Disambiguation of newly derived nominalizations in context: A Distributional Semantics approach

Abstract

One of the central problems in the semantics of derived words is polysemy (see, for example, the recent contributions by Lieber 2016 and Plag et al. 2018 ). In this paper, we tackle the problem of disambiguating newly derived words in context by applying Distributional Semantics ( Firth 1957 ) to deverbal -ment nominalizations (e.g. bedragglement, emplacement). We collected a dataset containing contexts of low frequency deverbal -ment nominalizations (55 types, 406 tokens, see Appendix B) extracted from large corpora such as the Corpus of Contemporary American English. We chose low frequency derivatives because high frequency formations are often lexicalized and thus tend to not exhibit the kind of polysemous readings we are interested in. Furthermore, disambiguating low-frequency words presents an especially difficult task because there is little to no prior knowledge about these words from which their semantic properties can be extrapolated. The data was manually annotated according to eventive vs. non-eventive interpretations, allowing also an ambiguous label in those cases where the context did not disambiguate. Our question then was to what extent, and under which conditions, context-derived representations such as those of Distributional Semantics can be successfully employed in the disambiguation of low-frequency derivatives. Our results show that, first, our models are able to distinguish between eventive and non-eventive readings with some success. Second, very small context windows are sufficient to find the intended interpretation in the majority of cases. Third, ambiguous instances tend to be classified as events. Fourth, the performance of the classifier differed for different subcategories of nouns, with non-eventive derivatives being harder to classify correctly. We present indirect evidence that this is due to the semantic similarity of abstract non-eventive nouns to eventive nouns. Overall, this paper demonstrates that distributional semantic models can be fruitfully employed for the disambiguation of low frequency words in spite of the scarcity of available contextual information. 1

What’s in an agent?

“…Distributional semantics has also been used to study the interaction between morphology and semantics, in which case works engage in the semantic analysis of morphologically implemented classes. Many studies aim at semantically distinguishing rival word-formation patterns (Varvara et al 2016;Wauquier et al 2020), while others address affix polysemy and word sense disambiguation (Lapesa et al 2018). Such studies benefit from the automatic corpus-driven analysis provided by distributional semantics, and carried out over large sets of data.…”

Towards a wider perspective in the social sciences using a network of variables based on thousands of results

“…The challenges we expect to encounter in automatic processing of the texts of the articles are, inter alia, assignment of different names to the same statistical variable in different articles (metonomy) and using the same term to refer to different variables (polysemy). In order to identify variations in variable names in different locations within the single article and in different articles, the state-of-the-art algorithms for word sense disambiguation and cross-document co-reference resolution (Wities et al 2017;Keshtkaran et al 2017;Lapesa et al 2018) may be adopted and utilized. These techniques comprise clustering methods based on identification of similar or entailing words and text segments from their contexts (Henderson and Popa 2016;Kotlerman et al 2010).…”

“…These techniques comprise clustering methods based on identification of similar or entailing words and text segments from their contexts (Henderson and Popa 2016;Kotlerman et al 2010). In addition, existing general ontologies, such as WordNet could also be effectively utilized for disambiguation of polysemous variable names (Lapesa et al 2018).…”

Différenciation sémantique de dérivés morphologiques à l’aide de critères distributionnels

“…Varvara et al (2016) ont pour leur part différencié sur le plan distributionnel deux procédés de nominalisation processive concurrents de l'allemand. Lapesa et al (2017) utilisent quant à eux des indices distributionnels pour entraîner des classifieurs automatiques à identifier les lectures événementielles des noms d'action anglais en -ment.…”