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| - | ====== Lexical Semantics Workshop (ESSLLI 2008) ====== | ||
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| - | [[esslli: | ||
| - | **Background** -- | ||
| - | [[esslli: | ||
| - | [[esslli: | ||
| - | [[esslli: | ||
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| - | ===== Background and motivation ===== | ||
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| - | [[http:// | ||
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| - | Corpus-based distributional models (such as LSA or HAL) | ||
| - | have been claimed to capture interesting aspects of word meaning | ||
| - | and provide an explanation for the rapid acquisition of semantic | ||
| - | knowledge by human language learners. | ||
| - | However, although these models have been proposed as | ||
| - | plausible simulations of human semantic space organization, | ||
| - | careful and extensive empirical tests of such claims are still lacking. | ||
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| - | Systematic evaluations typically focus on large-scale | ||
| - | quantitative tasks, often more oriented towards engineering | ||
| - | applications (see, e.g., the recent SEMEVAL evaluation campaign) than | ||
| - | towards the challenges posed by linguistic theory, | ||
| - | philosophy and cognitive science. | ||
| - | between corpus-driven computational approaches to semantics on the one | ||
| - | hand and theory-driven symbolic approaches on the other -- a situation that | ||
| - | is characteristic of the linguistic and of most | ||
| - | of the cognitive tradition. | ||
| - | Moreover, whereas human lexical semantic competence is obviously | ||
| - | multi-faceted -- ranging from free association to taxonomic judgments to | ||
| - | relational effects -- tests of distributional models tend to focus on a | ||
| - | single aspect (most typically the detection of semantic similarity), | ||
| - | and few if any models have been tuned to tackle | ||
| - | different facets of semantics in an integrated manner. | ||
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| - | Our workshop purports to fill these gaps by inviting research teams and | ||
| - | individual scholars to test their computational models on a variety of small but | ||
| - | carefully designed tasks that aim to bring out linguistically and | ||
| - | cognitively interesting aspects of semantics | ||
| - | ([[#Tasks and data sets|see below]] for details). | ||
| - | datasets are available to the participants, | ||
| - | encouraged to explore them and highlight interesting aspects of their | ||
| - | models' | ||
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| - | The focus is NOT on competition, | ||
| - | models highlight different semantic aspects, how far we are from | ||
| - | an integrated model, and which aspects of semantics are beyond the | ||
| - | reach of purely distributional approaches. | ||
| - | In fact, we believe that at the current state of the art in | ||
| - | computational and distributional semantics, our goal should not be | ||
| - | to develop the best-performing model for a specific application, | ||
| - | to enlarge our understanding of the limits and potentialities | ||
| - | of different approaches when confronted with cognitively realistic tasks. | ||
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| - | In addition to these practical tasks, theoretical and experimental papers | ||
| - | discussing the relation between distributional and symbolic approaches to meaning | ||
| - | are also invited. | ||
| - | task data sets from a theoretical perspective or that discuss simulation results | ||
| - | and their implications for semantic and cognitive theory. | ||
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| - | Through collaborative preparatory work on the Word Space wiki ([[http:// | ||
| - | collaboration among the nascent community of researchers interested in computational semantics from a theoretical rather than engineering-oriented point of view. | ||
