Paper Info
Title
A Multi-Layer System for Semantic Textual Similarity.
Abstract
Building a system able to cope with various phenomena which falls under the umbrella of semantic similarity is far from trivial. It is almost always the case that the performances of a system do not vary consistently or predictably from corpora to corpora. We analyzed the source of this variance and found that it is related to the word-pair similarity distribution among the topics in the various corpora. Then we used this insight to construct a 4-module system that would take into consideration not only string and semantic word similarity, but also word alignment and sentence structure. The system consistently achieves an accuracy which is very close to the state of the art, or reaching a new state of the art. The system is based on a multi-layer architecture and is able to deal with heterogeneous corpora which may not have been generated by the same distribution.
Year
DOI
Venue
2016
10.5220/0006045800560067
KDIR
Keywords
Field
DocType
Machine Learning,Natural Language Processing (NLP),Semantic Textual Similarity (STS)
Semantic similarity,Data mining,Architecture,Multi layer,Information retrieval,Computer science,Artificial intelligence,Natural language processing,Sentence
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Ngoc Phuoc An Vo1139.04
Octavian Popescu27818.05