2004 ModelsForTheSemanticClassifOfNounPhrases

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Subject Headings: Semantic Relation, Semantic Relation Detection Algorithm, Language Computer Corporation

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Abstract

  • 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.

Basic Approach

  • We approach the problem top-down, namely identify and study first the characteristics or feature vectors of each noun phrase linguistic pattern, then develop models for their semantic classification. This is in contrast to our prior approach ((Girju, Badulescu, and Moldovan 2003a)) when we studied one relation at a time, and learned constraints to identify only that relation. We study the distribution of the semantic relations across different NP patterns and analyze the similarities and differences among resulting semantic spaces. We define a semantic space as the set of semantic relations an NP construction can encode. We aim at uncovering the general aspects that govern the NP semantics, and thus delineate the semantic space within clusters of semantic relations. This process has the advantage of reducing the annotation effort, a time consuming activity. Instead of manually annotating a corpus for each semantic relation, we do it only for each syntactic pattern and get a clear view of its semantic space. This syntactico-semantic approach allows us to explore various NP semantic classification models in a unified way.
  • This approach stemmed from our desire to answer questions such as:
    • 1. What influences the semantic interpretation of various linguistic constructions?
    • 2. Is there only one interpretation system/model that

works best for all types of expressions at all syntactic levels? and

    • 3. What parameters govern the models capable of semantic interpretation of various syntactic constructions?
  • Table 1: A list of semantic relations at various syntactic levels (including NP level), their definitions, some examples, and references.
    • 1 POSSESSION an animate entity possesses (owns) another entity; (family estate; the girl has a new car.), (Vanderwende 1994)
    • 2 KINSHIP an animated entity related by blood, marriage, adoption or strong affinity to another animated entity; (Mary’s daughter; my sister); (Levi 1979)
    • 3 PROPERTY/ characteristic or quality of an entity/event/state; (red rose; The thunderstorm was awful.); (Levi 1979) ATTRIBUTE-HOLDER
    • 4 AGENT the doer or instigator of the action denoted by the predicate; (employee protest; parental approval; The king banished the general.); (Baker, Fillmore, and Lowe 1998)
    • 5 TEMPORAL time associated with an event; (5-o’clock tea; winter training; the store opens at 9 am), includes DURATION (Navigli and Velardi 2003),
    • 6 DEPICTION- an event/action/entity depicting another event/action/entity; (A picture of my niece.), DEPICTED
    • 7 PART-WHOLE an entity/event/state is part of another entity/event/state (door knob; door of the car), (MERONYMY) (Levi 1979), (Dolan et al. 1993),
    • 8 HYPERNYMY an entity/event/state is a subclass of another; (daisy flower; Virginia state; large company, such as Microsoft) (IS-A) (Levi 1979), (Dolan et al. 1993)
    • 9 ENTAIL an event/state is a logical consequence of another; (snoring entails sleeping)
    • 10 CAUSE an event/state makes another event/state to take place; (malaria mosquitoes; to die of hunger; The earthquake generated a Tsunami), (Levi 1979)
    • 11 MAKE/PRODUCE an animated entity creates or manufactures another entity; (honey bees; nuclear power plant; GM makes cars) (Levi 1979)
    • 12 INSTRUMENT an entity used in an event/action as instrument; (pump drainage; the hammer broke the box) (Levi 1979)
    • 13 LOCATION/SPACE spatial relation between two entities or between an event and an entity; includes DIRECTION; (field mouse; street show; I left the keys in the car), (Levi 1979), (Dolan et al. 1993)
    • 14 PURPOSE a state/action intended to result from a another state/event; (migraine drug; wine glass; rescue mission; He was quiet in order not to disturb her.) (Navigli and Velardi 2003)
    • 15 SOURCE/FROM place where an entity comes from; (olive oil; I got it from China) (Levi 1979)
    • 16 TOPIC an object is a topic of another object; (weather report; construction plan; article about terrorism); (Rosario and Hearst 2001)
    • 17 MANNER a way in which an event is performed or takes place; (hard-working immigrants; enjoy immensely; he died of cancer); (Blaheta and Charniak 2000)
    • 18 MEANS the means by which an event is performed or takes place; (bus service; I go to school by bus.) (Quirk et al.1985)
    • 19 ACCOMPANIMENT one/more entities accompanying another entity involved in an event; (meeting with friends; She came with us) (Quirk et al.1985)
    • 20 EXPERIENCER an animated entity experiencing a state/feeling; (Mary was in a state of panic.); (Sowa 1994)
    • 21 RECIPIENT an animated entity for which an event is performed; (The eggs are for you) ; includes BENEFICIARY; (Sowa 1994)
    • 22 FREQUENCY number of occurrences of an event; (bi-annual meeting; I take the bus every day); (Sowa 1994)
    • 23 INFLUENCE an entity/event that affects other entity/event; (drug-affected families; The war has an impact on the economy.);
    • 24 ASSOCIATED WITH an entity/event/state that is in an (undefined) relation with another entity/event/state; (Jazz-associated company;)
    • 25 MEASURE an entity expressing quantity of another entity/event; (cup of sugar; 70-km distance; centennial rite; The jacket cost $60.)
    • 26 SYNONYMY a word/concept that means the same or nearly the same as another word/concept; (NAME) (Marry is called Minnie); (Sowa 1994)
    • 27 ANTONYMY a word/concept that is the opposite of another word/concept; (empty is the opposite of full); (Sowa 1994)
    • 28 PROBABILITY OF the quality/state of being probable; likelihood

EXISTENCE (There is little chance of rain tonight); (Sowa 1994)

    • 29 POSSIBILITY the state/condition of being possible; (I might go to Opera tonight); (Sowa 1994)
    • 30 CERTAINTY the state/condition of being certain or without doubt; (He definitely left the house this morning);
    • 31 THEME an entity that is changed/involved by the action/event denoted by the predicate; (music lover; John opened the door.); (Sowa 1994)
    • 32 RESULT the inanimate result of the action/event denoted by the predicate; includes EFFECT and PRODUCT. (combustion gases; I finished the task completely.); (Sowa 1994)
    • 33 STIMULUS stimulus of the action or event denoted by the predicate (We saw [the painting]. I sensed [the eagerness] in him. I can see [that you are feeling great].) (Baker, Fillmore, and Lowe 1998)
    • 34 EXTENT the change of status on a scale (by a percentage or by a value) of some entity; (The price of oil increased [ten percent]. Oil’s price increased by [ten percent]. ); (Blaheta and Charniak 2000)
    • 35 PREDICATE expresses the property associated with the subject or the object through the verb; (He feels [sleepy]. They elected him [treasurer]. ) (Blaheta and Charniak 2000)

The data

  • We have assembled a corpus from two sources: Wall Street Journal articles from TREC-9, and eXtended WordNet glosses (XWN) http://xwn.hlt.utdallas.edu). We used XWN 2.0 since all its glosses are syntactically parsed and their words semantically disambiguated which saved us considerable amount of time. Table 2 shows for each syntactic category the number of randomly selected sentences from each corpus, the number of instances found in these sentences, and finally the number of instances that our group managed to annotate by hand. The annotation of each example consisted of specifying its feature vector and the most appropriate semantic relation from those listed in Table 1.

References

  • C. Baker, C. Fillmore, and J. Lowe. (1998). The Berkeley FrameNet Project. In: Proceedings of COLLING/ACL, Canada.
  • D. Blaheta and Eugene Charniak. (2000). Assigning function tags to parsed text. In: Proceedings of the 1st Annual Meeting of the North American Chapterof the Association for Computational Linguistics (NAACL), Seattle, WA.
  • Eugene Charniak. (2001). Immediate-head parsing for language models. In: Proceedings of ACL, Toulouse, France.
  • W. Dolan, L. Vanderwende, and S. Richardson. (1993). Automatically deriving structured KBs from on-line dictionaries. In: Proceedings of the Pacific Association for Computational Linguistics Conference.
  • P. Downing. 1977. On the creation and use of English compound nouns. Language, 53(4), 810-842.
  • T. Finin. (1980). The Semantic Interpretation of Compound Nominals. Ph.D dissertation, University of Illinois, Urbana, Illinois.
  • Daniel Gildea and Daniel Jurafsky. (2002). Automatic Labeling of Semantic Roles. In Computational Linguistics, 28(3).
  • (Gildea & Palmer, 2002) ⇒ Daniel Gildea, and Martha Palmer. (2002). The Necessity of Parsing for Predicate Argument Recognition. In: Proceedings of the 40th Meeting of the Association for Computational Linguistics (ACL 2002).
  • R. Girju, A. Badulescu, and Dan Moldovan. (2003). Learning Semantic Constraints for the Automatic Discovery of Part-Whole Relations. In: Proceedings of the Human Language Technology Conference (HLT-03), Canada.
  • R. Girju, A.M. Giuglea, M. Olteanu, O. Fortu, and Dan Moldovan. (2004). Support Vector Machines Applied to the Classification of Semantic Relations in Nominalized Noun Phrases. In: Proceedings of HLT/NAACL 2004 - Computational Lexical Semantics workshop, Boston, MA.
  • (Kingsbury et al., 2002) ⇒ P. Kingsbury, Martha Palmer, and M. Marcus. (2002). Adding Semantic Annotation to the Penn TreeBank. In: Proceedings of the Human Language Technology Conference (HLT 2002), California.
  • M. Lapata. (2002). The Disambiguation of Nominalisations. In Computational Linguistics 28:3, 357-388.
  • M. Lauer and M. Dras. (1994). A probabilistic model of compound nouns. In: Proceedings of the 7th Australian Joint Conference on AI.
  • M. Lauer. (1995). Designing Statistical Language Learners: Experiments on Compound Nouns. In PhD thesis, Macquarie University, Sidney.
  • Judith Levi. 1979. The Syntax and Semantics of Complex Nominals. New York: Academic Press.
  • Roberto Navigli and Paola Velardi. (2003). Ontology Learning and Its Application to Automated Terminology Translation. In IEEE Intelligent Systems.
  • James Pustejovsky, S. Bergler, and P. Anick. (1993). Lexical semantic techniques for corpus analysis. In Computational Linguistics, 19(2).
  • R. Quirk, S. Greenbaum, G. Leech, and J. Svartvik. 1985. A comprehensive grammar of english language, Longman, Harlow.
  • B. Rosario and Marti Hearst. (2001). Classifying the Semantic Relations in Noun Compounds via a Domain- Specific Lexical Hierarchy. In the Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing, (EMNLP 2001), Pittsburgh, PA.
  • B. Rosario, Marti Hearst, and C. Fillmore. (2002). The Descent of Hierarchy, and Selection in Relational Semantics. In the Proceedings of the Association for Computational Linguistics (ACL-02), University of Pennsylvania.
  • M. Semmelmeyer and D. Bolander. (1992). The New Webster’s Grammar Guide. Lexicon Publications, Inc.
  • J. F. Sowa. (1994). Conceptual Structures: Information Processing in Mind and Machine. AddisonWesley.
  • L. Vanderwende. (1994). Algorithm for automatic interpretation of noun sequences. In: Proceedings of COLING-94, pg. 782-788.,


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2004 ModelsForTheSemanticClassifOfNounPhrasesDan I. Moldovan
Adriana Badulescu
Marta Tatu
Daniel Antohe
Roxana Girju
Models for the Semantic Classification of Noun PhrasesProceedings of HLT/NAACL Workshop on Computational Lexical Semanticshttp://acl.ldc.upenn.edu/hlt-naacl2004/CLS/pdf/moldovan.pdf2004