SEF@UHH at SemEval-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph Vector
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 Spanish-English-WMT test set ranking first out of 53 runs submitted in total by all participants.
Code (0)
등록된 구현이 없습니다.
Tasks
Semantic SimilaritySemantic Textual SimilaritySimilar Papers 제목 키워드 기반
MedAI at SemEval-2021 Task 10: Negation-aware Pre-training for Source-free Negation Detection Domain Adaptation
Due to the increasing concerns for data privacy, source-free unsupervised domain adaptation attracts more and more research attention, where only a trained source model is assumed to be available, while the labeled sourc…
Domain AdaptationNegationNegation DetectionSource-Free Domain Adaptation+1CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations
This paper describes the winning contribution to SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection (Subtask 2) handed in by team UG Student Intern. We present an ensemble model that makes predictions ba…
Change DetectionSentenceSentence ClassificationWord Sense Disambiguation Based on Word Similarity Calculation Using Word Vector Representation from a Knowledge-based Graph
Word sense disambiguation (WSD) is the task to determine the word sense according to its context. Many existing WSD studies have been using an external knowledge-based unsupervised approach because it has fewer word set …
Information RetrievalMachine TranslationWord Sense DisambiguationWord SimilarityJCT at SemEval-2020 Task 1: Combined Semantic Vector Spaces Models for Unsupervised Lexical Semantic Change Detection
In this paper, we present our contribution in SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection, where we systematically combine existing models for unsupervised capturing of lexical semantic change acr…
Binary ClassificationChange DetectionAn Unsupervised Model with Attention Autoencoders for Question Retrieval
Question retrieval is a crucial subtask for community question answering. Previous research focus on supervised models which depend heavily on training data and manual feature engineering. In this paper, we propose a nov…
Community Question AnsweringFeature EngineeringQuestion AnsweringRetrieval