UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information
نویسندگان
چکیده
We present our UWB system for Semantic Textual Similarity (STS) task at SemEval 2016. Given two sentences, the system estimates the degree of their semantic similarity. We use state-of-the-art algorithms for the meaning representation and combine them with the best performing approaches to STS from previous years. These methods benefit from various sources of information, such as lexical, syntactic, and semantic. In the monolingual task, our system achieve mean Pearson correlation 75.7% compared with human annotators. In the cross-lingual task, our system has correlation 86.3% and is ranked first among 26 systems.
منابع مشابه
CNRC at SemEval-2016 Task 1: Experiments in Crosslingual Semantic Textual Similarity
We describe the systems entered by the National Research Council Canada in the SemEval-2016 Task1: Crosslingual Semantic Textual Similarity. We tried two approaches: One computes a true crosslingual similarity based on features extracted from lexical semantics and shallow semantic structures of the source and target fragments, combined using a linear model. The other approach relies on Statisti...
متن کاملRev at SemEval-2016 Task 2: Aligning Chunks by Lexical, Part of Speech and Semantic Equivalence
We present the description of our submission to SemEval-2016 Task 2, for the sub-task of aligning pre-annotated chunks between sentence pairs and providing similarity and relatedness labels for the alignment. The objective of the task is to provide interpretable semantic textual similarity assessments by adding an explanatory layer to aligned chunks. We analysed the provided datasets, consideri...
متن کاملNORMAS at SemEval-2016 Task 1: SEMSIM: A Multi-Feature Approach to Semantic Text Similarity
This paper presents the submission of our team (NORMAS) to the SemEval 2016 semantic textual similarity (STS) shared task. We submitted three system runs, each using a set of 36 features extracted from the training set. The runs explore the use of the following three machine learning algorithms: Support Vector Regression, Elastic Net and Random Forest. Each run was trained using sentence pairs ...
متن کاملUWB at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity with Distributional Semantics for Chunks
We introduce a system focused on solving SemEval 2016 Task 2 – Interpretable Semantic Textual Similarity. The system explores machine learning and rule-based approaches to the task. We focus on machine learning and experiment with a wide variety of machine learning algorithms as well as with several types of features. The core of our system consists in exploiting distributional semantics to com...
متن کاملUOW: Semantically Informed Text Similarity
The UOW submissions to the Semantic Textual Similarity task at SemEval-2012 use a supervised machine learning algorithm along with features based on lexical, syntactic and semantic similarity metrics to predict the semantic equivalence between a pair of sentences. The lexical metrics are based on wordoverlap. A shallow syntactic metric is based on the overlap of base-phrase labels. The semantic...
متن کامل