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PhD Thesis: Computational Model for Semantic Textual Similarity (A. Gonzalez, 2017/07/07)

Title: Computational Model for Semantic Textual Similarity
Author: Aitor Gonzalez-Agirre
Supervisors: German Rigau i Claramunt  / Eneko Agirre Bengoa (Ixa Group)
Date: July 7, 2017, Friday
Time: 11:00
Where:  Faculty of Informatics, Ada Lovelace Room (UPV/EHU)


The goal is to advance on computational models of meaning and their evaluation. We define two tasks: Semantic Textual Similarity (STS) and Typed Similarity.

STS aims to measure the degree of semantic equivalence between two sentences. We have collected pairs of sentences to construct datasets for STS, a total of 15,436 pairs of sentences, being by far the largest collection of data for STS.  We have designed, constructed and evaluated a new approach to combine knowledge-based and corpus-based methods using a cube.

Typed Similarity tries to identify the type of relation that holds between a pair of similar items in a digital library. Providing a reason why items are similar has applications in recommendation, personalization, and search. A range of types of similarity in this collection were identified and a set of 1,500 pairs of items from the collection were annotated using crowdsourcing.

We present systems that resolve the Typed Similarity task.

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