Semi-automatic recognition of noun modifier relationships

Abstract

Semantic relationships among words and phrases are often marked by explicit syntactic or lexical clues that help recognize such relationships in texts. Within complex nominals, however, few overt clues are available. Systems that analyze such nominals must compensate for the lack of surface clues with other information. One way is to load the system with lexical semantics for nouns or adjectives. This merely shifts the problem elsewhere: how do we define the lexical semantics and build large semantic lexicons? Another way is to find constructions similar to a given complex nominal, for which the relationships are already known. This is the way we chose, but it too has drawbacks. Similarity is not easily assessed, similar analyzed constructions may not exist, and if they do exist, their analysis may not be appropriate for the current nominal. We present a semi-automatic system that identifies semantic relationships in noun phrases without using precoded noun or adjective semantics. Instead, partial matching on previously analyzed noun phrases leads to a tentative interpretation of a new input. Processing can start without prior analyses, but the early stage requires user interaction. As more noun phrases are analyzed, the system learns to find better interpretations and reduces its reliance on the user. In experiments on English technical texts the system correctly identified 60-70% of relationships automatically.

A lexical semantic approach to interpreting and bracketing English noun compounds

“…Noun compound interpretation has a long history in both theoretical and computational linguistic research (Downing 1977;Levi 1978;Finin 1980;Vanderwende 1994;Barker and Szpakowicz 1998;Rosario and Hearst 2001;Lapata 2002;Moldovan et al 2004;Kim and Baldwin 2005;Girju 2007). Conventionally, SRs are used to describe how the components of a given NC interact with each other.…”

“…One drawback was that the system employed handwritten rules, making it labor-intensive and hard to repurpose to new domains or relation sets. Barker and Szpakowicz (1998) developed a larger, more general-purpose set of SRs (detailed in Table 1), and evaluated a semi-automatic NC interpretation method over instances from the technical repair domain. More recent work on SR-based interpretation has focused on fully automatic methods, over a broad range of different SR sets (Fan et al 2003;Girju 2007;Ó Séaghdha 2009;Tratz and Hovy 2010).…”

Schema label normalization for improving schema matching

“…Endocentric CNs are often not included in dictionaries, but they can be interpreted by using the knowledge about their constituents. Based on this property, endocentric CNs can be also defined as transparent [16].…”

“…• Precision = jTP j jTP j + jFP j 16 All the data sets are publicly available at http://queens.db.toronto.edu/project/clio/index.php/#testschemas and http://dbs.uni-leipzig.de/Research/ coma_index.html. • Recall = jTP j jFN j + jTP j…”

“…Each data set consists of two schemata that need to be integrated. These data sets 16 have been used in several schema matching experiments [28,29]. Fig.…”