UMD at SemEval-2018 Task 10: Can Word Embeddings Capture Discriminative Attributes?

2018-06-01 · SEMEVAL 2018 6 · Alex Zhang, er, Marine Carpuat

We describe the University of Maryland{'}s submission to SemEval-018 Task 10, {``}Capturing Discriminative Attributes{''}: given word triples (w1, w2, d), the goal is to determine whether d is a discriminating attribute belonging to w1 but not w2. Our study aims to determine whether word embeddings can address this challenging task. Our submission casts this problem as supervised binary classification using only word embedding features. Using a gaussian SVM model trained only on validation data results in an F-score of 60{\%}. We also show that cosine similarity features are more effective, both in unsupervised systems (F-score of 65{\%}) and supervised systems (F-score of 67{\%}).

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

AttributeBinary Classification Semantic Textual SimilarityWord Embeddings

Methods 이 논문이 사용한 방법론

SVM A Support Vector Machine, or SVM, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs…

ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet

2018-06-01 · SEMEVAL 2018 6 · Rui Mao, Guanyi Chen, Ruizhe Li, Chenghua Lin

This paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes. Our system is built upon a simple idea of measuring the attribute word{'}s similarity with each of the two …

Attribute Semantic Textual Similarity Word Embeddings

Meaning\_space at SemEval-2018 Task 10: Combining explicitly encoded knowledge with information extracted from word embeddings

2018-06-01 · SEMEVAL 2018 6 · Pia Sommerauer, Antske Fokkens, Piek Vossen

This paper presents the two systems submitted by the meaning space team in Task 10 of the SemEval competition 2018 entitled Capturing discriminative attributes. The systems consist of combinations of approaches exploitin…

Semantic Textual Similarity Word Embeddings

Igevorse at SemEval-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes

2018-06-01 · SEMEVAL 2018 6 · Maxim Grishin

This paper presents a comparison of several approaches for capturing discriminative attributes and considers an impact of concatenation of several word embeddings of different nature on the classification performance. A …

BIG-bench Machine Learning General Classification Semantic Textual Similarity Word Embeddings

ALB at SemEval-2018 Task 10: A System for Capturing Discriminative Attributes

2018-06-01 · SEMEVAL 2018 6 · Bogdan Dumitru, Alina Maria Ciobanu, Liviu P. Dinu

Semantic difference detection attempts to capture whether a word is a discriminative attribute between two other words. For example, the discriminative feature red characterizes the first word from the (apple, banana) pa…

Attribute Dependency Parsing Machine Translation Semantic Parsing+1

UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes

2018-06-01 · SEMEVAL 2018 6 · Milton King, Ali Hakimi Parizi, Paul Cook

In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these appro…

Semantic Textual Similarity Sentence Word Embeddings