Capturing Discriminative Attributes in a Distributional Space: Task Proposal

Krebs, Alicia; Paperno, Denis

doi:10.18653/v1/w16-2509

Cited by 14 publications

(19 citation statements)

References 11 publications

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“…We therefore select these embeddings for further experiments. Table 2: Averaged F-Score across GloVe Dimensions between our 2-step unsupervised system and the baseline from Krebs and Paperno (2016), for word vectors of size 50, 100, 200 and 300.…”

Section: Resultsmentioning

confidence: 99%

“…For example, we would expect the word car to have high semantic similarity with truck and with vehicle in distributional vector spaces, while the relation between car and truck differs from the relation between car and vehicle. In addition, popular datasets for similarity tasks are small, and similarity annotations are subjective with low inter-annotator agreement (Krebs and Paperno, 2016).…”

Section: Introductionmentioning

confidence: 99%

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

Zhang¹,

Carpuat²

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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We describe the University of Maryland's submission to SemEval-018 Task 10, "Capturing Discriminative Attributes": given word triples (w 1 , w 2 , d), the goal is to determine whether d is a discriminating attribute belonging to w 1 but not w 2 . 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%).

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang¹,

Carpuat²

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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show abstract

“…When creating the dataset, we started from the approach that Lazaridou et al (2016) used for visual discriminating attribute identification, followed by manual filtering for the test and validation data. Dataset creation consisted of three phases: As an initial source of data, we used the feature norms collected by McRae et al (2005) and created a pilot dataset (Krebs and Paperno, 2016). This set was then reverified and manually extended to improve the quality and the variety of the data.…”

Section: Data Collection and Annotationmentioning

confidence: 99%

SemEval-2018 Task 10: Capturing Discriminative Attributes

Krebs

Lenci

Paperno

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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This paper describes the SemEval 2018 Task 10 on Capturing Discriminative Attributes. Participants were asked to identify whether an attribute could help discriminate between two concepts. For example, a successful system should determine that urine is a discriminating feature in the word pair kidney,bone. The aim of the task is to better evaluate the capabilities of state of the art semantic models, beyond pure semantic similarity. The task attracted submissions from 21 teams, and the best system achieved a 0.75 F1 score.

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“…As emphasized by Krebs and Paperno (2016), this non-trivial task has numerous applications, such as automatized lexicography, conversational agents or machine translation.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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

Dumitru

Ciobanu

Dinu

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

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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) pair, but not the second. Modeling semantic difference is essential for language understanding systems, as it provides useful information for identifying particular aspects of word senses. This paper describes our system implementation (the ALB system of the NLP@Unibuc team) for the 10th task of the Semaval 2018 workshop, "Capturing Discriminative Attributes". We propose a method for semantic difference detection that uses an SVM classifier with features based on co-occurrence counts and shallow semantic parsing, achieving 0.63 F1 score in the competition.

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Capturing Discriminative Attributes in a Distributional Space: Task Proposal

Cited by 14 publications

References 11 publications

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

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

SemEval-2018 Task 10: Capturing Discriminative Attributes

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

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