Open AccessDomain‐Adaptation Technique for Semantic Role Labeling with Structural Learning
Author(s) -
Lim Soojong,
Lee Changki,
Ryu PumMo,
Kim Hyunki,
Park Sang Kyu,
Ra Dongyul
Publication year - 2014
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.14.0113.0645
Subject(s) - domain adaptation , computer science , domain (mathematical analysis) , adaptation (eye) , task (project management) , semantic role labeling , training set , artificial intelligence , labeled data , natural language processing , machine learning , mathematics , physics , management , sentence , classifier (uml) , optics , economics , mathematical analysis
Semantic role labeling (SRL) is a task in natural‐language processing with the aim of detecting predicates in the text, choosing their correct senses, identifying their associated arguments, and predicting the semantic roles of the arguments. Developing a high‐performance SRL system for a domain requires manually annotated training data of large size in the same domain. However, such SRL training data of sufficient size is available only for a few domains. Constructing SRL training data for a new domain is very expensive. Therefore, domain adaptation in SRL can be regarded as an important problem. In this paper, we show that domain adaptation for SRL systems can achieve state‐of‐the‐art performance when based on structural learning and exploiting a prior model approach. We provide experimental results with three different target domains showing that our method is effective even if training data of small size is available for the target domains. According to experimentations, our proposed method outperforms those of other research works by about 2% to 5% in F‐score.