2.2 Semantic Roles

This metric first counts the number of lexical overlaps SR-Or-t for all the different semantic roles I that are found in the system and reference translation sentence.

In their work, the different semantic roles r they considered include the various core and adjunct arguments as defined in the PropBank project (Palmer et al., 2005).

Besides matching a pair of system-reference sentences based on the surface form of words, previous work such as (Gimenez and Marquez, 2007) and (Rajman and Hartley, 2002) had shown that deeper linguistic knowledge such as semantic roles and syntax can be usefully exploited.

Possible future directions include adding semantic role information, using the distance between item pairs based on the token position within each sentence as additional weighting consideration, etc.

We apply our simplification system to semantic role labeling (SRL).

We evaluated our system using the setup of the Conll 2005 semantic role labeling task.2 Thus, we trained on Sections 2-21 of PropBank and used Section 24 as development data.

In semantic role labeling (SRL), given a sentence containing a target verb, we want to label the semantic arguments, or roles, of that verb.

Current semantic role labeling systems rely primarily on syntactic features in order to identify and

This allows us to learn that “give” has a preference for the labeling {ARGO = Subject NP, ARGI = Postverb NP2, ARGZ = Postverb NP1 Our final features are analogous to those used in semantic role labeling, but greatly simplified due to our use of simple sentences: head word of the constituent; category (i.e., constituent label); and position in the simple sentence.

Another area of related work in the semantic role labeling literature is that on tree kernels (Moschitti, 2004; Zhang et al., 2007).

This paper presents an empirical study on the robustness and generalization of two alternative role sets for semantic role labeling: PropBank numbered roles and VerbNet thematic roles.

Our basic Semantic Role Labeling system represents the tagging problem as a Maximum Entropy Markov Model (MEMM).

Semantic Role Labeling is the problem of analyzing clause predicates in open text by identifying arguments and tagging them with semantic labels indicating the role they play with respect to the verb.

While Arg0 and Argl are intended to indicate the general roles of Agent and Theme, other argument numbers do not generalize across verbs and do not correspond to general semantic roles .

Many scholars hypothesize that the behavior of a verb, particularly with respect to the expression of arguments and the assignment of semantic roles is to a large extent driven by deep semantic regularities (Dowty, 1991; Green, 1974; Goldberg, 1995; Levin, 1993).

When the information about a verb type is not available or sufficient for us to draw firm conclusions about its usage, the information about the class to which the verb type belongs can compensate for it, addressing the pervasive problem of data sparsity in a wide range of NLP tasks, such as automatic extraction of subcategorization frames (Korhonen, 2002), semantic role labeling (Swier and Stevenson, 2004; Gildea and Juraf-sky, 2002), natural language generation for machine translation (Habash et al., 2003), and deriving predominant verb senses from unlabeled data (Lapata and Brew, 2004).

0 Animacy of NPs: The animacy of the semantic role corresponding to the head noun in each syntactic slot can also distinguish classes of verbs.