International audienceIn this paper we propose WordNet as an alternative to ontologies for the purpose of natural language generation. In particular , the synset-based structure of WordNet proves useful for the lexicalization of concepts , by providing ready lists of lemmas for each concept to generate

Basile, Valerio

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17 October 2023AperTO - Archivio Istituzionale Open Access dell'Università di TorinoOriginal Citation:WordNet as an Ontology for GenerationTerms of use:Open Access(Article begins on next page)Anyone can freely access the full text of works made available as "Open Access". Works made availableunder a Creative Commons license can be used according to the terms and conditions of said license. Useof all other works requires consent of the right holder (author or publisher) if not exempted from copyrightprotection by the applicable law.Availability:This is the author's manuscriptThis version is available http://hdl.handle.net/2318/1760027 since 2020-10-28T16:21:06ZHAL Id: hal-01195793https://hal.inria.fr/hal-01195793Submitted on 9 Sep 2015HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.WordNet as an Ontology for GenerationValerio BasileTo cite this version:Valerio Basile. WordNet as an Ontology for Generation. WebNLG 2015 1st International Workshopon Natural Language Generation from the Semantic Web, Jun 2015, Nancy, France. ￿hal-01195793￿WordNet as an Ontology for GenerationValerio BasileUniversity of Groningenv.basile@rug.nlAbstractIn this paper we propose WordNet as analternative to ontologies for the purpose ofnatural language generation. In particu-lar, the synset-based structure of WordNetproves useful for the lexicalization of con-cepts, by providing ready lists of lemmasfor each concept to generate.1 IntroductionWhile RDF/OWL ontologies are a popular for-mat to encode domain and general knowledge,there are alternatives. It is debatable, for instance,whether one should consider WordNet (Miller,1995) a computational ontology in the propersense. This paper tries to answer to this question,by providing an overview of WordNet, an argu-ment for its employment as a knowledge base forgeneration, and a practical example involving lex-ical choice.WordNet has been used both as a standaloneknowledge base and as a mean to augment ex-isting RDF/OWL ontologies (Lin and Sandkuhl,2008). Despite its wide application in other fields,such as, for instance, word sense disambigua-tion, WordNet has been rarely been applied toNLG. A notable exception is the work by Jing(1998) who proposes WordNet-based methods toaddress specific NLG tasks, in particular lexical-ization and paraphrasing. The author shows thatthe open-domain nature of WordNet makes it arobust knowledge base to support generation inopen-domain scenarios, but also that it can alsoused in combination with other knowledge bases,i.e., to adapt to a particular domain.2 WordNetThe book detailing the WordNet project is titled“WordNet: an Electronic Lexical Database”, thusas a starting point the resource can be defined asa structured database of words in a format read-able by electronic calculators. For each word inthe database, WordNet provides a list of sensesand their definition in plain English. The senses,besides having a inner identifier, are representedas synsets, i.e., sets of synonym words. Wordsin general belong to multiple synsets, as theyhave more than one sense, so the relation betweenwords and synsets in WordNet is a many-to-manyone. The synsets are grouped into four categoriesbased on part of speech: noun, verb, adjective oradverb.WordNet is more than only an electronic dic-tionary though. As the “net” in the name sug-gests, WordNet not only contains words and theirdefinitions, but also a whole set of relations de-fined among the word senses. In particular, thehyponymy relation between noun synsets inducesa taxonomical structure of concepts.Figure 1 shows a screenshot of the WordNet 3.1search Web interface1 used to search for the wordbox. Clicking on the inherited hypernym link un-der the first sense of the word, the full hypernymchain is shown, all the way up to the root nodeentity.3 WordNet as an OntologyAn ontology is “an explicit specification of a con-ceptualization” (Gruber, 1993), a collection offacts about some domain of defined entities2. On-tologies vary on different dimensions, includingtheir size, complexity, domain, and specificity.Some ontologies have complex logical formulasamong their rules, while others are more shallowcollections of classes. Rules in an ontology are if-then-like statements describing the logical infer-1http://wordnetweb.princeton.edu/perl/webwn2A more extensive definition of ontology in the field ofcomputer science is given in the Encyclopedia of DatabaseSystems (Liu and Özsu, 2009).S: (n) box (a (usually rectangular) container; may have a lid) "he rummaged througha box of spare parts"direct hyponym / full hyponympart meronymdirect hypernym / inherited hypernym / sister termS: (n) container (any object that can be used to hold things (especially alarge metal boxlike object of standardized dimensions that can be loadedfrom one form of transport to another))S: (n) instrumentality, instrumentation (an artifact (or system ofartifacts) that is instrumental in accomplishing some end)S: (n) artifact, artefact (a man made object taken as a whole)S: (n) whole, unit (an assemblage of parts that isregarded as a single entity) "how big is that partcompared to the whole?"; "the team is a unit"S: (n) object, physical object (a tangible and visibleentity; an entity that can cast a shadow) "it was fullof rackets, balls and other objects"S: (n) physical entity (an entity that hasphysical existence)S: (n) entity (that which is perceived orknown or inferred to have its owndistinct existence (living or nonliving))derivationally related formS: (n) box, loge (private area in a theater or grandstand where a small group canwatch the performance) "the royal box was empty"S: (n) box, boxful (the quantity contained in a box) "he gave her a box of chocolates"S: (n) corner, box (a predicament from which a skillful or graceful escape isimpossible) "his lying got him into a tight corner"S: (n) box (a rectangular drawing) "the flowchart contained many boxes"S: (n) box, boxwood (evergreen shrubs or small trees)S: (n) box (any one of several designated areas on a ball field where the batter orcatcher or coaches are positioned) "the umpire warned the batter to stay in thebatter's box"S: (n) box, box seat (the driver's seat on a coach) "an armed guard sat in the box withthe driver"S: (n) box (separate partitioned area in a public place for a few people) "the sentrystayed in his box to avoid the cold"S: (n) box (a blow with the hand (usually on the ear)) "I gave him a good box on theear"Figure 1: The result of the search for box in theWordnet 3.1 search Web interface.ences that can be drawn from assertions.WordNet was created with the initial goal ofproving psycholinguistic models about the men-tal organization of concepts. Nevertheless, theelectronic lexical database has grown more andmore popular among NLP scholars dealing withthe meaning of words and their relations, and alsoamong ontology experts. As a matter of fact,many employ WordNet as an ontology by treatingthe hypernymy relation between synsets as sub-sumption between concepts, or in some cases asan instantiation relation between named entities(cities, countries, people, ...) and their hyponymsin WordNet. Gangemi et al. (2003b) went as faras defining a “complete formal specification of theconceptualizations expressed by means of Word-net’s synsets” in the OntoWordNet project.We argue that WordNet constitutes a solidchoice as knowledge base for generation. Themain argument is that some unique features ofWordNet facilitate the NLG process as designedin the Unboxer pipeline, in particular the fact thatconcepts are represented in WordNet as sets ofwords, ready to be picked up for the generationof surface forms. WordNet can be seen as alightweight ontology about words, senses, and aseries of relations among them, while still beingmore ontology-like than, say, a machine-readabledictionary or a thesaurus. This last point is debat-able, as technically WordNet is a lexical resource,and additional work is necessary to transform itinto a formal ontology specified in some logic for-malism (Gangemi et al., 2003a). Nevertheless, forthe purpose of designing an NLG system, the dif-ference of definitions is not crucial.4 Lexical Choice from WordNet SynsetsIn recent work, we described a novel method forlexicalization that incorporates WordNet as lin-guistic knowledge base to provide natural sound-ing generations. The Ksel algorithm (Basile,2014) exploits the network structure of WordNetin order to reduce the problem of the lexicalizationof concepts to that of lexical choice from synsets.The algorithm works by computing a similarityscore between each candidate lemma and the set ofsynsets in the abstract meaning representation thatis provided as input to the NLG system. This sim-ilarity score is actually an aggregate measure ofthe semantic similarities between the synsets con-taining the candidate lemmas and the synsets inthe input structure. For example, consider an ab-stract meaning representation with three conceptsencoded as WordNet synsets:• c1 = {stimulant, stimulant drug, excitant}“a drug that temporarily quickens some vitalprocess”• c2 = {tonic, restorative} “a medicine thatstrengthens and invigorates”• c3 = {doctor, doc, physician, MD, Dr.,medico} “a licensed medical practitioner”• c4 = {food, nutrient} “any substance that canbe metabolized by an animal to give energyand build tissue”The ksel algorithm will select nutrient over foodas the realization of c4 because the word nutrientis semantically closer to the synsets c1, c2 and c3,based on measures of similarity computed on theWordNet structure (e.g., path distance).5 ConclusionIn this paper we argued in favor of the use ofWordNet as a lexical resource to support naturallanguage generation. Despite it not being a full-fledged ontology, the structure of WordNet has in-teresting features that facilitate tasks such as lexi-cal choice in the context of generation.ReferencesValerio Basile. 2014. A lesk-inspired unsupervisedalgorithm for lexical choice from wordnet synsets.The First Italian Conference on Computational Lin-guistics CLiC-it 2014, page 48.Aldo Gangemi, Nicola Guarino, Claudio Masolo, andAlessandro Oltramari. 2003a. Sweetening wordnetwith dolce. AI Mag., 24(3):13–24, September.Aldo Gangemi, Roberto Navigli, and Paola Velardi.2003b. The ontowordnet project: extension and ax-iomatization of conceptual relations in wordnet. Inin WordNet, Meersman, pages 3–7. Springer.Thomas R. Gruber. 1993. A translation approach toportable ontology specifications. Knowl. Acquis.,5(2):199–220, June.Hongyan Jing. 1998. Usage of wordnet in nat-ural language generation. In Proceedings of theJoint 17th International Conference on Computa-tional Linguistics 36th Annual Meeting of the As-sociation for Computational Linguistics (COLING-ACL’98) workshop on Usage of WordNet in NaturalLanguage Processing Systems, pages 128–134.Feiyu Lin and Kurt Sandkuhl. 2008. A survey ofexploiting wordnet in ontology matching. In MaxBramer, editor, Artificial Intelligence in Theory andPractice II, volume 276 of IFIP The InternationalFederation for Information Processing, pages 341–350. Springer US.Ling Liu and M. Tamer Özsu, editors. 2009. Encyclo-pedia of Database Systems. Springer US.G. Miller. 1995. WordNet: A lexical database for En-glish. Communications of the ACM, 38(11):39–41.

WordNet as an Ontology for Generation

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WordNet as an Ontology for Generation

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