Articles

This paper presents our approach towards the SemEval-2016 Task 10 – Detecting Minimal Semantic Units and their Meanings. Systems are expected to provide a representation of lex- ical semantics by (1) segmenting tokens into words and multiword units and (2) providing a supersense tag for segments that function as nouns …...

In this paper, a system for semantic tex- tual similarity, which participated in Task- 1 in SemEval 2016 (monolingual and cross- lingual sub-tasks) is described. The system contains a preprocessing step that simplifies text using PPDB 2.0 and detects negations. Also, six lexical similarity functions were constructed using string matching, …...

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 feature- based system that leverages word alignment and machine translation quality evaluation metrics, two end-to-end LSTM-based sys- tems, and an ensemble system. The LSTM- …...

In this paper we consider several approaches to predicting semantic textual similarity us- ing word embeddings, as well as methods for forming embeddings for larger units of text. We compare these methods to several base- lines, and find that none of them outperform the baselines. We then consider both a …...

We present a multi-feature system for comput- ing the semantic similarity between two sen- tences. We introduce the use of soft align- ment for computing text similarity, and also evaluate different methods to produce it. The main features used by our system are based on alignment and Explicit Semantic Analysis. …...

This paper describes the SOPA-N system used by the LIPN-IIMAS team in Semeval 2016 Semantic Textual Similarity (Task 1). We based our work on the SOPA 2015 system. The SOPA-2015 system used 16 similarity features (including Wordnet, Information Re- trieval and Syntactic Dependencies) within a Random Forest learning model. We …...

This paper presents the submission of our team (NORMAS) to the SemEval 2016 se- mantic 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 …...

Semantic Textual Similarity measures similar- ity between pair of texts, even though the sim- ilar context is projected using different words. This work attempted to incorporate the con- text space of the sentence from that sentence alone. It proposes combination of Word2Vec and Non-Negative Matrix Factorization to rep- resent the …...

In this paper we describe the JUNITMZ 1 sys- tem that was developed for participation in Se- mEval 2016 Task 1: Semantic Textual Simi- larity. Methods for measuring the textual sim- ilarity are useful to a broad range of applica- tions including: text mining, information re- trieval, dialogue systems, machine …...

This paper describes the system for rating the degree of semantic equivalence between two text snippets developed by IHS-RD-Belarus for the SemEval 2016 STS shared task (Task 1). To predict the human ratings of text simi- larity we use a support vector regression mod- el with multiple features representing similari- …...

A growing body of research has recently been conducted on semantic textual similarity using a variety of neural network models. While re- cent research focuses on word-based represen- tation for phrases, sentences and even paragraphs, this study considers an alternative approach based on character n-grams. We generate embeddings for character …...

This paper describes our IR (Information Re- trieval) based method for SemEval 2016 task 1, Semantic Textual Similarity (STS). The main feature of our approach is to extend a conventional IR-based scheme by incorporat- ing word alignment information. This enables us to develop a more fine-grained similarity measurement. In the …...

Given two sentences, participating systems assign a semantic similarity score in the range of 0-5. We applied two different techniques for the task: one is based on lexical semantic net (corresponding to run 1) and the other is based on deep learning semantic model (corresponding to run 2). We also …...

We present in this paper a system for measur- ing Semantic Text Similarity (STS) in English. We introduce three novel techniques: the use of Types, methods of linking phrases, and the use of a Surprise Factor to generate 8,370 sim- ilarity measures, which we then combine us- ing Support Vector …...

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 se- mantics and shallow semantic structures of the source and target fragments, combined using a …...

We present a matrix factorization model for learning cross-lingual representations for sentences. Using sentence-aligned corpora, the proposed model learns distributed repre- sentations by factoring the given data into language-dependent factors and one shared factor. As a result, input sentences from both languages can be mapped into fixed-length vectors and then …...

We experiment with learning word representa- tions designed to be combined into sentence- level semantic representations, using an ob- jective function which does not directly make use of the supervised scores provided with the training data, instead opting for a simpler objective which encourages similar phrases to be close together …...

We describe a set of systems submitted to the SemEval-2016 English Semantic Textual Sim- ilarity (STS) task. Given two English sen- tences, the task is to compute the degree of their semantic similarity. Each of our systems uses the SemEval 2012–2015 STS datasets to train a ridge regression model that …...

This paper describes our system developed for English Monolingual subtask (STS Core) of SemEval-2016 Task 1: “Semantic Textual Similarity: A Unified Framework for Seman- tic Processing and Evaluation”. We measure the similarity between two sentences using three different types of features, includ- ing word alignment-based similarity, sentence vector-based similarity and …...

This paper describes three unsupervised sys- tems for determining the semantic similarity between two short texts or sentences submit- ted to the SemEval 2016 Task 1, all of which make use of only off-the-shelf software and data making them easy to replicate. Two systems achieved a similar Pearson correlation coefficient …...