Temporal Ordering of Events via Deep Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10439915" target="_blank" >RIV/00216208:11320/21:10439915 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-030-86331-9_49" target="_blank" >https://doi.org/10.1007/978-3-030-86331-9_49</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-86331-9_49" target="_blank" >10.1007/978-3-030-86331-9_49</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Temporal Ordering of Events via Deep Neural Networks

Popis výsledku v původním jazyce

Ordering events with temporal relations in texts remains a challenge in natural language processing. In this paper, we introduce a new combined neural network architecture that is capable of classifying temporal relations between events in an Arabic sentence. Our model consists of two branches: the first one extracts the syntactic information and identifies the orientation of the relation between the two given events based on a Shortest Dependency Path (SDP) layer with Long and Short Memory (LSTM), and the second one encourages the model to focus on the important local information when learning sentence representations based on a Bidirectional-LSTM (BiLSTM) attention layer. The experiments suggest that our proposed model outperforms several previous state-of-the-art methods, with an F1-score equal to 86.40%.

Název v anglickém jazyce

Temporal Ordering of Events via Deep Neural Networks

Popis výsledku anglicky

Ordering events with temporal relations in texts remains a challenge in natural language processing. In this paper, we introduce a new combined neural network architecture that is capable of classifying temporal relations between events in an Arabic sentence. Our model consists of two branches: the first one extracts the syntactic information and identifies the orientation of the relation between the two given events based on a Shortest Dependency Path (SDP) layer with Long and Short Memory (LSTM), and the second one encourages the model to focus on the important local information when learning sentence representations based on a Bidirectional-LSTM (BiLSTM) attention layer. The experiments suggest that our proposed model outperforms several previous state-of-the-art methods, with an F1-score equal to 86.40%.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

978-3-030-86330-2

ISSN

—

e-ISSN

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Počet stran výsledku

16

Strana od-do

762-777

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

Lausanne

Datum konání akce

5. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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