Georgiana Dinu

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Context-predicting models (more commonly known as embeddings or neural language models) are the new kids on the distributional semantics block. Despite the buzz surrounding these models, the literature is still lacking a systematic comparison of the predictive models with classic, count-vector-based distributional semantic approaches. In this paper, we(More)
The computation of meaning similarity as operationalized by vector-based models has found widespread use in many tasks ranging from the acquisition of synonyms and paraphrases to word sense disambiguation and textual entailment. Vector-based models are typically directed at representing words in isolation and thus best suited for measuring similarity out of(More)
The zero-shot paradigm exploits vector-based word representations extracted from text corpora with unsupervised methods to learn general mapping functions from other feature spaces onto word space, where the words associated to the nearest neighbours of the mapped vectors are used as their linguistic labels. We show that the neighbourhoods of the mapped(More)
In recent years, there has been widespread interest in compositional distributional semantic models (cDSMs), that derive meaning representations for phrases from their parts. We present an evaluation of alternative cDSMs under truly comparable conditions. In particular, we extend the idea of Baroni and Zamparelli (2010) and Guevara (2010) to use(More)
We present a model for compositional distributional semantics related to the framework of Coecke et al. (2010), and emulating formal semantics by representing functions as tensors and arguments as vectors. We introduce a new learning method for tensors, generalising the approach of Baroni and Zamparelli (2010). We evaluate it on two benchmark data sets, and(More)
Zero-shot methods in language, vision and other domains rely on a cross-space mapping function that projects vectors from the relevant feature space (e.g., visualfeature-based image representations) to a large semantic word space (induced in an unsupervised way from corpus data), where the entities of interest (e.g., objects images depict) are labeled with(More)
We present a vector space model that supports the computation of appropriate vector representations for words in context, and apply it to a paraphrase ranking task. An evaluation on the SemEval 2007 lexical substitution task data shows promising results: the model significantly outperforms a current state of the art model, and our treatment of context is(More)