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PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings

2017-08-01 · SEMEVAL 2017 8 · I-Ta Lee, Mahak Goindani, Chang Li, Di Jin, Kristen Marie Johnson, Xiao Zhang, Maria Leonor Pacheco, Dan Goldwasser

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 Pearson Correlation scores between the predicted scores and human judgements. Our proposed system consists of two subsystems and one regression model for predicting STS scores. The two subsystems are designed to learn Paraphrase and Event Embeddings that can take the consideration of paraphrasing characteristics and sentence structures into our system. The regression model associates these embeddings to make the final predictions. The experimental result shows that our system acquires 0.8 of Pearson Correlation Scores in this task.

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Tasks

Question Answering regression Semantic Textual SimilaritySentenceSTSWord Embeddings

JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction

2021-08-01 · SEMEVAL 2021 · Chaya Liebeskind, Otniel Elkayam, Shmuel Liebeskind

In this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within …

Lexical Complexity Prediction Word Embeddings

Tweety at SemEval-2018 Task 2: Predicting Emojis using Hierarchical Attention Neural Networks and Support Vector Machine

2018-06-01 · SEMEVAL 2018 6 · Daniel Kopev, Atanas Atanasov, Dimitrina Zlatkova, Momchil Hardalov 외

We present the system built for SemEval-2018 Task 2 on Emoji Prediction. Although Twitter messages are very short we managed to design a wide variety of features: textual, semantic, sentiment, emotion-, and color-related…

Task 2 Word Embeddings

AlexU-AUX-BERT at SemEval-2020 Task 3: Improving BERT Contextual Similarity Using Multiple Auxiliary Contexts

2020-12-01 · SEMEVAL 2020 · Somaia Mahmoud, Marwan Torki

This paper describes the system we built for SemEval-2020 task 3. That is predicting the scores of similarity for a pair of words within two different contexts. Our system is based on both BERT embeddings and WordNet. We…

UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection

2020-04-30 · SEMEVAL 2020 · Andrey Kutuzov, Mario Giulianelli

We apply contextualised word embeddings to lexical semantic change detection in the SemEval-2020 Shared Task 1. This paper focuses on Subtask 2, ranking words by the degree of their semantic drift over time. We analyse t…

Change Detection Word Embeddings

MineriaUNAM at SemEval-2020 Task 3: Predicting Contextual WordSimilarity Using a Centroid Based Approach and Word Embeddings

2020-12-01 · SEMEVAL 2020 · Helena Gomez-Adorno, Gemma Bel-Enguix, Jorge Reyes-Maga{\~n}a, Benjam{\'\i}n Moreno 외

This paper presents our systems to solve Task 3 of Semeval-2020, which aims to predict the effect that context has on human perception of similarity of words. The task consists of two subtasks in English, Croatian, Finni…

Language Modeling Language Modelling Word Embeddings