Natural Language Processing Resources for Finnish. Corpus Development in the General and Clinical Domains

Siirretty Doriast

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022)

The prevailing practice in the academia is to evaluate the model performance on in-domain evaluation data typically set aside from the training corpus. However, in many real world applications the data on which the model is applied may very substantially differ from the characteristics of the training data. In this paper, we focus on Finnish out-of-domain parsing by introducing a novel UD Finnish-OOD out-of-domain treebank including five very distinct data sources (web documents, clinical, online discussions, tweets, and poetry), and a total of 19,382 syntactic words in 2,122 sentences released under the Universal Dependencies framework. Together with the new treebank, we present extensive out-of-domain parsing evaluation utilizing the available section-level information from three different Finnish UD treebanks (TDT, PUD, OOD). Compared to the previously existing treebanks, the new Finnish-OOD is shown include sections more challenging for the general parser, creating an interesting evaluation setting and yielding valuable information for those applying the parser outside of its training domain.</p

Yleiskäyttöinen tekstinluokittelija suomenkielisille potilaskertomusteksteille

Medical texts are an underused source of data in clinical analytics. Extracting the relevant information from unstructured texts is difficult and while there are some tools available, they are often targeted for English texts. The situation is worse for smaller languages, such as Finnish. In this work, we reviewed literature in text mining and natural language processing fields in the scope of analyzing medical texts. Using the results of our literature review, we created an algorithm for information extraction from patient record texts. During this thesis work we created a decent text mining tool that works through text classification. This algorithm can be used detect medical conditions solely from medical texts. The usage of the algorithm is limited through the availability of large training data.Potilaskertomustekstejä käytetään kliinisessä analytiikassa huomattavan vähäisessä määrin. Olennaisen tiedon poimiminen tekstin joukosta on vaikeaa, ja vaikka siihen on työkaluja saatavilla, ovat ne useimmiten tehty englanninkielisille teksteille. Pienempien kielten, kuten suomen kohdalla tilanne on heikompi. Tässä työssä tehtiin kirjallisuuskatsaus tekstinlouhintaan ja luonnollisen kielen käsittelyyn liittyvään kirjallisuuteen, keskittyen erityisesti menetelmiin jotka soveltuvat lääketieteellisten tekstien analysointiin. Kirjallisuuskatsauksen pohjalta loimme algoritmin, joka soveltuu yleisesti lääketieteellisten tekstien luokitteluun. Tämän diplomityön osana luotiin tekstinlouhintatyökalu suomenkielisille lääketieteellisille teksteille. Kehitettyä algoritmia voidaan käyttää erilaisten tilojen tunnistamiseen potilaskertomusteksteistä. Algoritmin käyttöä kuitenkin rajoittaa tarve suurehkolle määrälle opetusdataa

Revealing effects of psychosocial factors of cancer patients

Abstract. This research shows different methodologies applied on different platforms in order to extract both social and psychosocial factors that might be related to caner by applying natural language processing tools on text from different platforms as social media or other online forums. We also present challenges associated with every platform and the corresponding tools used on it. From text mining to text analysis and then data visualisation, this research compares different analysis methods and outputs. We discuss many tools either tested, used or modified in order to achieve such analysis. Meanwhile, we were able to get interesting findings for the medical fields to explore and research more. We developed a modular system that can help clinicians and medical experts use to analyse similar forums.Syöpäpotilaiden psykososiaalisten tekijöiden vaikutusten paljastaminen. Tiivistelmä. Tämä tutkimus esittelee erilaisia menetelmiä sovellettuina eri alustoilla, tavoitteena hahmottaa sekä sosiaalisia että psykokososiaalisia tekijöitä, jotka voivat liittyä syöpään sovellettaessa luonnollisia kielenkäsittelyvälineitä eri alustojen tekstille sosiaalisen median tai muiden online-foorumeiden muodossa. Esitämme myös haasteita, jotka liittyvät jokaiseen alustaan ja siihen liittyviin työkaluihin. Teksti-mining, tekstianalyysiin ja sitten datan visualisointiin tässä tutkimuksessa verrataan erilaisia analyysimenetelmiä ja -tuloksia. Keskustelemme monista työkaluista, jotka on testattu, käytetty tai muunnettu tällaisen analyysin saavuttamiseksi. Samaan aikaan saimme mielenkiintoisia tuloksia lääketieteen aloille tutkia ja tutkia lisää. Kehitimme modulaarisen järjestelmän, jonka avulla lääkärit ja lääketieteen asiantuntijat voivat analysoida samanlaisia foorumeita

Clinical Natural Language Processing in languages other than English: opportunities and challenges

Background: Natural language processing applied to clinical text or aimed at a clinical outcome has been thriving in recent years. This paper offers the first broad overview of clinical Natural Language Processing (NLP) for languages other than English. Recent studies are summarized to offer insights and outline opportunities in this area. Main Body We envision three groups of intended readers: (1) NLP researchers leveraging experience gained in other languages, (2) NLP researchers faced with establishing clinical text processing in a language other than English, and (3) clinical informatics researchers and practitioners looking for resources in their languages in order to apply NLP techniques and tools to clinical practice and/or investigation. We review work in clinical NLP in languages other than English. We classify these studies into three groups: (i) studies describing the development of new NLP systems or components de novo, (ii) studies describing the adaptation of NLP architectures developed for English to another language, and (iii) studies focusing on a particular clinical application. Conclusion: We show the advantages and drawbacks of each method, and highlight the appropriate application context. Finally, we identify major challenges and opportunities that will affect the impact of NLP on clinical practice and public health studies in a context that encompasses English as well as other languages

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

Knowledge representation and text mining in biomedical, healthcare, and political domains

Knowledge representation and text mining can be employed to discover new knowledge and develop services by using the massive amounts of text gathered by modern information systems. The applied methods should take into account the domain-specific nature of knowledge. This thesis explores knowledge representation and text mining in three application domains. Biomolecular events can be described very precisely and concisely with appropriate representation schemes. Protein–protein interactions are commonly modelled in biological databases as binary relationships, whereas the complex relationships used in text mining are rich in information. The experimental results of this thesis show that complex relationships can be reduced to binary relationships and that it is possible to reconstruct complex relationships from mixtures of linguistically similar relationships. This encourages the extraction of complex relationships from the scientific literature even if binary relationships are required by the application at hand. The experimental results on cross-validation schemes for pair-input data help to understand how existing knowledge regarding dependent instances (such those concerning protein–protein pairs) can be leveraged to improve the generalisation performance estimates of learned models. Healthcare documents and news articles contain knowledge that is more difficult to model than biomolecular events and tend to have larger vocabularies than biomedical scientific articles. This thesis describes an ontology that models patient education documents and their content in order to improve the availability and quality of such documents. The experimental results of this thesis also show that the Recall-Oriented Understudy for Gisting Evaluation measures are a viable option for the automatic evaluation of textual patient record summarisation methods and that the area under the receiver operating characteristic curve can be used in a large-scale sentiment analysis. The sentiment analysis of Reuters news corpora suggests that the Western mainstream media portrays China negatively in politics-related articles but not in general, which provides new evidence to consider in the debate over the image of China in the Western media