Models For The Semantic Classification Of Noun Phrases

This paper presents an approach for detecting semantic relations in noun phrases. A learning algorithm, called semantic scattering, is used to automatically label complex nominals, genitives and adjectival noun phrases with the corresponding semantic relation. 1 Problem description This paper is about the automatic labeling of semantic relations in noun phrases (NPs). The semantic relations are the underlying relations between two concepts expressed by words or phrases. We distinguish here between semantic relations and semantic roles. Semantic roles are always between verbs (or nouns derived from verbs) and other constituents (run quickly, went to the store, computer maker), whereas semantic relations can occur between any constituents, for example in complex nominals (malaria mosquito (CAUSE)), genitives (girl’s mouth (PART-WHOLE)), prepositional phrases attached to nouns (man at the store (LOCATIVE)), or discourse level (The bus was late. As a result, I missed my appointment (CAUSE)). Thus, in a sense, semantic relations are more general than semantic roles and many semantic role types will appear on our list of semantic relations. The following NP level constructions are considered here (cf. the classifications provided by (Quirk et al.1985) and (Semmelmeyer and Bolander 1992)): (1) Compound Nominals consisting of two consecutive nouns (eg night club a TEMPORAL relation indicating that club functions at night), (2) Adjective Noun constructions where the adjectival modifier is derived from a noun (eg musical clock a MAKE/PRODUCE relation), (3) Genitives (eg the door of the car a PART-WHOLE relation), and (4) Adjective phrases (cf. (Semmelmeyer and Bolander 1992)) in which the modifier noun is expressed by a prepositional phrase which functions as an adjective (eg toy in the box a LOCATION relation). Example: “Saturday’s snowfall topped a one-day record in Hartford, Connecticut, with the total of 12.5 inches, the weather service said. The storm claimed its fatality Thursday, when a car which was driven by a college student skidded on an interstate overpass in the mountains of Virginia and hit a concrete barrier, police said”. (www.cnn.com “Record-setting Northeast snowstorm winding down”, Sunday, December 7, 2003). There are several semantic relations at the noun phrase level: (1) Saturday’s snowfall is a genitive encoding a TEMPORAL relation, (2) one-day record is a TOPIC noun compound indicating that record is about one-day snowing an ellipsis here, (3) record in Hartford is an adjective phrase in a LOCATION relation, (4) total of 12.5 inches is an of-genitive that expresses MEASURE, (5) weather service is a noun compound in a TOPIC relation, (6) car which was driven by a college student encodes a THEME semantic role in an adjectival clause, (7) college student is a compound nominal in a PART-WHOLE/MEMBER-OF relation, (8) interstate overpass is a LOCATION noun compound, (9) mountains of Virginia is an of-genitive showing a PART-WHOLE/PLACE-AREA and LOCATION relation, (10) concrete barrier is a noun compound encoding PART-WHOLE/STUFF-OF. 1.1 List of Semantic Relations After many iterations over a period of time we identified a set of semantic relations that cover a large majority of text semantics. Table 1 lists these relations, their definitions, examples, and some references. Most of the time, the semantic relations are encoded by lexico-syntactic patterns that are highly ambiguous. One pattern can express a number of semantic relations, its disambiguation being provided by the context or world knowledge. Often semantic relations are not disjoint or mutually exclusive, two or more appearing in the same lexical construct. This is called semantic blend (Quirk et al.1985). For example, the expression “Texas city” contains both a LOCATION as well as a PART-WHOLE relation. Other researchers have identified other sets of semantic relations (Levi 1979), (Vanderwende 1994), (Sowa 1994), (Baker, Fillmore, and Lowe 1998), (Rosario and Hearst 2001), (Kingsbury, et al. 2002), (Blaheta and Charniak 2000), (Gildea and Jurafsky 2002), (Gildea and Palmer 2002). Our list contains the most frequently used semantic relations we have observed on a large corpus. Besides the work on semantic roles, considerable interest has been shown in the automatic interpretation of complex nominals, and especially of compound nominals. The focus here is to determine the semantic relations that hold between different concepts within the same phrase, and to analyze the meaning of these compounds. Several approaches have been proposed for empirical noun-compound interpretation, such as syntactic analysis based on statistical techniques (Lauer and Dras 1994), (Pustejovsky et al. 1993). Another popular approach focuses on the interpretation of the underlying semantics. Many researchers that followed this approach relied mostly on hand-coded rules (Finin 1980), (Vanderwende 1994). More recently, (Rosario and Hearst 2001), (Rosario, Hearst, and Fillmore 2002), (Lapata 2002) have proposed automatic methods that analyze and detect noun compounds relations from text. (Rosario and Hearst 2001) focused on the medical domain making use of a lexical ontology and standard machine learning techniques.