the underlying graph structure.