Systems that automatically discover semantic classes have emerged in part to address the limitations of broad-coverage lexical resources such as WordNet and Cyc. The current state of the art discovers many semantic classes but fails to label their concepts. We propose an algorithm labeling semantic classes and for leveraging them to extract is-a relationships using a top-down approach.

Semantic Role Labeling (SRL) captures semantic roles (or participants) such as agent, patient, and theme associated with verbs from the text. While it provides important intermediate semantic representations for many traditional NLP tasks (such as information extraction and question answering), it does not capture grounded semantics so that an artificial agent can reason, learn, and perform the...

This paper describes our contribution to the semantic role labeling task (SRL-only) of the CoNLL-2009 shared task in the closed challenge (Hajič et al., 2009). Our system consists of a pipeline of independent, local classifiers that identify the predicate sense, the arguments of the predicates, and the argument labels. Using these local models, we carried out a beam search to generate a pool of...

We present a four-step hierarchical SRL strategy which generalizes the classical two-level approach (boundary detection and classification). To achieve this, we have split the classification step by grouping together roles which share linguistic properties (e.g. Core Roles versus Adjuncts). The results show that the nonoptimized hierarchical approach is computationally more efficient than the t...

Semantic role labeling (SRL) focuses on recognizing the predicate–argument structure of a sentence and plays critical in many natural language processing tasks such as machine translation question answering. Practically all available methods do not perform full SRL, since they rely pre-identified predicates, most them follow pipeline strategy, using specific models for undertaking one or severa...

Semantic roles, logical relations such as AGENT or INSTRUMENT that hold between events and their participants and circumstances, need to be determined automatically by several types of applications in natural language processing. This process is referred to as semantic role labeling. This dissertation describes how to construct statistical models for semantic role labeling of English text, and ...