The Role of a Computational Lexicon for Query Expansion in Full-Text Search
Authors: Emiliano Giovannetti, Davide Albanesi, Andrea Bellandi, Simone Marchi, Mafalda Papini, Flavia Sciolette (CNR-ILC, 2021)
Topic: Lexical and Semantic Resources and Analysis

Abstract
This work describes the first experiments conducted with a computational lexicon of Italian in a context of query expansion for full-text search. An application, composed of a graphical user interface and backend services to access the lexicon and the database containing the corpus to be queried, was developed. The text was morphologically analysed to improve the precision of the search process. Some examples of queries are given to show the potential of a text search approach supported by a complex and stratified lexical resource.

Introduction
This work describes the first experiments conducted with a computational lexicon of Italian in a context of query expansion for full-text search. An application, composed of a graphical user interface and backend services to access the lexicon and the database containing the corpus to be queried, was developed. The text was morphologically analysed to improve the precision of the search process. Some examples of queries are given to show the potential of a text search approach supported by a complex and stratified lexical resource. This work describes the first experiments conducted with a computational lexicon of Italian in a context of query expansion for full-text search. An application, composed of a graphical user interface and backend services to access the lexicon and the database containing the corpus to be queried, was developed. The text was morphologically analysed to improve the precision of the search process. Some examples of queries are given to show the potential of a text search approach supported by a complex and stratified lexical resource. 60s on the development of the very first question answering (QA) systems already included linguistic resources as support datasets. To bring some “old school” examples, the “General Inquirer” QA system (Stone et al., 1962) used a thesaurus for “coding words as to concept membership” while Simmon’s “Protosynthex” was equipped with a synonym dictionary (Simmons et al, 1963) to “expand the meaning of the question's words to any desired level”. One of the first works specifically focussed on the use of a lexical resource for NLP tasks was about COMPLEX (for “COMPutational LEXicon”), a resource developed at IBM (Klavans, 1988). The support of linguistic resources has proved its potential in the field of information retrieval (IR) too, as highlighted in many of Bill Woods’ works, culminating in the introduction of his conceptual indexing technique and the conceptual taxonomy resource (Woods, 1997) and later refined in an article entitled “Linguistic Knowledge can Improve Information Retrieval” (Woods, et al, 2000). More recently, other researchers have stressed the importance of the availability of a “Lexical Knowledge Base” (another way to refer to a computational lexicon) in tasks such as Word Sense Disambiguation, since their use, in some contexts, can outperform supervised systems (Agirre et al., 2009). The use of linguistic resources in QA of the earliest period of computational linguistics can be considered as the precursor of “query expansion” (QE), the technique that Manning and Raghavanat describe as the most used “local method” in IR to tackle those situations in which “the same concept may be referred to using different words” (Manning et al., 2008). Though QE may be obtained in different ways (among which query reformulations based on query log mining) we are here interested in those applications that make use of lexical resources. Most of the works, published from the 90s to nowadays (proving that QE is still being investigated), exploit WordNet (Fellbaum, 1998), the de facto and most widespread ontological (or lexical, depending from the point of view) multilingual resource. Ellen Vorhees was one of the first and used WordNet’s IS_A relations to improve text retrieval (Vorhees, 1993). Moving on directly to the most recent works, WordNet has been used with all its ontological features to expand queries in a semantic text search context in (Ngo et al., 2018) while in (Azad and Deepak, 2019) the authors combined WordNet and Wikipedia for QE, exploiting the first to expand individual terms and the second to expand phrase terms. The research work here illustrated places itself in the context of full-text search carried out using a lexical resource-driven QE technique. However, the focus of this research, differently from that of the cited works, is not on the specific QE technique and the relative evaluation, but on the resource we chose to exploit, introduced in the next section, in place of WordNet and on the frontend and backend technologies implemented to query the text, as described in details in Section 3. The advantages derived from the adoption of a rich and highly structured computational lexicon will also be remarked through some query examples shown in Section 4. The developed application can be freely accessed and used to query the corpus1.

Conclusion
As shown in this paper, the availability of a rich and structured linguistic resource (as the computational lexicon we have taken into account) seems to provide an edge over the standard query expansion techniques for full- text search based on WordNet. Now that a very first portion of the resource has been made available (though with a preliminary conversion) and the web application has been implemented, the road is cleared for the next steps.
The first critical issue that will need to be faced involves the limitedness of the resource, covering most - but not all - the lemmas, forms, and senses of standard contemporary Italian and that lacks many domain-related terms or senses. To fill this gap the resource will have to be updated and enriched with more entries. At the same time, as anticipated, a more in- depth and rigorous conversion of PSC will have to be carried out, a process that will probably take a lot of time and research effort and that for the sake of this first experiment would have been premature and unnecessary. As soon as the whole conversion will be ready, the rest of the information encoded in the lexicon will be made available and integrated in the search process. Though the benefits of the availability of a computational lexicon wrt WordNet (or a similar resource) may seem obvious in a context of QE for full-text search, an empirical evaluation would be desirable. However, the set up of a benchmark conceived for this purpose appears anything but easy, mainly due to the lack of comparable works or evaluation campaigns focussing on the role of linguistic resources as support. In conclusion, we believe these first experiments carried out by querying the talmudic text appear promising, especially considering that only a small part of the lexicon has been used. In addition, the support in the disambiguation provided by the POS tagging of the text suggests that an hybridization of a resource-driven QE technique with a deeper stochastic annotation of the corpus to be queried may constitute an interesting experimental field to be investigated.