Publication Type:

Conference Paper

Source:

2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Udupi, India (2017)

URL:

https://ieeexplore.ieee.org/document/8125818

Keywords:

coreference resolution, document handling, document level, Feature extraction, Labeling, learning (artificial intelligence), learning method, Machine learning, machine learning technique, NAtural language processing, Natural language processing systems, nonmachine learning technique, noun phrase coreference resolution, Pattern classification, Semantic features, semantic role labeling, semantic role labeling features, Semantics, SRL, Support vector machines, SVM classifier, syntatic features, Testing, Training

Abstract:

Coreference resolution plays a significant role in natural language processing systems. It is the method of figuring out all the noun phrases that refer back to the identical real world entity. Several researches have been done in noun phrase coreference resolution by using certain machine learning techniques. Our paper proposes a machine learning approach using support vector machines (SVM) towards coreference resolution at document level. In this research work, 17 well-defined syntactic and semantic features including the 13 baseline features with Semantic Role labeling (SRL) have been used. The use of SVM classifier leads to a better outcome when compared to other machine learning models. The system was evaluated using a machine learning technique and a non-machine learning technique. Experiments show that addition of SRL improves the performance of the system when compared to the Decision tree model and a non machine learning approach.

Cite this Research Publication

G. Veena, Dr. Deepa Gupta, Daniel, A. N., and Roshny, S., “A learning method for coreference resolution using semantic role labeling features”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.