Articles

In this paper we describe our system (DT- Sim) submitted at SemEval-2016 Task 1: Se- mantic Textual Similarity (STS Core). We developed Support Vector Regression model with various features including the similarity scores calculated using alignment based meth- ods and semantic composition based methods. The correlations between our system output …...

This paper describes the WOLVESAAR sys- tems that participated in the English Seman- tic Textual Similarity (STS) task in SemEval- 2016. We replicated the top systems from the last two editions of the STS task and ex- tended the model using GloVe word embed- dings and dense vector space LSTM …...

This paper describes the SAARSHEFF sys- tems that participated in the English Seman- tic Textual Similarity (STS) task in SemEval- 2016. We extend the work on using ma- chine translation (MT) metrics in the STS task by automatically annotating the STS datasets with a variety of MT scores for each …...

This paper presents our submissions for se- mantic textual similarity task in SemEval 2016. Based on several traditional fea- tures (i.e., string-based, corpus-based, ma- chine translation similarity and alignmen- t metrics), we leverage word embedding from macro (i.e., first get representation of sen- tence, then measure the similarity of sentence …...

We present a description of the system submit- ted 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 com- pute a similarity score between sentences. …...

This paper describes our proposed solutions designed for a STS core track within the Se- mEval 2016 English Semantic Textual Sim- ilarity (STS) task. Our method of similar- ity detection combines recursive autoencoders with a WordNet award-penalty system that ac- counts for semantic relatedness, and an SVM classifier, which produces …...

This paper describes our participation in the SemEval-2016 Task 1: Semantic Textual Sim- ilarity (STS). We developed three methods for the English subtask (STS Core). The first method is unsupervised and uses WordNet and word2vec to measure a token-based overlap. In our second approach, we train a neural net- work …...

In this paper, we propose a deep neural net- work based natural language processing sys- tem for semantic textual similarity prediction. We leverage multi-layer bidirectional LSTM to learn sentence representation. After that, we construct matching features followed by High- way Multilayer Perceptron to make predic- tions. Experimental results demonstrate that …...

We present our UWB system for Seman- tic Textual Similarity (STS) task at SemEval 2016. Given two sentences, the system esti- mates 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 …...

VRep is a system designed for SemEval 2016 Task 1 - Semantic Textual Similarity (STS) and Task 2 - Interpretable Semantic Textual Similarity (iSTS). STS quantifies the seman- tic equivalence between two snippets of text, and iSTS provides a reason why those snip- pets of text are similar. VRep makes …...

In this paper we describe our participa- tion in the STS Core subtask which is the determination of the monolingual seman- tic similarity between pair of sentences. In our participation we adapted state-of- the-art approaches from related work ap- plied on previous STS Core subtasks and run them on the …...

We report the findings of the Complex Word Identification task of SemEval 2016. To cre- ate a dataset, we conduct a user study with 400 non-native English speakers, and find that complex words tend to be rarer, less ambigu- ous and shorter. A total of 42 systems were submitted from …...

This paper describes the system by FBK HLT- MT for cross-lingual semantic textual similar- ity measurement. Our approach is based on supervised regression with an ensemble deci- sion tree. In order to assign a semantic similar- ity score to an input sentence pair, the model combines features collected by state-of-the-art …...

This task combines the labeling of multiword expressions and supersenses (coarse-grained classes) in an explicit, yet broad-coverage paradigm for lexical semantics. Nine sys- tems participated; the best scored 57.7% F1 in a multi-domain evaluation setting, indicating that the task remains largely unresolved. An error analysis reveals that a large number …...

This paper describes the SemEval–2016 Task 3 on Community Question Answer- ing, which we offered in English and Ara- bic. For English, we had three sub- tasks: Question–Comment Similarity (subtask A), Question–Question Similarity (B), and Question–External Comment Similarity (C). For Arabic, we had another subtask: Rerank the correct answers for …...

The final goal of Interpretable Semantic Tex- tual Similarity (iSTS) is to build systems that explain which are the differences and com- monalities between two sentences. The task adds an explanatory level on top of STS, for- malized as an alignment between the chunks in the two input sentences, indicating …...

Semantic Textual Similarity (STS) seeks to measure the degree of semantic equivalence between two snippets of text. Similarity is ex- pressed on an ordinal scale that spans from semantic equivalence to complete unrelated- ness. Intermediate values capture specifically defined levels of partial similarity. While prior evaluations constrained themselves to just …...

This paper describes our system submissions to task 7 in SemEval 2016, i.e., Determining Sentiment Intensity. We participated the first two subtasks in English, which are to predict the sentiment intensity of a word or a phrase in English Twitter and General English domains. To address this task, we present …...

This paper presents a model that was devel- oped to address SemEval Task 7: “Determin- ing Sentiment Intensity of English and Arabic Phrases”, with focus on ‘Arabic Phrases’. The goal of this task is to determine the degree to which some given term is associated with pos- itive sentiment. The …...

We present a novel method for determining sentiment intensity. The main goal is to as- sign a phrase a score from 0 to 1 which indi- cates the strength of its association with pos- itive sentiment. The proposed model uses a rich set of features with Gaussian pro- cesses regression …...