Vector-based Models of Semantic Composition

A Joint Semantic Vector Representation Model for Text Clustering and Classification

Text clustering and classification are two main tasks of text mining. Feature selection plays the key role in the quality of the clustering and classification results. Although word-based features such as term frequency-inverse document frequency (TF-IDF) vectors have been widely used in different applications, their shortcoming in capturing semantic concepts of text motivated researches to use...

Semantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models

Output Composition of Monetary Policy Transmission

This paper aims to investigate the role of each aggregate spending component in the monetary policy transmission in Indonesia. It assesses the relative strength of the role of each spending component in the monetary policy transmission. In so doing, this study employs the contribution analysis, which is calculated based on the cumulative impulse response of each component of GDP to a monetary p...

Semantic Composition with Quotient Algebras

We describe an algebraic approach for computing with vector based semantics. The tensor product has been proposed as a method of composition, but has the undesirable property that strings of different length are incomparable. We consider how a quotient algebra of the tensor algebra can allow such comparisons to be made, offering the possibility of data-driven models of semantic composition.

mwetoolkit+sem: Integrating Word Embeddings in the mwetoolkit for Semantic MWE Processing

This paper presents mwetoolkit+sem: an extension of the mwetoolkit that estimates semantic compositionality scores for multiword expressions (MWEs) based on word embeddings. First, we describe our implementation of vector-space operations working on distributional vectors. The compositionality score is based on the cosine distance between the MWE vector and the composition of the vectors of its...