Segmentation and (segment ) labeling are generally treated separately in lexical semantics, raising issues due to their close inter-dependence and necessitating joint annotation. We therefore investigate the lexical semantic recognition task of multiword expression segmentation and supersense disambiguation, unifying several previously-disparate styles of lexical semantic annotation. We evaluate a neural CRF model along all annotation axes available in version 4.3 of the STREUSLE corpus: lexical unit segmentation (multiword expressions), word-level syntactic tags, and supersense classes for noun, verb, and preposition/possessive units . As the label set generalizes that of previous tasks ( DiMSUM, PARSEME ), we additionally evaluate how well the model generalizes to those test sets, with encouraging results. By establishing baseline models and evaluation metrics , we pave the way for comprehensive and accurate modeling of lexical semantics  .
In lexical semantics, full-sentence segmentation and segment labeling of various phenomena are generally treated separately , despite their interdependence. We hypothesize that a unified lexical semantic recognition task is an effective way to encapsulate previously disparate styles of annotation, including multiword expression identification / classification and supersense tagging. Using the STREUSLE corpus, we train a neural CRF sequence tagger and evaluate its performance along various axes of annotation . As the label set generalizes that of previous tasks ( PARSEME, DiMSUM ), we additionally evaluate how well the model generalizes to those test sets, finding that it approaches or surpasses existing models despite training only on STREUSLE. Our work also establishes baseline models and evaluation metrics for integrated and accurate modeling of lexical semantics , facilitating future work in this area .
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Segmentation and (segment ) labeling
In lexical semantics, full-sentence segmentation and segment labeling of various phenomena
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in lexical semantics, raising issues due to their close inter-dependence and necessitating joint annotation. We therefore investigate the
, despite their interdependence. We hypothesize that a unified
R
of multiword expression segmentation and supersense disambiguation, unifying several previously-disparate styles of lexical semantic annotation. We evaluate
is an effective way to encapsulate previously disparate styles of annotation, including multiword expression identification / classification and supersense tagging. Using the STREUSLE corpus, we train
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model along all annotation axes available in version 4.3 of the STREUSLE corpus: lexical unit segmentation (multiword expressions), word-level syntactic tags, and supersense classes for noun, verb, and preposition/possessive units
sequence tagger and evaluate its performance along various axes of annotation
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DiMSUM, PARSEME
PARSEME, DiMSUM
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with encouraging results. By establishing
finding that it approaches or surpasses existing models despite training only on STREUSLE. Our work also establishes
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, we pave the way for comprehensive
for integrated
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, facilitating future work in this area