We show that it is possible to reliably discriminate whether a syntactic construction is meant
literally or metaphorically using lexical semantic features of the words that participate in the …

Unsupervisedly learned word vectors have proven to provide exceptionally effective features
in many NLP tasks. Most common intrinsic evaluations of vector quality measure correlation …

This paper introduces a task of identifying and semantically classifying lexical expressions in
running text. We investigate the online reviews genre, adding semantic supersense …

This task combines the labeling of multiword expressions and supersenses (coarse-grained
classes) in an explicit, yet broad-coverage paradigm for lexical semantics. Nine systems …

Coarse-grained semantic categories such as supersenses have proven useful for a range of
downstream tasks such as question answering or machine translation. To date, no effort has …

In lexical semantics, full-sentence segmentation and segment labeling of various
phenomena are generally treated separately, despite their interdependence. We …

We present the first corpus annotated with preposition supersenses, unlexicalized
categories for semantic functions that can be marked by English prepositions (Schneider et …

The central goal of this thesis is to bridge the divide between theoretical linguistics—the
scientific inquiry of language—and applied data-driven statistical language processing, to …

Computer programs that make inferences about natural language are easily fooled by the
often haphazard relationship between words and their meanings. This thesis develops …

As empirically demonstrated by the Word Sense Disambiguation (WSD) tasks of the last
SensEval/SemEval exercises, assigning the appropriate meaning to words in context has …