Latent Variable Models of Concept-Attribute Attachment
نویسندگان
چکیده
This paper presents a set of Bayesian methods for automatically extending the WORDNET ontology with new concepts and annotating existing concepts with generic property fields, or attributes. We base our approach on Latent Dirichlet Allocation and evaluate along two dimensions: (1) the precision of the ranked lists of attributes, and (2) the quality of the attribute assignments to WORDNET concepts. In all cases we find that the principled LDA-based approaches outperform previously proposed heuristic methods, greatly improving the specificity of attributes at each concept.
منابع مشابه
Discovering Shades of Attribute Meaning with the Crowd
To learn semantic attributes, existing methods typically train one discriminative model for each word in a vocabulary of nameable properties. This “one model per word” assumption is problematic: while a word might have a precise linguistic definition, it need not have a precise visual definition. We propose to discover shades of attribute meaning. Given an attribute name, we use crowdsourced im...
متن کاملUsing multivariate generalized linear latent variable models to measure the difference in event count for stranded marine animals
BACKGROUND AND OBJECTIVES: The classification of marine animals as protected species makes data and information on them to be very important. Therefore, this led to the need to retrieve and understand the data on the event counts for stranded marine animals based on location emergence, number of individuals, behavior, and threats to their presence. Whales are g...
متن کاملA Joint Model of Language and Perception for Grounded Attribute Learning
As robots become more ubiquitous and capable, it becomes ever more important for untrained users to easily interact with them. Recently, this has led to study of the language grounding problem, where the goal is to extract representations of the meanings of natural language tied to the physical world. We present an approach for joint learning of language and perception models for grounded attri...
متن کاملModeling Concept-Attribute Structure
We apply hierarchical Latent Dirichlet Allocation (hLDA) to the problem of ontology annotation; automatically extending WORDNET with new concepts and annotating existing concepts with generic property fields, or attributes. The resulting annotations are evaluated along two dimensions: (1) the precision of the ranked lists of attributes at each concept, and (2) the specificity of the attribute a...
متن کاملFast and Robust Multilingual Dependency Parsing with a Generative Latent Variable Model
We use a generative history-based model to predict the most likely derivation of a dependency parse. Our probabilistic model is based on Incremental Sigmoid Belief Networks, a recently proposed class of latent variable models for structure prediction. Their ability to automatically induce features results in multilingual parsing which is robust enough to achieve accuracy well above the average ...
متن کامل