Aligning Packed Dependency Trees: A Theory of Composition for Distributional Semantics

Aligning Packed Dependency Trees: a theory of composition for distributional semantics

We present a new framework for compositional distributional semantics in which the distributional contexts of lexemes are expressed in terms of anchored packed dependency trees. We show that these structures have the potential to capture the full sentential contexts of a lexeme and provide a uniform basis for the composition of distributional knowledge in a way that captures both mutual disambi...

Composition in Distributional Semantics

Denoising composition in distributional semantics

Since in distributional semantics we derive the embedding from a corpus, and the corpus is just a sample from the entire distribution, it is inevitable that the vectors obtained in the process will be somewhat noisy. In Section 2 we analyze this and other sources of noise, and in Section 3 we turn to the question of how much the considerations of compositionality discussed in Kornai and Kracht ...

DISSECT - DIStributional SEmantics Composition Toolkit

We introduce DISSECT, a toolkit to build and explore computational models of word, phrase and sentence meaning based on the principles of distributional semantics. The toolkit focuses in particular on compositional meaning, and implements a number of composition methods that have been proposed in the literature. Furthermore, DISSECT can be useful to researchers and practitioners who need models...

A Generalisation of Lexical Functions for Composition in Distributional Semantics

Over the last two decades, numerous algorithms have been developed that successfully capture something of the semantics of single words by looking at their distribution in text and comparing these distributions in a vector space model. However, it is not straightforward to construct meaning representations beyond the level of individual words – i.e. the combination of words into larger units – ...