Sentence Similarity Estimator (SenSim)
This repository currently supports Python 2.7
For the used default values in sts.py/sts_light.py, you need the following:
sklearn==0.18
polyglot==16.07.04
Dependencies: (python-numpy libicu-dev)
(to use in ubuntu/debian) sudo apt-get install python-numpy libicu-dev
beard==0.2
digify==0.2
enchant==1.6.8
spacy==0.100.5
Needed models: python -m spacy.en.download glove
After cloning the repositpry, use sts.py with its documented arguments
After cloning the repositpry, use sts_benchmark.py with its default param
http://www.aclweb.org/anthology/S17-2013
@InProceedings{alnatsheh-EtAl:2017:SemEval,
author = {Al-Natsheh, Hussein T. and Martinet, Lucie and Muhlenbach, Fabrice and ZIGHED, Djamel Abdelkader},
title = {UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise Features},
booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
month = {August},
year = {2017},
address = {Vancouver, Canada},
publisher = {Association for Computational Linguistics},
pages = {115--119},
abstract = {This paper describes the model UdL we proposed to solve the semantic textual
similarity task of SemEval 2017 workshop. The track we participated in was
estimating the semantics relatedness of a given set of sentence pairs in
English. The best run out of three submitted runs of our model achieved a
Pearson correlation score of 0.8004 compared to a hidden human annotation of
250~pairs. We used random forest ensemble learning to map an expandable set of
extracted pairwise features into a semantic similarity estimated value bounded
between 0 and 5. Most of these features were calculated using word embedding
vectors similarity to align Part of Speech (PoS) and Name Entities (NE) tagged
tokens of each sentence pair. Among other pairwise features, we experimented a
classical tf-idf weighted Bag of Words (BoW) vector model but with
character-based range of n-grams instead of words. This sentence vector
BoW-based feature gave a relatively high importance value percentage in the
feature importances analysis of the ensemble learning.},
url = {http://www.aclweb.org/anthology/S17-2013}
}