Word Space Wiki

cfp - removed - external edit (Unknown date)

Anonymous (anonymous@undisclosed.example.com) — 2026-09-11T19:54:29+00:00

Start page -- Background -- Programme and proceedings -- Shared tasks -- Call for papers Call for papers We welcome papers reporting results of experimenting with word space models on one or more workshop tasks, as well as comparing different models on the same task(s) (authors are asked to carry out their own evaluation, using, if possible, the tools provided on the Website).

task - removed - external edit (Unknown date)

Anonymous (anonymous@undisclosed.example.com) — 2026-09-11T18:20:51+00:00

Start page -- Background -- Programme and proceedings -- Shared tasks -- Call for papers Shared tasks and data sets In order to reach a better understanding of the possibilities and limitations of distributional models of word meaning, we envisaged a number of tasks that focus on linguistic and cognitive challenges rather than application engineering.

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Anonymous (anonymous@undisclosed.example.com) — 2026-09-11T16:56:08+00:00

Start page -- Background -- Programme and proceedings -- Shared tasks -- Call for papers Background and motivation Array 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…

bibliography - [Courses and Tutorials on DSM]

schtepf (schtepf@undisclosed.example.com) — 2026-09-11T16:40:51+00:00

Courses and Tutorials on DSM ESSLLI 2009 – NAACL-HLT 2010 – ESSLLI '16 & '18 – ESSLLI '21 & '22 – Software & data sets – Bibliography DSM Bibliography: Suggested readings Core readings * Curran, J.R. (2004), From Distributional to Semantic Similarity, PhD thesis, University of Edinburgh * Harris, Z.S. (1954), “Distributional structure”, Word, 10/2-3, 1954: 146-62

concrete_noun_categorization - removed - external edit (Unknown date)

Anonymous (anonymous@undisclosed.example.com) — 2026-09-11T15:58:07+00:00

Introduction The goal of the sub-task is to group concrete nouns into semantic categories. The [data set] consists of 44 concrete nouns, belonging to 6 semantic categories (four natural and two man-made). The nouns are included in the feature norms described in McRae et al. (2005) (cf. Task3).

concrete_nouns_categorization - removed - external edit (Unknown date)

Anonymous (anonymous@undisclosed.example.com) — 2026-09-11T15:54:04+00:00

Introduction The goal of the sub-task is to group concrete nouns into semantic categories. The [data set] consists of 44 concrete nouns, belonging to 6 semantic categories (four animates and two inanimates). The nouns are included in the feature norms described in McRae et al. (2005) (cf. Task3).

verb_categorization - removed - external edit (Unknown date)

Anonymous (anonymous@undisclosed.example.com) — 2026-09-11T15:51:22+00:00

Introduction The goal of the sub-task is to group verbs into semantic categories. The [data set] consists of 45 verbs, belonging to 9 semantic classes. The classification scheme is inspired by P. Vinson & G. Vigliocco (2007), “Semantic Feature Production Norms for a Large Set of Objects and Events”, Behavior Research Methods, which in turn closely follows the classification proposed in Levin (1993).

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Anonymous (anonymous@undisclosed.example.com) — 2026-09-11T15:46:23+00:00

Start page -- Background -- Programme and proceedings -- Shared tasks -- Call for papers Proceedings Bridging the gap between semantic theory and computational simulations Workshop at ESSLLI 2008, Hamburg, August 4-8 2008 [Workshop proceedings] (PDF, 1.3 MB)

comparison_with_speaker-generated_features - removed - external edit (Unknown date)

Anonymous (anonymous@undisclosed.example.com) — 2026-09-11T15:40:07+00:00

Introduction From a cognitive point of view, there is little doubt that salient properties of a concept are an important part of its “meaning”, and subjects show a remarkable degree of agreement in tasks that require enumerating the typical properties of a concept: a dog barks, has a tail, is a pet, etc.

abstract_concrete_nouns_discrimination - removed - external edit (Unknown date)

Anonymous (anonymous@undisclosed.example.com) — 2026-09-11T15:39:20+00:00

Introduction The contrast between abstract and concrete words plays a central role in human cognition. Behavioural and neuropsychological evidence suggests that abstract and concrete concepts might be represented, retrieved and processed differently in the human brain (Noppeney, U. and C. Price (2004), “Retrieval of abstract semantics”, Brain and Image, 22: 164-170)

correlation_with_free_association_norms - removed - external edit (Unknown date)

Anonymous (anonymous@undisclosed.example.com) — 2026-09-11T15:26:43+00:00

Overview and main goals In psychology, free associations are the first words that come to the mind of a native speaker when he or she is presented with a stimulus word, presumably retrieved from associative memory. It is tempting to make a connection between such free associations and the statistical association patterns of words in the linguistic experience of speakers, including both first-order associations (collocations) and higher-order associations (distributional similarity). The misl…

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Anonymous (anonymous@undisclosed.example.com) — 2026-09-11T15:23:33+00:00

start - [Hands-on Distributional Semantics (ESSLLI 2021 / 2022)]

schtepf (schtepf@undisclosed.example.com) — 2022-08-11T10:26:35+00:00

Hands-on Distributional Semantics (ESSLLI 2021 / 2022) Hands-on Distributional Semantics – From first steps to interdisciplinary applications [ESSLLI 2022 (Galway)] [ESSLLI 2021 (online)] Foundational course at ESSLLI 2021, online, August 9–13, 2021 Hands-on Distributional Semantics for Linguistics using R

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schtepf (schtepf@undisclosed.example.com) — 2022-08-11T10:20:24+00:00

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schtepf (schtepf@undisclosed.example.com) — 2022-08-11T10:20:24+00:00

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schtepf (schtepf@undisclosed.example.com) — 2022-08-09T23:47:15+00:00

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schtepf (schtepf@undisclosed.example.com) — 2022-08-09T23:46:30+00:00

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schtepf (schtepf@undisclosed.example.com) — 2022-08-09T23:46:30+00:00