A Structured Vector Space Model for Hidden Attribute Meaning in Adjective-Noun Phrases
نویسندگان
چکیده
We present an approach to model hidden attributes in the compositional semantics of adjective-noun phrases in a distributional model. For the representation of adjective meanings, we reformulate the pattern-based approach for attribute learning of Almuhareb (2006) in a structured vector space model (VSM). This model is complemented by a structured vector space representing attribute dimensions of noun meanings. The combination of these representations along the lines of compositional semantic principles exposes the underlying semantic relations in adjective-noun phrases. We show that our compositional VSM outperforms simple pattern-based approaches by circumventing their inherent sparsity problems.
منابع مشابه
Assessing Interpretable, Attribute-related Meaning Representations for Adjective-Noun Phrases in a Similarity Prediction Task
We present a distributional vector space model that incorporates Latent Dirichlet Allocation in order to capture the semantic relation holding between adjectives and nouns along interpretable dimensions of meaning: The meaning of adjective-noun phrases is characterized in terms of ontological attributes that are prominent in their compositional semantics. The model is evaluated in a similarity ...
متن کاملDistributional Semantic Models of Attribute Meaning in Adjectives and Nouns
Attributes such as size, weight or color are at the core of conceptualization, i.e., the formal representation of entities or events in the real world. In natural language, formal attributes find their counterpart in attribute nouns which can be used in order to generalize over individual properties (e.g., big or small in case of size, blue or red in case of color). In order to ascribe such pro...
متن کاملLearning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases
Word embeddings have been shown to be highly effective in a variety of lexical semantic tasks. They tend to capture meaningful relational similarities between individual words, at the expense of lacking the capabilty of making the underlying semantic relation explicit. In this paper, we investigate the attribute relation that often holds between the constituents of adjective-noun phrases. We us...
متن کاملExploring Supervised LDA Models for Assigning Attributes to Adjective-Noun Phrases
This paper introduces an attribute selection task as a way to characterize the inherent meaning of property-denoting adjectives in adjective-noun phrases, such as e.g. hot in hot summer denoting the attribute TEMPERATURE, rather than TASTE. We formulate this task in a vector space model that represents adjectives and nouns as vectors in a semantic space defined over possible attributes. The vec...
متن کاملA Generative Model of Vector Space Semantics
We present a novel compositional, generative model for vector space representations of meaning. This model reformulates earlier tensor-based approaches to vector space semantics as a top-down process, and provides efficient algorithms for transformation from natural language to vectors and from vectors to natural language. We describe procedures for estimating the parameters of the model from p...
متن کامل