Weakly Supervised Learning of Presupposition Relations between Verbs
نویسنده
چکیده
Presupposition relations between verbs are not very well covered in existing lexical semantic resources. We propose a weakly supervised algorithm for learning presupposition relations between verbs that distinguishes five semantic relations: presupposition, entailment, temporal inclusion, antonymy and other/no relation. We start with a number of seed verb pairs selected manually for each semantic relation and classify unseen verb pairs. Our algorithm achieves an overall accuracy of 36% for type-based classification.
منابع مشابه
Extending Fine-Grained Semantic Relation Classification to Presupposition Relations between Verbs
In contrast to typical semantic relations between verbs, such as antonymy, synonymy or hyponymy, presupposition is a lexical relation that is not very well covered in existing lexical resources. It is also understudied in the field of corpus-based methods of learning semantic relations. But presupposition is very important for the quality of automatic semantic and discourse analysis tasks. In t...
متن کاملA Discriminative Analysis of Fine-Grained Semantic Relations including Presupposition: Annotation and Classification
In contrast to classical lexical semantic relations between verbs, such as antonymy, synonymy or hypernymy, presupposition is a lexically triggered semantic relation that is not well covered in existing lexical resources. It is also understudied in the field of corpus-based methods of learning semantic relations. Yet, presupposition is very important for semantic and discourse analysis tasks, g...
متن کاملFactivity and presupposition in Dependent Type Semantics
Dependent Type Semantics (DTS, Bekki [3]) is a framework of natural language semantics based on dependent type theory (Martin-Löf [19]). In contrast to traditional model-theoretic semantics, DTS is a proof-theoretic semantics, where entailment relations are characterized as provability relations between semantic representations. One of the distinctive features of DTS, as compared to other type-...
متن کاملLearning Verb Inference Rules from Linguistically-Motivated Evidence
Learning inference relations between verbs is at the heart of many semantic applications. However, most prior work on learning such rules focused on a rather narrow set of information sources: mainly distributional similarity, and to a lesser extent manually constructed verb co-occurrence patterns. In this paper, we claim that it is imperative to utilize information from various textual scopes:...
متن کاملWeakly Supervised Learning of Part-Based Spatial Models for Visual Object Recognition
In this paper we investigate a new method of learning partbased models for visual object recognition, from training data that only provides information about class membership (and not object location or configuration). This method learns both a model of local part appearance and a model of the spatial relations between those parts. In contrast, other work using such a weakly supervised learning...
متن کامل