In this paper we explore the role of verb frequencies and the number of clusters in soft-clustering approaches as a tool for automatic semantic classification. Relying on a large-scale setup including 4,871 base verb types and 3,173 complex verb types, and focusing on synonymy as a taskindependent goal in semantic classification, we demonstrate that low-frequency German verbs are clustered sign...

1. Goals and Methodology The aim of this abstract is to introduce LexIt, a freely available lexical resource to characterize Italian verb argument properties in terms of distributional information automatically extracted from large corpora with state-of-the-art computational linguistics methods. Research on automatic extraction of subcategorization frames from corpora has a long tradition in co...

Although English verbs can be either regular (walk-walked) or irregular (sing-sang), "denominal verbs" that are derived from nouns, such as the use of the verb ring derived from the noun a ring, take the regular form even if they are homophonous with an existing irregular verb: The soldiers ringed the city rather than *The soldiers rang the city. Is this regularization due to a semantic differe...

Aiming to create verb paraphrases to lay the foundation of sentence paraphrases I automatically created Hungarian semantic verb classes with k-means algorithm. The vector representation of verbs was special: dimensions were cases and values were sets of lemmas that can fill the verb frame position defined by the case. I clustered 900 frequent verbs, from which 243 got into 71 smaller clusters, ...

German particle verbs are a type of multi word expression which is often compositional with respect to a base verb. If they are compositional they tend to express the same types of semantic arguments, but they do not necessarily express them in the same syntactic subcategorization frame: some arguments may be expressed by differing syntactic subcategorization slots and other arguments may be on...

Where does a verb’s frame come from? The obvious answer is the verb itself, and this is the answer that syntacticians have traditionally provided, whether they describe predicator-argument relations as syntactic sisterhood relations or as lexical properties (the predicator’s combinatoric potential, or valence). Thus, Haegeman, in her introduction to Government and Binding theory, states, “the t...

Most Semantic Role Labeling (SRL) systems rely on available annotated corpora, being PropBank the most widely used corpus so far. Propbank role set is based on theory-neutral numbered arguments, which are linked to fine grained verb-dependant semantic roles through the verb framesets. Recently, thematic roles from the computational verb lexicon VerbNet have been suggested to be more adequate fo...

Multiple emotions are often triggered in readers in response to text stimuli like news article. In this paper, we present a novel method for classifying news sentences into multiple emotion categories using an ensemble based multi-label classification technique called RAKEL. The emotion data consists of 1305 news sentences and the emotion classes considered are disgust, fear, happiness and sadn...