Semantic Textual Similarity 2017


Semantic textual similarity measures the degree of semantic equivalence between two texts.

  • Corpus similarity
  • Document similarity
  • Semantic similarity
  • Text similarity
  • Textual similarity


See also:

Cosine Similarity & Dialog Systems 2015

SemEval-2017 Task 1: Semantic Textual Similarity-Multilingual and Cross-lingual Focused Evaluation
D Cer, M Diab, E Agirre, I Lopez-Gazpio… – arXiv preprint arXiv …, 2017 –
Abstract: Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems.

Interpretable semantic textual similarity: Finding and explaining differences between sentences
I Lopez-Gazpio, M Maritxalar, A Gonzalez-Agirre… – Knowledge-Based …, 2017 – Elsevier
Abstract User acceptance of artificial intelligence agents might depend on their ability to explain their reasoning to the users. We focus on a specific text processing task, the Semantic Textual Similarity task (STS), where systems need to measure the degree of

HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate semantic textual similarity
Y Shao – … of the 11th International Workshop on Semantic …, 2017 –
Abstract This paper describes our convolutional neural network (CNN) system for the Semantic Textual Similarity (STS) task. We calculated semantic similarity score between two sentences by comparing their semantic vectors. We generated a semantic vector by max

CompiLIG at SemEval-2017 Task 1: Cross-language plagiarism detection methods for semantic textual similarity
J Ferrero, F Agnes, L Besacier, D Schwab – arXiv preprint arXiv …, 2017 –
Abstract: We present our submitted systems for Semantic Textual Similarity (STS) Track 4 at SemEval-2017. Given a pair of Spanish-English sentences, each system must estimate their semantic similarity by a score between 0 and 5. In our submission, we use syntax-based,

BIT at SemEval-2017 Task 1: Using semantic information space to evaluate semantic textual similarity
H Wu, H Huang, P Jian, Y Guo, C Su – … Workshop on Semantic …, 2017 –
Abstract 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 systems which simply employ the unsupervised one. All our systems mainly depend on the

LIPN-IIMAS at SemEval-2017 Task 1: Subword embeddings, attention recurrent neural networks and cross word alignment for semantic textual similarity
I Arroyo-Fernández, IVM Ruiz – … 11th International Workshop on Semantic …, 2017 –
Abstract In this paper we report our attempt to use, on the one hand, state-of-the-art neural approaches that are proposed to measure Semantic Textual Similarity (STS). On the other hand, we propose an unsupervised cross-word alignment approach, which is linguistically

PurdueNLP at SemEval-2017 Task 1: Predicting semantic textual similarity with paraphrase and event embeddings
IT Lee, M Goindani, C Li, D Jin, KM Johnson… – … Workshop on Semantic …, 2017 –
Abstract This paper describes our proposed solution for SemEval 2017 Task 1: Semantic Textual Similarity (Daniel Cer and Specia, 2017). The task aims at measuring the degree of equivalence between sentences given in English. Performance is evaluated by computing

… Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity
J Tian, Z Zhou, M Lan, Y Wu – … 11th International Workshop on Semantic …, 2017 –
Abstract To model semantic similarity for multilingual and cross-lingual sentence pairs, we first translate foreign languages into English, and then build an efficient monolingual English system with multiple NLP features. Our system is further supported by deep learning models

L2f/inesc-id at semeval-2017 tasks 1 and 2: Lexical and semantic features in word and textual similarity
P Fialho, HP Rodrigues, L Coheur… – … Workshop on Semantic …, 2017 –
Abstract This paper describes our approach to the SemEval-2017 “Semantic Textual Similarity” and “Multilingual Word Similarity” tasks. In the former, we test our approach in both English and Spanish, and use a linguistically-rich set of features. These move from

STS-UHH at SemEval-2017 Task 1: Scoring semantic textual similarity using supervised and unsupervised ensemble
S Kohail, AR Salama, C Biemann – … International Workshop on Semantic …, 2017 –
Abstract This paper reports the STS-UHH participation in the SemEval 2017 shared Task 1 of Semantic Textual Similarity (STS). Overall, we submitted 3 runs covering monolingual and cross-lingual STS tracks. Our participation involves two approaches: unsupervised

FCICU at SemEval-2017 Task 1: Sense-based language independent semantic textual similarity approach
B Hassan, S AbdelRahman, R Bahgat… – … Workshop on Semantic …, 2017 –
Abstract This paper describes FCICU team systems that participated in SemEval-2017 Semantic Textual Similarity task (Task1) for monolingual and cross-lingual sentence pairs. A sense-based language independent textual similarity approach is presented, in which a

ITNLP-AiKF at SemEval-2017 Task 1: Rich features based svr for semantic textual similarity computing
W Liu, C Sun, L Lin, B Liu – … the 11th International Workshop on Semantic …, 2017 –
Abstract Semantic Textual Similarity (STS) devotes to measuring the degree of equivalence in the underlying semantic of the sentence pair. We proposed a new system, ITNLP-AiKF, which applies in the SemEval 2017 Task1 Semantic Textual Similarity track 5 English

OPI-JSA at SemEval-2017 Task 1: Application of ensemble learning for computing semantic textual similarity
M ?piewak, P Sobecki, D Kara? – … International Workshop on Semantic …, 2017 –
Abstract Semantic Textual Similarity (STS) evaluation assesses the degree to which two parts of texts are similar, based on their semantic evaluation. In this paper, we describe three models submitted to STS SemEval 2017. Given two English parts of a text, each of proposed

UMDeep at SemEval-2017 Task 1: End-to-end shared weight LSTM model for semantic textual similarity
J Barrow, D Peskov – … of the 11th International Workshop on Semantic …, 2017 –
Abstract We describe a modified shared-LSTM network for the Semantic Textual Similarity (STS) task at SemEval-2017. The network builds on previously explored Siamese network architectures. We treat max sentence length as an additional hyperparameter to be tuned

SEFUHH at SemEval-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph Vector
MS Duma, W Menzel – … of the 11th International Workshop on Semantic …, 2017 –
Abstract This paper describes our unsupervised knowledge-free approach to the SemEval-2017 Task 1 Competition. The proposed method makes use of Paragraph Vector for assessing the semantic similarity between pairs of sentences. We experimented with various

Cross-lingual Learning of Semantic Textual Similarity with Multilingual Word Representations
J Bjerva, R Östling – Proceedings of the 21st Nordic Conference on …, 2017 –
Abstract Assessing the semantic similarity between sentences in different languages is challenging. We approach this problem by leveraging multilingual distributional word representations, where similar words in different languages are close to each other. The

Udl at semeval-2017 task 1: Semantic textual similarity estimation of english sentence pairs using regression model over pairwise features
HT Al-Natsheh, L Martinet, F Muhlenbach… – … Workshop on Semantic …, 2017 –
Abstract This paper describes the model UdL we proposed to solve the semantic textual similarity task of SemEval 2017 workshop. The track we participated in was estimating the semantics relatedness of a given set of sentence pairs in English. The best run out of three

ResSim at SemEval-2017 Task 1: Multilingual word representations for semantic textual similarity
J Bjerva, R Östling – … of the 11th International Workshop on Semantic …, 2017 –
Abstract Shared Task 1 at SemEval-2017 deals with assessing the semantic similarity between sentences, either in the same or in different languages. In our system submission, we employ multilingual word representations, in which similar words in different languages

Improving Semantic Textual Similarity with Phrase Entity Alignment
V Sowmya, BV Vardhan, MSVSB Raju – International Journal of Intelligent …, 2017 –
Abstract: Semantic Textual Similarity (STS) measures the degree of semantic equivalence between two segments of text, even though the similar context is expressed using different words. The textual segments are word phrases, sentences, paragraphs or documents. The

A resource-light method for cross-lingual semantic textual similarity
G Glavaš, M Franco-Salvador, SP Ponzetto… – Knowledge-Based …, 2017 – Elsevier
Abstract Recognizing semantically similar sentences or paragraphs across languages is beneficial for many tasks, ranging from cross-lingual information retrieval and plagiarism detection to machine translation. Recently proposed methods for predicting cross-lingual

A simple neural network for evaluating semantic textual similarity
S Yang –
Abstract 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 in the primary track. More variant neural network structures and experiments

V Sowmya, KK Kiran, T Putta –
Abstract Sentence similarity measures plays a key role in text-related research and applications in areas like as text mining, natural language processing, information extraction, etc. Semantic Textual Similarity (STS) measures the degree of semantic

QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings
F Meng, W Lu, Y Zhang, J Cheng, Y Du… – … Workshop on Semantic …, 2017 –
Abstract This paper reports the details of our submissions in the task 1 of SemEval 2017. This task aims at assessing the semantic textual similarity of two sentences or texts. We submit three unsupervised systems based on word embeddings. The differences between

Semantic Textual Similarity for Hindi
D AGARWAL – 2017 –
Abstract Short texts play a major role in our day-to-day communication in many forms such as emails, chat, tweets, news headlines, image captions and many more. Many techniques have been developed in the field of natural language processing to automatically process

Determining Semantic Textual Similarity using Natural Deduction Proofs
H Yanaka, K Mineshima, P Martinez-Gomez… – arXiv preprint arXiv …, 2017 –
Abstract: Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic

Computational models for semantic textual similarity
A González Aguirre – 2017 –
The overarching goal of this thesis is to advance on computational models of meaning and their evaluation. To achieve this goal we define two tasks and develop state-of-the-art systems that tackle both task: Semantic Textual Similarity (STS) and Typed Similarity. STS

Gradually Improving the Computation of Semantic Textual Similarity in Portuguese
HG Oliveira, AO Alves, R Rodrigues – Portuguese Conference on Artificial …, 2017 – Springer
Abstract There is much research on Semantic Textual Similarity (STS) in English, specially since its inclusion in the SemEval evaluations. For other languages, it is not as common, mostly due to the unavailability of benchmarks. Recently, the ASSIN shared task targeted

K Anuradha, H Indukuri, T Putta –
Abstract The Semantic similarity is to measure the similarity between two texts. Semantic similarity is measured between two words, sentences, paragraphs and documents. In this paper, text chosen is sentences. Similarity measure is based on semantic and syntactic

If Sentences Could See: Investigating Visual Information for Semantic Textual Similarity
G Glavaš, I Vuli?, SP Ponzetto – IWCS 2017-12th International …, 2017 –
Abstract We investigate the effects of incorporating visual signal from images into unsupervised Semantic Textual Similarity (STS) measures. STS measures exploiting visual signal alone are shown to outperform, in some settings, linguistic-only measures by a wide

University of Mannheim@ CLSciSumm-17: Citation-Based Summarization of Scientific Articles Using Semantic Textual Similarity
A Lauscher, G Glavaš, K Eckert – 2017 –
Abstract: The number of publications is rapidly growing and it is essential to enable fast access and analysis of relevant articles. In this paper, we describe a set of methods based on measuring semantic textual similarity, which we use to semantically analyze and

Survey of Simple Neural Networks in Semantic Textual Similarity Analysis
DS Prijatelj, J Ventura, J Kalita –
Abstract—Learning the semantic resemblance between natural language sentences for computers is a difficult task due to the inherent complexity of natural language. To combat this complexity in learning natural language semantics, a rise in the research of complex

Neural Networks for Semantic Textual Similarity
DS Prijatelj, J Ventura, J Kalita –
Abstract Complex neural network architectures are being increasingly used to learn to compute the semantic resemblances among natural language texts. It is necessary to establish a lower bound of performance that must be met in order for new complex

Computing textual semantic similarity for short texts using different similarity measures
RA Gandhi, VB Vaghela – 2017 –
Abstract-Semantic similarity of short text is the method of natural language processing which is widely used in natural language processing, opinion mining, text mining, text summarization, information retrieval and recognizing textual entailment (RTE), etc. Semantic