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Semeval-2010 Task 9: the Interpretation of Noun Compounds Using Paraphrasing Verbs and Prepositions

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Semeval-2010 Task 9: the Interpretation of Noun Compounds Using Paraphrasing Verbs and Prepositions SemEval-2010 Task 9: The Interpretation of Noun Compounds Using Paraphrasing Verbs and Prepositions Cristina Butnariu Su Nam Kim Preslav Nakov University College Dublin University of Melbourne National University of Singapore [email protected] [email protected] [email protected] Diarmuid OS´ eaghdha´ Stan Szpakowicz Tony Veale University of Cambridge University of Ottawa University College Dublin [email protected] Polish Academy of Sciences [email protected] [email protected] Abstract the factors of high frequency and high productiv- ity mean that achieving robust NC interpretation is We present a brief overview of the main an important goal for broad-coverage semantic pro- challenges in understanding the semantics of noun compounds and consider some known cessing. NCs provide a concise means of evoking a methods. We introduce a new task to be relationship between two or more nouns, and natu- part of SemEval-2010: the interpretation of ral language processing (NLP) systems that do not noun compounds using paraphrasing verbs try to recover these implicit relations from NCs are and prepositions. The task is meant to provide effectively discarding valuable semantic informa- a standard testbed for future research on noun tion. Broad coverage should therefore be achieved compound semantics. It should also promote by post-hoc interpretation rather than pre-hoc enu- paraphrase-based approaches to the problem, which can benefit many NLP applications. meration, since it is impossible to build a lexicon of all NCs likely to be encountered. 1 Introduction The challenges presented by NCs and their se- mantics have generated significant ongoing interest Noun compounds (NCs) – sequences of two or more in NC interpretation in the NLP community. Repre- 1 nouns acting as a single noun, e.g., colon cancer sentative publications include (Butnariu and Veale, tumor suppressor protein – are abundant in English 2008; Girju, 2007; Kim and Baldwin, 2006; Nakov, and pose a major challenge to the automatic anal- 2008b; Nastase and Szpakowicz, 2003; OS´ eaghdha´ ysis of written text. Baldwin and Tanaka (2004) and Copestake, 2007). Applications that have been calculated that 3.9% and 2.6% of the tokens in suggested include Question Answering, Machine the Reuters corpus and the British National Corpus Translation, Information Retrieval and Information (BNC), respectively, are part of a noun compound. Extraction. For example, a question-answering sys- Compounding is also an extremely productive pro- tem may need to determine whether headaches in- cess in English. The frequency spectrum of com- duced by caffeine withdrawal is a good paraphrase pound types follows a Zipfian or power-law distribu- for caffeine headaches when answering questions ´ tion (OSeaghdha,´ 2008), so in practice many com- about the causes of headaches, while an information pound tokens encountered belong to a “long tail” extraction system may need to decide whether caf- of low-frequency types. For example, over half of feine withdrawal headache and caffeine headache the two-noun NC types in the BNC occur just once refer to the same concept when used in the same (Lapata and Lascarides, 2003). Even for relatively document. Similarly, a machine translation system frequent NCs that occur ten or more times in the facing the unknown NC WTO Geneva headquarters BNC, static English dictionaries give only 27% cov- might benefit from the ability to paraphrase it as erage (Tanaka and Baldwin, 2003). Taken together, Geneva headquarters of the WTO or as WTO head- 1We follow the definition in (Downing, 1977). quarters located in Geneva. Given a query like can- 100 Proceedings of the NAACL HLT Workshop on Semantic Evaluations: Recent Achievements and Future Directions, pages 100–105, Boulder, Colorado, June 2009. c 2009 Association for Computational Linguistics cer treatment, an information retrieval system could In computational linguistics, popular invento- use suitable paraphrasing verbs like relieve and pre- ries of semantic relations have been proposed by vent for page ranking and query refinement. Nastase and Szpakowicz (2003) and Girju et al. In this paper, we introduce a new task, which will (2005), among others. The former groups 30 fine- be part of the SemEval-2010 competition: NC inter- grained relations into five coarse-grained super- pretation using paraphrasing verbs and prepositions. categories, while the latter is a flat list of 21 re- The task is intended to provide a standard testbed lations. Both schemes are intended to be suit- for future research on noun compound semantics. able for broad-coverage analysis of text. For spe- We also hope that it will promote paraphrase-based cialized applications, however, it is often useful approaches to the problem, which can benefit many to use domain-specific relations. For example, NLP applications. Rosario and Hearst (2001) propose 18 abstract rela- The remainder of the paper is organized as fol- tions for interpreting NCs in biomedical text, e.g., lows: Section 2 presents a brief overview of the DEFECT, MATERIAL, PERSON AFFILIATED, existing approaches to NC semantic interpretation ATTRIBUTE OF CLINICAL STUDY. and introduces the one we will adopt for SemEval- Inventory-based analyses offer significant advan- 2010 Task 9; Section 3 provides a general descrip- tages. Abstract relations such as ‘location’ and ‘pos- tion of the task, the data collection, and the evalua- session’ capture valuable generalizations about NC tion methodology; Section 4 offers a conclusion. semantics in a parsimonious framework. Unlike paraphrase-based analyses (Section 2.2), they are 2 Models of Relational Semantics in NCs not tied to specific lexical items, which may them- selves be semantically ambiguous. They also lend 2.1 Inventory-Based Semantics themselves particularly well to automatic interpreta- The prevalent view in theoretical and computational tion methods based on multi-class classification. linguistics holds that the semantic relations that im- On the other hand, relation inventories have been plicitly link the nouns of an NC can be adequately criticized on a number of fronts, most influentially enumerated via a small inventory of abstract re- by Downing (1977). She argues that the great vari- lational categories. In this view, mountain hut, ety of NC relations makes listing them all impos- field mouse and village feast all express ‘location sible; creative NCs like plate length (‘what your in space’, while the relation implicit in history book hair is when it drags in your food’) are intuitively and nativity play can be characterized as ‘topicality’ compositional, but cannot be assigned to any stan- or ‘aboutness’. A sample of some of the most influ- dard inventory category. A second criticism is that ential relation inventories appears in Table 1. restricted inventories are too impoverished a repre- Levi (1978) proposes that complex nominals – sentation scheme for NC semantics, e.g., headache a general concept grouping together nominal com- pills and sleeping pills would both be analyzed as pounds (e.g., peanut butter), nominalizations (e.g., FOR in Levi’s classification, but express very differ- dream analysis) and non-predicative noun phrases ent (indeed, contrary) relationships. Downing writes (e.g., electric shock) – are derived through the com- (p. 826): “These interpretations are at best reducible plementary processes of recoverable predicate dele- to underlying relationships. , but only with the loss tion and nominalization; each process is associated of much of the semantic material considered by sub- with its own inventory of semantic categories. Table jects to be relevant or essential to the definitions.” 1 lists the categories for the former. A further drawback associated with sets of abstract Warren (1978) posits a hierarchical classifica- relations is that it is difficult to identify the “correct” tion scheme derived from a large-scale corpus study inventory or to decide whether one proposed classi- of NCs. The top-level relations in her hierar- fication scheme should be favored over another. chy are listed in Table 1, while the next level subdivides CONSTITUTE into SOURCE-RESULT, 2.2 Interpretation Using Verbal Paraphrases RESULT-SOURCE and COPULA; COPULA is then An alternative approach to NC interpretation asso- further subdivided at two additional levels. ciates each compound with an explanatory para- 101 Author(s) Relation Inventory Levi (1978) CAUSE, HAVE, MAKE, USE, BE, IN, FOR, FROM, ABOUT Warren (1978) POSSESSION, LOCATION, PURPOSE, ACTIVITY-ACTOR, RESEMBLANCE, CONSTITUTE Nastase and CAUSALITY (cause, effect, detraction, purpose), Szpakowicz PARTICIPANT (agent, beneficiary, instrument, object property, (2003) object, part, possessor, property, product, source, whole, stative), QUALITY (container, content, equative, material, measure, topic, type), SPATIAL (direction, location at, location from, location), TEMPORALITY (frequency, time at, time through) Girju et al. (2005) POSSESSION, ATTRIBUTE-HOLDER, AGENT, TEMPORAL, PART-WHOLE, IS-A, CAUSE, MAKE/PRODUCE, INSTRUMENT, LOCATION/SPACE, PURPOSE, SOURCE, TOPIC, MANNER, MEANS, THEME, ACCOMPANIMENT, EXPERIENCER, RECIPIENT, MEASURE, RESULT Lauer (1995) OF, FOR, IN, AT, ON, FROM, WITH, ABOUT Table 1: Previously proposed inventories of semantic relations for noun compound interpretation. The first two come from linguistic theories; the rest have been proposed in computational linguistics. phrase. Thus, cheese knife and kitchen knife can be bee is a bee that produces honey, a sleeping pill
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