@inproceedings{mullick-etal-2022-using,
title = "Using Sentence-level Classification Helps Entity Extraction from Material Science Literature",
author = "Mullick, Ankan and
Pal, Shubhraneel and
Nayak, Tapas and
Lee, Seung-Cheol and
Bhattacharjee, Satadeep and
Goyal, Pawan",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.483",
pages = "4540--4545",
abstract = "In the last few years, several attempts have been made on extracting information from material science research domain. Material Science research articles are a rich source of information about various entities related to material science such as names of the materials used for experiments, the computational software used along with its parameters, the method used in the experiments, etc. But the distribution of these entities is not uniform across different sections of research articles. Most of the sentences in the research articles do not contain any entity. In this work, we first use a sentence-level classifier to identify sentences containing at least one entity mention. Next, we apply the information extraction models only on the filtered sentences, to extract various entities of interest. Our experiments for named entity recognition in the material science research articles show that this additional sentence-level classification step helps to improve the F1 score by more than 4{\%}.",
}
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%0 Conference Proceedings
%T Using Sentence-level Classification Helps Entity Extraction from Material Science Literature
%A Mullick, Ankan
%A Pal, Shubhraneel
%A Nayak, Tapas
%A Lee, Seung-Cheol
%A Bhattacharjee, Satadeep
%A Goyal, Pawan
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F mullick-etal-2022-using
%X In the last few years, several attempts have been made on extracting information from material science research domain. Material Science research articles are a rich source of information about various entities related to material science such as names of the materials used for experiments, the computational software used along with its parameters, the method used in the experiments, etc. But the distribution of these entities is not uniform across different sections of research articles. Most of the sentences in the research articles do not contain any entity. In this work, we first use a sentence-level classifier to identify sentences containing at least one entity mention. Next, we apply the information extraction models only on the filtered sentences, to extract various entities of interest. Our experiments for named entity recognition in the material science research articles show that this additional sentence-level classification step helps to improve the F1 score by more than 4%.
%U https://aclanthology.org/2022.lrec-1.483
%P 4540-4545
Markdown (Informal)
[Using Sentence-level Classification Helps Entity Extraction from Material Science Literature](https://aclanthology.org/2022.lrec-1.483) (Mullick et al., LREC 2022)
ACL