UNBNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation

King, Milton; Gharbieh, Waseem; Park, So‐Hyun; Cook, Paul F.

doi:10.18653/v1/s16-1113

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…We accomplished comparisons between our method and state-of-the-art methods on the basis of the SICK and the SemEval ( 2016) datasets (Bentivogli et al, 2016;Agirre et al, 2016). Evaluations of some of these methods have been performed recently on the SemEval (2016) STS competitions (King et al, 2016;Cer et al, 2017).…”

Section: Datasetsmentioning

confidence: 99%

Unsupervised sentence representations as word information series: Revisiting TF–IDF

Arroyo-Fernández

Méndez-Cruz

Sierra

et al. 2019

Computer Speech & Language

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Sentence representation at the semantic level is a challenging task for Natural Language Processing and Artificial Intelligence. Despite the advances in word embeddings (i.e. word vector representations), capturing sentence meaning is an open question due to complexities of semantic interactions among words. In this paper, we present an embedding method, which is aimed at learning unsupervised sentence representations from unlabeled text. We propose an unsupervised method that models a sentence as a weighted series of word embeddings. The weights of the word embeddings are fitted by using Shannon's word entropies provided by the Term Frequency-Inverse Document Frequency (TF-IDF) transform. The hyperparameters of the model can be selected according to the properties of data (e.g. sentence length and textual gender). Hyperparameter selection involves word embedding methods and dimensionalities, as well as weighting schemata. Our method offers advantages over existing methods: identifiable modules, short-term training, online inference of (unseen) sentence representations, as well as independence from domain, external knowledge and language resources. Results showed that our model outperformed the state of the art in well-known Semantic Textual Similarity (STS) benchmarks. Moreover, our model reached state-of-the-art performance when compared to supervised and knowledge-based STS systems.

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Section: Datasetsmentioning

confidence: 99%

Unsupervised sentence representations as word information series: Revisiting TF–IDF

Arroyo-Fernández

Méndez-Cruz

Sierra

et al. 2019

Computer Speech & Language

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show abstract

“…We present in this subsection the state-of-the-art in STS-Task 1 using Paragraph Vector since it is the most relevant to our work. King et al (2016), for instance make use of Paragraph Vectors as one approach in the English monolingual sub-task. Results are reported for a single vector size and the Cosine metric which is employed in obtaining the similarity score between sentences.…”

Section: Semantic Textual Similaritymentioning

confidence: 99%

SEF$@$UHH at SemEval-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph Vector

Duma¹,

Menzel²

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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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 dimensions of the vector and three state-of-the-art similarity metrics. Given a cross-lingual task, we trained models corresponding to its two languages and combined the models by averaging the similarity scores. The results of our submitted runs are above the median scores for five out of seven test sets by means of Pearson Correlation. Moreover, one of our system runs performed best on the SpanishEnglish-WMT test set ranking first out of 53 runs submitted in total by all participants.

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Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model

Zhuang¹,

Chang²

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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This paper describes a neural-network model which performed competitively (top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS) task. Our system employs an attention-based recurrent neural network model that optimizes the sentence similarity. In this paper, we describe our participation in the multilingual STS task which measures similarity across English, Spanish, and Arabic.

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UNBNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation

Cited by 4 publications

References 2 publications

Unsupervised sentence representations as word information series: Revisiting TF–IDF

Unsupervised sentence representations as word information series: Revisiting TF–IDF

SEF$@$UHH at SemEval-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph Vector

Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model

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