Child-Sum (N2E2N)Tree-LSTMs: An interactive Child-Sum Tree-LSTMs to extract biomedical event

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AVR7IN25K" target="_blank" >RIV/00216208:11320/25:VR7IN25K - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185519892&doi=10.1016%2fj.sasc.2024.200075&partnerID=40&md5=bcd3a867cc8d5b412c430bc1e1b7a589" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185519892&doi=10.1016%2fj.sasc.2024.200075&partnerID=40&md5=bcd3a867cc8d5b412c430bc1e1b7a589</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.sasc.2024.200075" target="_blank" >10.1016/j.sasc.2024.200075</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Child-Sum (N2E2N)Tree-LSTMs: An interactive Child-Sum Tree-LSTMs to extract biomedical event

Popis výsledku v původním jazyce

LSTM has been presented to overcome the problem of the gradient vanishing and explosion. Tree-LSTM could improve the parallel speed of LSTM, and incorporate relevant information from dependency or syntax trees. Tree-LSTM can update gate and memory vectors from the multiple sub-units. Learning edge features can strengthen the expression ability of graph neural networks. However, the original Child-Sum Tree-LSTMs ignores edge features during aggregating the sub-nodes hidden states. To enhance node representation, we propose an interaction mechanism that can alternately updating nodes and edges vectors, thus the model can learn the richer nodes vectors. The interaction mechanism attaches the node embedding to its connected link at the first stage. Next, it superimposes the updated edge into the parent node once more. Repeat the above steps from bottom to top. We present five strategies during the alternant renewal process. Meanwhile, we adopt one constituent parser and one dependency parser to produce the diversified formats, and compare their performances in the experiment result. The proposed model achieves better performance than baseline methods on the BioNLP'09 and MLEE corpuses. The experimental results show that the simple event results are almost identical for each parser. But for complex events, Stanford Parser is better than MaltParser because of more frequent interactive behaviors. The different parsing formats have different results, and CoNLL'2008 Dependencies show competitive and superior performance for each parser. © 2024

Název v anglickém jazyce

Child-Sum (N2E2N)Tree-LSTMs: An interactive Child-Sum Tree-LSTMs to extract biomedical event

Popis výsledku anglicky

LSTM has been presented to overcome the problem of the gradient vanishing and explosion. Tree-LSTM could improve the parallel speed of LSTM, and incorporate relevant information from dependency or syntax trees. Tree-LSTM can update gate and memory vectors from the multiple sub-units. Learning edge features can strengthen the expression ability of graph neural networks. However, the original Child-Sum Tree-LSTMs ignores edge features during aggregating the sub-nodes hidden states. To enhance node representation, we propose an interaction mechanism that can alternately updating nodes and edges vectors, thus the model can learn the richer nodes vectors. The interaction mechanism attaches the node embedding to its connected link at the first stage. Next, it superimposes the updated edge into the parent node once more. Repeat the above steps from bottom to top. We present five strategies during the alternant renewal process. Meanwhile, we adopt one constituent parser and one dependency parser to produce the diversified formats, and compare their performances in the experiment result. The proposed model achieves better performance than baseline methods on the BioNLP'09 and MLEE corpuses. The experimental results show that the simple event results are almost identical for each parser. But for complex events, Stanford Parser is better than MaltParser because of more frequent interactive behaviors. The different parsing formats have different results, and CoNLL'2008 Dependencies show competitive and superior performance for each parser. © 2024

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

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

2024

Kód důvěrnosti údajů

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

Název periodika

Systems and Soft Computing

ISSN

2772-9419

e-ISSN

—

Svazek periodika

6

Číslo periodika v rámci svazku

2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

1-9

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85185519892