Language identification in texts

This work investigates the task of identifying the language of digitally encoded text. Automatic methods for language identification have been developed since the 1960s. During the years, the significance of language identification as an important preprocessing element has grown at the same time as other natural language processing systems have become mainstream in day-to-day applications. The methods used for language identification are mostly shared with other text classification tasks as almost any modern machine learning method can be trained to distinguish between different languages. We begin the work by taking a detailed look at the research so far conducted in the field. As part of this work, we provide the largest survey on language identification available so far. Comparing the performance of different language identification methods presented in the literature has been difficult in the past. Before the introduction of a series of language identification shared tasks at the VarDial workshops, there were no widely accepted standard datasets which could be used to compare different methods. The shared tasks mostly concentrated on the issue of distinguishing between similar languages, but other open issues relating to language identification were addressed as well. In this work, we present the methods for language identification we have developed while participating in the shared tasks from 2015 to 2017. Most of the research for this work was accomplished within the Finno-Ugric Languages and the Internet project. In the project, our goal was to find and collect texts written in rare Uralic languages on the Internet. In addition to the open issues addressed at the shared tasks, we dealt with issues concerning domain compatibility and the number of languages. We created an evaluation set-up for addressing short out-of-domain texts in a large number of languages. Using the set-up, we evaluated our own method as well as other promising methods from the literature. The last issue we address in this work is the handling of multilingual documents. We developed a method for language set identification and used a previously published dataset to evaluate its performance.Tässä väitöskirjassa tutkitaan digitaalisessa muodossa olevan tekstin kielen automaattista tunnistamista. Tekstin kielen tunnistamisen automaattisia menetelmiä on kehitetty jo 1960-luvulta lähtien. Kuluneiden vuosikymmenien aikana kielentunnistamisen merkitys osana laajempia tietojärjestelmiä on vähitellen kasvanut. Tekstin kieli on tarpeellista tunnistaa, jotta tekstin jatkokäsittelyssä osataan käyttää sopivia kieliteknologisia menetelmiä. Tekstin kielentunnistus on kieleltään tai kieliltään tuntemattoman tekstin kielen tai kielien määrittämistä. Suurimmaksi osaksi kielentunnistukseen käytettyjä menetelmiä käytetään tai voidaan käyttää tekstin luokitteluun myös tekstin muiden ominaisuuksien, kuten aihealueen, perusteella. Tähän artikkeliväitöskirjaan kuuluvassa katsausartikkelissa esittelemme laajasti kielentunnistuksen tähänastista tutkimusta ja käymme kattavasti lävitse kielentunnistukseen tähän mennessä käytetyt menetelmät. Seuraavat kolme väistöskirjan artikkelia esittelevät ne kielentunnistuksen menetelmät joita käytimme VarDial työpajojen yhteydessä järjestetyissä kansainvälisissä kielentunnistuskilpailuissa vuodesta 2015 vuoteen 2017. Suurin osa tämän väitöskirjan tutkimuksesta on tehty osana Koneen säätiön rahoittamaa suomalais-ugrilaiset kielet ja internet -hanketta. Hankkeen päämääränä oli löytää internetistä tekstejä, jotka olivat kirjoitettu harvinaisemmilla uralilaisilla kielillä ja väitöskirjan viides artikkeli keskittyy projektin alkuvaiheiden kuvaamiseen. Väitöskirjan kuudes artikkeli kertoo miten hankkeen verkkoharavaan liitetty kielentunnistin evaluoitiin vaativasssa testiympäristössä, joka sisälsi tekstejä kirjoitettuna 285 eri kielellä. Seitsemäs ja viimeinen artikkeli käsittelee monikielisten tekstien kielivalikoiman selvittämistä

Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

Peer reviewe

A survey on author profiling, deception, and irony detection for the Arabic language

"This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] The possibility of knowing people traits on the basis of what they write is a field of growing interest named author profiling. To infer a user's gender, age, native language, language variety, or even when the user lies, simply by analyzing her texts, opens a wide range of possibilities from the point of view of security. In this paper, we review the state of the art about some of the main author profiling problems, as well as deception and irony detection, especially focusing on the Arabic language.Qatar National Research Fund, Grant/Award Number: NPRP 9-175-1-033Rosso, P.; Rangel-Pardo, FM.; Hernandez-Farias, DI.; Cagnina, L.; Zaghouani, W.; Charfi, A. (2018). 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