NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features

Piotr Przybyla, Nhung Nguyen, Matthew Shardlow, Georgios Kontonatsios, Sophia Ananiadou

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

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Abstract

We present a description of the system submitted
to the Semantic Textual Similarity (STS)
shared task at SemEval 2016. The task is
to assess the degree to which two sentences
carry the same meaning. We have designed
two different methods to automatically compute
a similarity score between sentences. The
first method combines a variety of semantic
similarity measures as features in a machine
learning model. In our second approach, we
employ training data from the Interpretable
Similarity subtask to create a combined wordsimilarity
measure and assess the importance
of both aligned and unaligned words. Finally,
we combine the two methods into a single hybrid
model. Our best-performing run attains
a score of 0:7732 on the 2015 STS evaluation
data and 0:7488 on the 2016 STS evaluation
data.
Original languageEnglish
Title of host publicationProceedings of the 10th International Workshop on Semantic Evaluation
Publication statusPublished - 2016

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