<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mondal-2020-bertchem">
    <titleInfo>
        <title>BERTChem-DDI : Improved Drug-Drug Interaction Prediction from text using Chemical Structure Information</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ishani</namePart>
        <namePart type="family">Mondal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Oren</namePart>
            <namePart type="given">Sar</namePart>
            <namePart type="family">Shalom</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alexander</namePart>
            <namePart type="family">Panchenko</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Cicero</namePart>
            <namePart type="family">dos Santos</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Varvara</namePart>
            <namePart type="family">Logacheva</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alessandro</namePart>
            <namePart type="family">Moschitti</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ido</namePart>
            <namePart type="family">Dagan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Suzhou, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Traditional biomedical version of embeddings obtained from pre-trained language models have recently shown state-of-the-art results for relation extraction (RE) tasks in the medical domain. In this paper, we explore how to incorporate domain knowledge, available in the form of molecular structure of drugs, for predicting Drug-Drug Interaction from textual corpus. We propose a method, BERTChem-DDI, to efficiently combine drug embeddings obtained from the rich chemical structure of drugs (encoded in SMILES) along with off-the-shelf domain-specific BioBERT embedding-based RE architecture. Experiments conducted on the DDIExtraction 2013 corpus clearly indicate that this strategy improves other strong baselines architectures by 3.4% macro F1-score.</abstract>
    <identifier type="citekey">mondal-2020-bertchem</identifier>
    <identifier type="doi">10.18653/v1/2020.knlp-1.4</identifier>
    <location>
        <url>https://aclanthology.org/2020.knlp-1.4</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>27</start>
            <end>32</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T BERTChem-DDI : Improved Drug-Drug Interaction Prediction from text using Chemical Structure Information
%A Mondal, Ishani
%Y Shalom, Oren Sar
%Y Panchenko, Alexander
%Y dos Santos, Cicero
%Y Logacheva, Varvara
%Y Moschitti, Alessandro
%Y Dagan, Ido
%S Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F mondal-2020-bertchem
%X Traditional biomedical version of embeddings obtained from pre-trained language models have recently shown state-of-the-art results for relation extraction (RE) tasks in the medical domain. In this paper, we explore how to incorporate domain knowledge, available in the form of molecular structure of drugs, for predicting Drug-Drug Interaction from textual corpus. We propose a method, BERTChem-DDI, to efficiently combine drug embeddings obtained from the rich chemical structure of drugs (encoded in SMILES) along with off-the-shelf domain-specific BioBERT embedding-based RE architecture. Experiments conducted on the DDIExtraction 2013 corpus clearly indicate that this strategy improves other strong baselines architectures by 3.4% macro F1-score.
%R 10.18653/v1/2020.knlp-1.4
%U https://aclanthology.org/2020.knlp-1.4
%U https://doi.org/10.18653/v1/2020.knlp-1.4
%P 27-32

Markdown (Informal)

[BERTChem-DDI : Improved Drug-Drug Interaction Prediction from text using Chemical Structure Information](https://aclanthology.org/2020.knlp-1.4) (Mondal, knlp 2020)

• BERTChem-DDI : Improved Drug-Drug Interaction Prediction from text using Chemical Structure Information (Mondal, knlp 2020)

ACL

• Ishani Mondal. 2020. BERTChem-DDI : Improved Drug-Drug Interaction Prediction from text using Chemical Structure Information. In Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP, pages 27–32, Suzhou, China. Association for Computational Linguistics.