Samsung Poland NLP Team at SemEval-2016 Task 1: Necessity for diversity; combining recursive autoencoders, WordNet and ensemble methods to measure semantic similarity.

Rychalska, Barbara; Pakulska, Katarzyna; Chodorowska, Krystyna; Walczak, Wojciech; Andruszkiewicz, Piotr

doi:10.18653/v1/s16-1091

Cited by 37 publications

(30 citation statements)

References 12 publications

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“…More recently, deep learning became competitive with top performing feature engineered systems . The best performance tends to be obtained by ensembling feature engineered and deep learning models (Rychalska et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

Cer¹,

Diab²,

Agirre³

et al. 2017

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

1,162

775

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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. The STS shared task is a venue for assessing the current state-of-the-art. The 2017 task focuses on multilingual and cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE) data. The task obtained strong participation from 31 teams, with 17 participating in all language tracks. We summarize performance and review a selection of well performing methods. Analysis highlights common errors, providing insight into the limitations of existing models. To support ongoing work on semantic representations, the STS Benchmark is introduced as a new shared training and evaluation set carefully selected from the corpus of English STS shared task data (2012)(2013)(2014)(2015)(2016)(2017).

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Section: Introductionmentioning

confidence: 99%

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

Cer¹,

Diab²,

Agirre³

et al. 2017

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

1,162

775

View full text Add to dashboard Cite

show abstract

“…After all, if no relation is found between a pair of word to measure the distance between them, the distance will set to -1 and then we calculate similarity score using equation 1 introduced by (Rychalska et al, 2016):…”

Section: Knowledge-based Methodsmentioning

confidence: 99%

Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word Similarity

Ranjbar¹,

Mashhadirajab

Shamsfard

et al. 2017

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

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In this paper, we describe our proposed method for measuring semantic similarity for a given pair of words at SemEval-2017 monolingual semantic word similarity task. We use a combination of knowledge-based and corpus-based techniques. We use FarsNet, the Persian WordNet, besides deep learning techniques to extract the similarity of words. We evaluated our proposed approach on Persian (Farsi) test data at SemEval-2017. It outperformed the other participants and ranked the first in the challenge.

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“…In an attempt to follow these statements and also inspired by success obtained by authors like Han et al (2013) and Rychalska et al (2016), we implemented a word alignment system. Unlike previous works, our system considers that verbs operate on nouns.…”

Section: Cross Word Alignermentioning

confidence: 99%

LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity

Arroyo-Fernández

Meza²

2017

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

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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 motivated. The neural approaches proposed herein are divided into two main stages. The first stage deals with constructing neural word embeddings, the components of sentence embeddings. The second stage deals with constructing a semantic similarity function relating pairs of sentence embeddings. Unfortunately our competition results were poor in all tracks, therefore we concentrated our research to improve them for Track 5 (EN-EN).

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Samsung Poland NLP Team at SemEval-2016 Task 1: Necessity for diversity; combining recursive autoencoders, WordNet and ensemble methods to measure semantic similarity.

Cited by 37 publications

References 12 publications

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word Similarity

LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity

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