1,445 research outputs found
SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation
We present SimLex-999, a gold standard resource for evaluating distributional
semantic models that improves on existing resources in several important ways.
First, in contrast to gold standards such as WordSim-353 and MEN, it explicitly
quantifies similarity rather than association or relatedness, so that pairs of
entities that are associated but not actually similar [Freud, psychology] have
a low rating. We show that, via this focus on similarity, SimLex-999
incentivizes the development of models with a different, and arguably wider
range of applications than those which reflect conceptual association. Second,
SimLex-999 contains a range of concrete and abstract adjective, noun and verb
pairs, together with an independent rating of concreteness and (free)
association strength for each pair. This diversity enables fine-grained
analyses of the performance of models on concepts of different types, and
consequently greater insight into how architectures can be improved. Further,
unlike existing gold standard evaluations, for which automatic approaches have
reached or surpassed the inter-annotator agreement ceiling, state-of-the-art
models perform well below this ceiling on SimLex-999. There is therefore plenty
of scope for SimLex-999 to quantify future improvements to distributional
semantic models, guiding the development of the next generation of
representation-learning architectures
Proceedings of the Workshop Semantic Content Acquisition and Representation (SCAR) 2007
This is the proceedings of the Workshop on Semantic Content Acquisition and Representation, held in conjunction with NODALIDA 2007, on May 24 2007 in Tartu, Estonia.</p
Measuring praise and criticism: Inference of semantic orientation from association
The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This paper introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8% on the full test set, but the accuracy rises above 95% when the algorithm is allowed to abstain from classifying mild words
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