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Query Selection for Improved Greek Web Searches

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Query Selection for Improved Greek Web Searches Query Selection for Improved Greek Web Searches Sofia Stamou Lefteris Kozanidis Paraskevi Tzekou Nikos Zotos Computer Engineering Department, Patras University 26500 Greece {stamou, kozanid, tzekou, ztoson} @ceid.upatras.gr ABSTRACT is to effectively process the multilingual Web content in order to As the Web becomes an integral part of our everyday life and the be able to serve multilingual queries and the second is to be able Internet-literate population grows rapidly, the Search Engine to suggest users with alternative queries in case their issued key- market is steadily gaining a high monetary value. Unfortunately, words fail to retrieve the desired information. today, the distribution of the search market share is dominated by For the first challenge, a search engine needs not only accurately English-speaking users and stakeholders, basically because Eng- detect the query language and look for information in a multilin- lish is the lingua franca of the Web. Thus, although the majority gual index, but it also needs to account for the language properties of the Web users are non-English native speakers, they naturally and usage. To tackle such problems there have been previous gravitate to using English in order to explore the plentiful Web studies that investigate Web searching through non-English que- content. In this paper, we propose a query selection mechanism ries (cf. to [3] for an overview). The striking majority of these for assisting users perform successful non-English Web searches. studies concentrate on either searching the Web in a particular Our mechanism combines linguistic analysis and Web mining language (other than English) [26] [5] [14] [15] [17], or on cross- techniques and aims at assisting users select informative and well- lingual Web information retrieval [20] [10] [6]. The most popular specified queries for expressing their information needs in lan- approach towards cross-lingual Web information retrieval is to guages other than English. Our technique is validated on a dataset explore bilingual corpora or dictionaries in order to translate the of 70 Greek queries issued to Google search engine over a period query and/or the documents from one language to the other, over- of 3 weeks. Obtained results demonstrate that our query selection coming thus language barriers. Despite the usefulness of Machine mechanism yields improved retrieval performance compared to Translation techniques in a multilingual Web search setting, these existing non-English search strategies and as such we believe that are intrinsically insufficient in a practical deployment because of it can be fruitfully deployed for other natural languages. their implementation and maintenance high cost, their limited availability and their complete dependence on the resources’ ac- Categories and Subject Descriptors curacy and lexical completeness. With respect to the second chal- H.3.3 [Information Search and Retrieval]: Query formulation, lenge, i.e. how to help users specify informative queries, re- Search process, Retrieval models, Selection process; H.3.4 [Sys- searchers have studied the improvement of the user issued queries tems and Software]: Performance evaluation (efficiency and with semantically similar terms [4] [9]. Query refinement is the effectiveness). process of providing users with alternative query formulations in the hope of retrieving relevant information. Besides query refine- General Terms ment, researchers have also studied ways of personalizing search Performance, Design, Experimentation. results according to the user interests [32] One aspect that none of the reported studies addresses is how to Keywords assist non-English Web searchers select informative queries that Greek web search, query selection, linguistic analysis, text min- are both expressive of their search interests and semantically re- ing, semantics. lated to their typed keywords. In this paper, we investigate the problem of assisting Greek Web users select alternative query wordings for expressing their search intentions. In particular, we 1. INTRODUCTION present a personalized query selection mechanism that employs The most popular way for finding information on the Web is go to linguistic analysis and Web mining techniques in order to identify a search engine, issue a keyword query that describes an informa- good query alternatives in Greek Web searches. tion need and receive a list of results that somehow relate to the information sought. Despite the recent advances in Web search Our model relies on the intuition that a user’s search behavior and there are two main challenges that a search engine needs to deal querying patterns are common throughout her different Web with in order to satisfy all user search requests. The first challenge searches, irrespectively of the language employed. Therefore, we suggest the uniform mining of the user’s previously collected search trace in order to derive the user interests and the user pre- Permission to make digital or hard copies of all or part of this work for ferred queries for expressing those interests. Based on the identi- personal or classroom use is granted without fee provided that copies are fied user interests and interest-expressive queries, we employ not made or distributed for profit or commercial advantage and that linguistic analysis in order to firstly decipher the intention of the copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, re- user’s current query and thereafter improve it with alternative quires prior specific permission and/or a fee. keywords that match both the query semantics and the user inter- iNEWS’08, October 30, 2008, Napa Valley California, USA. ests. We applied our query selection mechanism to a number of Copyright 2008 ACM 978-1-60558-253-5 /08/10…$5.00. experimental Greek language queries that have been issued to Google search engine and we evaluated the accuracy of the search characteristic of the Greek language is its rich inflectional mor- results. Obtained results demonstrate that our query selection phology, which is attested in the numerous wordforms1 that might mechanism yields improved retrieval performance compared to correspond to a single lemma. When it comes to orthography, existing search strategies and as such we believe that it can be Greek writing is much complicated not only due to the different fruitfully deployed for other natural languages. ways of spelling the same sounds, but also due to the wide use of diphthongs and digraphs, including various pairs of vowel letters. The rest of the paper is organized as follows. We begin our dis- cussion with a brief introduction to the particularities associated Another issue that adds to the complexity of understanding and with Greek Web searches. In section 3, we present our proposed using Greek concerns the transliteration of Greek to Latin letters; query selection mechanism and we describe the way in which we a practice so common in the digital era that has given rise to the 2 mine the user’s search logs in order to derive the user interests formation of a hybrid language, known as Greeklish . Greeklish and preferred search keywords (section 3.1). We then discuss how emerged as a convenient mean for e-writing Greek in operating we explore the identified interests for deciphering the user’s cur- systems and applications with no support of the Greek character rent query and based on this knowledge we show how our model set. Today, modern software supports Greek but still it is much picks alternative query wordings for refining search (section 3.2). easier for Greek computer literates to e-write in Greeklish because In section 4, we present our experimental evaluation and we dis- it is faster to type and they do not have to worry for orthography. cuss obtained results. We conclude the paper in section 5. Based on the above, it becomes evident that the computational processing of Greek is a complex task that requires extensive 2. SEARCHING THE GREEK WEB linguistic knowledge. When it comes to the Web search paradigm, This section provides a brief overview on the Greek Web, the handling and understanding Greek is a challenging task that has characteristics of the Greek language and how search engines attracted the interest of many researchers, as we present next. handle Greek language queries. 2.3 How Search Engines Respond to Greek 2.1 The Greek Web Language Queries? The prime difficulty associated with searching the Greek Web is Stimulated by the characteristics of the Greek language and the to accurately define the latter, i.e. to detect the boundaries of its Web’s evolution in non-English domains, many researchers [17] graph so as to be able to download its content and subsequently [7] [28] [18] have studied the problems associated with searching head Greek language searches against it. The straightforward the Web via Greek queries and they have come up with some claim that the Greek Web is composed of sites registered under interesting observations. Lazarinis [17] studied how different the .gr domain is misleading, since many Greek sites are hosted search engines respond to Greek queries and found that there is under the .net, .com, or .org top-level domains [16]. In addition, great variation in the handling of Greek between global and local many sites in the .gr domain contain English content that is search engines. In particular, he found that global search engines, Greek-oriented. Under stricter criteria, the Greek Web is defined apart from Google, are case sensitive, they do not apply stopword as the collection of Web pages written in Greek. Despite some removal and stemming for Greek and as such they hinder the recent attempts to capture the entire picture of the Greek Web, the retrieval of pages that contain the query terms in a slightly differ- latter remains incomplete as we would need to equip web crawlers ent form compared to the user-typed keywords.
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