One of the principal objectives of Natural Language Processing (NLP) is to generate
meaningful representations from text. Improving the informativeness of the representations …

This paper presents our submissions for semantic textual similarity task in SemEval 2016.
Based on several traditional features (ie, string-based, corpus-based, machine translation …

This paper presents three systems for semantic textual similarity (STS) evaluation at
SemEval-2017 STS task. One is an unsupervised system and the other two are supervised …

This paper describes our convolutional neural network (CNN) system for Semantic Textual
Similarity (STS) task. We calculated semantic similarity score between two sentences by …

Semantic textual similarity is the task of determining how similar two texts are. In this study,
we present the first Turkish evaluation benchmark dataset for semantic textual similarity. We …

This paper describes the SimiHawk system submission from UMass Lowell for the core
Semantic Textual Similarity task at SemEval-2016. We built four systems: a small …

This paper describes a simple neural network system for Semantic Textual Similarity (STS)
task. The basic type of the system took part in the STS task of SemEval 2017 and ranked 3rd …

This paper summarizes the latest research progress on semantic textual similarity
calculation methods, including string-based, statistics-based, knowledge-based, and deep …

The Semantic Textual Similarity (STS) problem measures the degree to which two text
fragments are similar. In QA systems, it is a crucial tool for extracting useful information from …

Semantic similarity analysis and modeling is a fundamentally acclaimed task in many
pioneering applications of natural language processing today. Owing to the sensation of …