Classifying compound words has been the ultimate goal of much research in formal linguistics. A popular, cross-linguistically applicable classification (Bisetto & Scalise 2005) distinguishes three main types of compounds, namely Subordinate, Attributive, and Coordinate on the basis of the underlying syntactic relation between the compound elements. Similar tripartitions have also been proposed in cognitive psychology by works exploring conceptual combination. Focusing on the type of semantic interpretation assigned to novel combinations, three main classes have been traditionally described, namely Relation-linking, Property-mapping, and Hybrid or Conjunctive (see Wisniewski 1996). Based on these commonalities, we conjecture that syntax-based compound types might also be explained by means of the semantic properties of the compound and its constituents. Using a compositional model of distributional semantics (cDSM), we show that (a) the contribution of each constituent in determining the meaning of the compound and (b) the semantic similarity between the two constituent words are significant predictors of these classes. These findings suggest that the various compound types identified by syntactic criteria can also be predicted by means of semantic features. On the one hand, this confirms the validity of the proposed linguistic categorization. On the other hand, we bring further evidence proving the effectiveness of cDSMs in describing linguistic phenomena.

Pezzelle, S., Marelli, M. (2020). Do semantic features capture a syntactic classification of compounds? Insights from compositional distributional semantics. In The Role of Constituents in Multiword Expressions: An Interdisciplinary, Cross-Lingual Perspective (pp. 33-60). Language Science Press, Berlin [10.5281/zenodo.3598556].

Do semantic features capture a syntactic classification of compounds? Insights from compositional distributional semantics

Marelli, M
2020

Abstract

Classifying compound words has been the ultimate goal of much research in formal linguistics. A popular, cross-linguistically applicable classification (Bisetto & Scalise 2005) distinguishes three main types of compounds, namely Subordinate, Attributive, and Coordinate on the basis of the underlying syntactic relation between the compound elements. Similar tripartitions have also been proposed in cognitive psychology by works exploring conceptual combination. Focusing on the type of semantic interpretation assigned to novel combinations, three main classes have been traditionally described, namely Relation-linking, Property-mapping, and Hybrid or Conjunctive (see Wisniewski 1996). Based on these commonalities, we conjecture that syntax-based compound types might also be explained by means of the semantic properties of the compound and its constituents. Using a compositional model of distributional semantics (cDSM), we show that (a) the contribution of each constituent in determining the meaning of the compound and (b) the semantic similarity between the two constituent words are significant predictors of these classes. These findings suggest that the various compound types identified by syntactic criteria can also be predicted by means of semantic features. On the one hand, this confirms the validity of the proposed linguistic categorization. On the other hand, we bring further evidence proving the effectiveness of cDSMs in describing linguistic phenomena.
Capitolo o saggio
compositionality, compound words, distributional semantic models, compound headedness
English
The Role of Constituents in Multiword Expressions: An Interdisciplinary, Cross-Lingual Perspective
2020
9783961101856
Language Science Press, Berlin
33
60
Pezzelle, S., Marelli, M. (2020). Do semantic features capture a syntactic classification of compounds? Insights from compositional distributional semantics. In The Role of Constituents in Multiword Expressions: An Interdisciplinary, Cross-Lingual Perspective (pp. 33-60). Language Science Press, Berlin [10.5281/zenodo.3598556].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/267348
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