This work introduces a lexical search model based on a type of knowledge graphs, namely word association norms. The aim of the search is to retrieve a target word, given the description of a concept, i.e., the query. This differs from traditional information retrieval models were complete documents related to the query are retrieved. Our algorithm looks for the keywords of the definition in a graph, built over a corpus of word association norms for Mexican Spanish, and computes the centrality in order to find the relevant concept. We performed experiments over a corpus of human-definitions in order to evaluate our model. The results are compared with a Boolean information retrieval (IR) model, the BM25 text-retrieval algorithm, an algorithm based on word vectors and an online onomasiological dictionary–OneLook Reverse Dictionary. The experiments show that our lexical search method outperforms the IR models in our study case.
AlgarabelS., RuízJ.C.and
SanmartínJaime, The University of Valencia’s computerized Word pool. Behavior Research Methods, Instruments & Computers, 1998.
Arias-TrejoN., Barrón-MartínezJ.B., López AldereteR.H. and
Robles AguirreF.A., Corpus de normas de asociación de palabras para el español de México [NAP]. UNAM, Universidad Nacional Autónoma de México, 2015.
4.
KurtB., Teoría semántica: Hacia una semántica moderna, volume 12. Alcalá, 1970.
5.
BalogK., BronM. and de RijkeM., Categorybased query modeling for entity search, In Proceedings of the 32nd European Conference on Information Retrieval (ECIR), Springer, 2010, pp. 319–331.
6.
BalogK., BronM. and de RijkeM., Query modeling for entity search based on terms, categories, and examples, ACM Transactions on Information Systems29(4, Article 22) (2011).
7.
BavelasA., A mathematical model for group structure, Social Networks: Critical Concepts in Sociology, New York: Routledge1 (2002), 161–188.
8.
Bel-EnguixG., RappR. and ZockM., A graph-based approach for computing free word associations, In Proceedings of the 9th edition of the Language Resources and Evaluation Conference, 2014, pp. 221–230.
9.
BilacS., WatanabeW., HashimotoT., TokunagaT. and TanakaH., Dictionary search based on the target word description, In Proceedings of the Tenth Anual Meeting of the Assocviation for Natural Language Processing, 2004, pp. 556–559.
10.
BirdS. and LoperE., Nltk: The natural language toolkit, In Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions, Association for Computational Linguistics, 2004, p. 31.
11.
BojanowskiP., GraveE., JoulinA. and MikolovT., Enriching word vectors with subword information, Computing Research Repository (2016), arXiv:1607.04606.
12.
BoninP., Mental lexicon: Some words to talk about words, Nova Science Publishers, 2004.
13.
Borge-HolthoeferJ. and ArenasA., Navigating word association norms to extract semantic information, In Proceedings of the 31st Annual Conference of the Cognitive Science Society, 2009.
14.
BrandesU., On variants of shortest-path betweenness centrality and their generic computation, Social Networks30(2) (2008), 136–145.
15.
CleverdonC., Optimizing convenient online access to bibliographic databases, Information services and Use4 (1984), 37–47.
16.
DutoitM. and NuguesP., A lexical database and an algorithm to find words from definitions, In Proceedings of the 15th European Conference on Artificial Intelligence, 2002, pp. 450–454.
17.
El-KahloutI.D. and OflazerK., Use of wordnet for retrieving words from their meanings, In 2nd Global WordNet Conference, 2004.
18.
FernándezÁ., DíezE., AlonsoM.Á. and BeatoM.S., Free-association norms form the spanish names of the snodgrass and vanderwart pictures, Behavior Research Methods, Instruments & Computers36 (2004), 577–583.
19.
Ferrer i CanchoR. and
SoléR.V., The small-world of human language, Proceedings of the Royal Society of London Series B, Biological Sciences268(1482) (2001), 2261–2265.
20.
FerretO., Using collocations for topic segmentation and link detection, In COLING 2002, 2002, pp. 260–266.
21.
OlivierF., Building a network of topical relations from a corpus, In LREC2006 (2006).
22.
FreemanL.C., A set of measures of centrality based on betweenness, Sociometry (1997), 35–41.
23.
GarimellaA., BaneaC. and MihalceaR., Demographic-aware word associations, In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017, pp. 2285–2295.
24.
GhoshU., JainS. and SomaP., A two-stage approach for computing associative responses to a set of stimulus words, In Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon (CogALex-IV) COLING 2014 25th International Conference on Computational Linguistics, 2014, pp. 15–21.
25.
HagbergA., SchultD. and SwartP., Networkx: Python software for the analysis of networks. Mathematical Modeling and Analysis, Los Alamos National Laboratory, 2005.
26.
HernándezL., Creación semi-automática de la base de datos y mejora del motor de búsqueda de un diccionario onomasiológico. Universidad Nacional Autónoma de México, 2012.
27.
JaremaG., LibbenG. and KehayiaE., The mental lexicon, Brain and Language81 (2002).
28.
KissG.R., ArmstrongCh., MilroyR. and PiperJ., An associative thesaurus of English and its computer analysis, Edinburgh University Press, Edinburgh, 1973.
29.
LafourcadeM. and JoubertA., TOTAKI: A Help for Lexical Access on the TOT Problem. Text, Speech and Language Technology XI, 2015.
30.
LafourcadeM., Making people play for lexical acquisition, In Proceedings of the th SNLP 2007, Pattaya, Thailand, 7, 2007, pp. 13–15.
31.
LeveltW.J.M., Spoken word production: A theory of lexical access, PNAS98(23) (2005), 13464–13471.
32.
MacizoP., Gómez-ArizaC.J. and Teresa BajoM., , Associative norms of 58 spanish for children from 8 to 13 years old, Psicológica21 (2000), 287–300.
33.
ManningC.D., RaghavanP. and SchutzeH., Introduction to Information Retrieval, Cambridge University Press, 2009.
34.
MikolovT., GraveE., BojanowskiP., PuhrschC. and JoulinA., Advances in pre-training distributed word representations, In Proceedings of the EleventhInternational Conference on Language Resources and Evaluation, LREC’18, 2018.
35.
MillerG.A., et al., Introduction to wordnet: An on-line lexical database, International Journal of Lexicography3(4) (1990), 235–244.
36.
NelsonD.L., McEvoyC.L. and SchreiberT.A., Word association rhyme and word fragment norms. The University of South Florida, 1998.
37.
NelsonD.L., McEvoyC.L. and SchreiberT.A., Words in the mind: An introduction to the mental lexicon, Blackwell,2003.
38.
PadróL. and StanilovskyE., Freeling 3.0: Towards wider multilinguality, In Proceedings of the Language Resources and Evaluation Conference (LREC 2012), Istanbul, Turkey, 2012, ELRA.
39.
RAE, Diccionario de la Lengua Española. Real Academia Española de la Lengua, 2013.
40.
RobertsonS. and ZaragozaH., The probabilistic relevance framework: Bm25 and beyond, Foundations and Trends in Information Retrieval3(4) (2009), 333–389.
41.
RobertsonS.E. and Sparck JonesK., Relevance weighting of search terms, Journal of the American Society for Information Science27(3) (1976).
42.
RogetR., Roget’s Thesaurus of English Words and Phrases (TY Crowell co, 1911.
43.
SanfeliuC. and FernándezÁ., A set of 254 Snodgrass’ Vanderwart pictures standardized for Spanish: Norms for name agreement, image agreement, familiarity, and visual complexity, Behavior Research Methods, Instruments, & Computers28 (1996), 537–555.
44.
SarwarS.M., FoleyJ. and AllanJ., Term relevance feedback for contextual named entity retrieval, arXiv:1801.02687v1 [cs. IR] (2018).
45.
SierraG., Design of an onomasiological search sistem: A concept-oriented tool for terminology, Terminology6(1) (2000), 1–34.
46.
SierraG., The onomasiological dictionary: A gap in lexicography, In Proceedings of the Ninth Euralex International Congress, 2000, pp. 223–235.
47.
SierraG. and McNaughtJ., Natural language system for terminological information retrieval, In
GelbukhAlexander, editor, Computational Linguistics and Intelligent Text Processing, Berlin, Heidelberg, Springer, 2003, pp. 541–552.
48.
SinopalnikovaA. and SmrzP., Word association thesaurus as a resource for extending semantic networks, In Communications in Computing, 2004, pp. 267–273.
49.
WiddowsD. and DorowB., A graph model for unsupervised lexical acquisition, In 19th International Conference on Computational Linguistics, 2002.
50.
ZockM., FerretO. and SchwabD., Deliberate word access: An intuition, a roadmap and some preliminary empirical results, International Journal of Speech Technology13(4) (2010), 201–218.
51.
ZockM., SchwabD. and RakotonanaharyN., Lexical access, a search-problem, In Proceedings of the 2nd Workshop on Cognitive Aspects of the Lexicon (CogALex 2010), 2010, pp. 75–84.
