TY  - JOUR
T1  - An Extended Model for Semantic Role Labeling Using Word Sense
Disambiguation and Dependency Parsing
AU - Veena, G. AU - R. Pillai, Lekshmi AU - Gupta, Deepa 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 24
SP  - 7508
EP  - 7513
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.7508.7513
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.7508.7513
KW  - Word sense disambiguation
KW  -dependency parsing
KW  -semantic role labeling
KW  -conditional random field
KW  -WordNet
KW  -SVM
KW  -PractNLP tool
AB  - In NLP, the main two fundamental tasks are Word Sense Disambiguation (WSD) and Semantic Role
Labeling (SRL). Semantic role labeling is used to label the arguments in the sentence with the help of predicates.
Word sense disambiguation is the procedure of predicting the correct definition of a word in a given context.
In the research, we improved SRL using WSD and dependency parsing. The dependency parser helps to
improve the semantic relationship between the predicates and its arguments. A modified Conditional Random
Field (CRF) is used to bind dependency parser with SRL. We have used SVM classifier for WSD and PractNLP
tool is used for dependency parser. The model is evaluated and compared with an online WSD with SRL tool.
From the results obtained with the aid of our proposals, the labeling performs much better than a tool.
ER  -