Eesti keele ühendverbide automaattuvastus lingvistiliste ja statistiliste meetoditega

Tänapäeval on inimkeeli (kaasa arvatud eesti keelt) töötlevad tehnoloogiaseadmed igapäevaelu osa, kuid arvutite „keeleoskus“ pole kaugeltki täiuslik. Keele automaattöötluse kõige rohkem kasutust leidev rakendus on ilmselt masintõlge. Ikka ja jälle jagatakse sotsiaalmeedias, kuidas tuntud süsteemid (näiteks Google Translate) midagi valesti tõlgivad. Enamasti tekitavad absurdse olukorra mitmest sõnast koosnevad fraasid või laused. Näiteks ei suuda tõlkesüsteemid tabada lauses „Ta läks lepinguga alt“ ühendi alt minema tähendust petta saama, sest õige tähenduse edastamiseks ei saa selle ühendi komponente sõna-sõnalt tõlkida ja seetõttu satubki arvuti hätta. Selleks et nii masintõlkesüsteemide kui ka teiste kasulike rakenduste nagu libauudiste tuvastuse või küsimus-vastus süsteemide kvaliteet paraneks, on oluline, et arvuti oskaks tuvastada mitmesõnalisi üksuseid ja nende eri tähendusi, mida inimesed konteksti põhjal üpriski lihtalt teha suudavad. Püsiühendite (tähenduse) automaattuvastus on oluline kõikides keeltes ja on seetõttu pälvinud arvutilingvistikas rohkelt tähelepanu. Seega on eriti inglise keele põhjal välja pakutud terve hulk meetodeid, mida pole siiamaani eesti keele püsiühendite tuvastamiseks rakendatud. Doktoritöös kasutataksegi masinõppe meetodeid, mis on teiste keelte püsiühendite tuvastamisel edukad olnud, üht liiki eesti keele püsiühendi – ühendverbi – automaatseks tuvastamiseks. Töös demonstreeritakse suurte tekstiandmete põhjal, et seni eesti keele traditsioonilises käsitluses esitatud eesti keele ühendverbide jaotus ainukordseteks (ühendi komponentide koosesinemisel tekib uus tähendus) ja korrapärasteks (ühendi tähendus on tema komponentide summa) ei ole piisavalt põhjalik. Nimelt kinnitab töö arvutilingvistilistes uurimustes laialt levinud arusaama, et püsiühendid (k.a ühendverbid) jaotuvad skaalale, mille ühes otsas on ühendid, mille tähendus on selgelt komponentide tähenduste summa. ja teises need ühendid, mis saavad uue tähenduse. Uurimus näitab, et lisaks kontekstile aitavad arvutil tuvastada ühendverbi õiget tähendust mitmed teised tunnuseid, näiteks subjekti ja objekti elusus ja käänded. Doktoritöö raames valminud andmestikud ja vektoresitused on vajalikud uued ressursid, mis on avalikud edaspidisteks uurimusteks.Nowadays, applications that process human languages (including Estonian) are part of everyday life. However, computers are not yet able to understand every nuance of language. Machine translation is probably the most well-known application of natural language processing. Occasionally, the worst failures of machine translation systems (e.g. Google Translate) are shared on social media. Most of such cases happen when sequences longer than words are translated. For example, translation systems are not able to catch the correct meaning of the particle verb alt (‘from under’) minema (‘to go’) (‘to get deceived’) in the sentence Ta läks lepinguga alt because the literal translation of the components of the expression is not correct. In order to improve the quality of machine translation systems and other useful applications, e.g. spam detection or question answering systems, such (idiomatic) multi-word expressions and their meanings must be well detected. The detection of multi-word expressions and their meaning is important in all languages and therefore much research has been done in the field, especially in English. However, the suggested methods have not been applied to the detection of Estonian multi-word expressions before. The dissertation fills that gap and applies well-known machine learning methods to detect one type of Estonian multi-word expressions – the particle verbs. Based on large textual data, the thesis demonstrates that the traditional binary division of Estonian particle verbs to non-compositional (ainukordne, meaning is not predictable from the meaning of its components) and compositional (korrapärane, meaning is predictable from the meaning of its components) is not comprehensive enough. The research confirms the widely adopted view in computational linguistics that the multi-word expressions form a continuum between the compositional and non-compositional units. Moreover, it is shown that in addition to context, there are some linguistic features, e.g. the animacy and cases of subject and object that help computers to predict whether the meaning of a particle verb in a sentence is compositional or non-compositional. In addition, the research introduces novel resources for Estonian language – trained embeddings and created compositionality datasets are available for the future research.https://www.ester.ee/record=b5252157~S

Sõnadevahelise seose tugevuse mõõtmise statistilised meetodid ühendverbide tuvastamisel

http://www.ester.ee/record=b4435326~S1*es

Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction

Training recurrent neural networks on long texts, in particular scholarly documents, causes problems for learning. While hierarchical attention networks (HANs) are effective in solving these problems, they still lose important information about the structure of the text. To tackle these problems, we propose the use of HANs combined with structure-tags which mark the role of sentences in the document. Adding tags to sentences, marking them as corresponding to title, abstract or main body text, yields improvements over the state-of-the-art for scholarly document quality prediction. The proposed system is applied to the task of accept/reject prediction on the PeerRead dataset and compared against a recent BiLSTM-based model and joint textual+visual model as well as against plain HANs. Compared to plain HANs, accuracy increases on all three domains. On the computation and language domain our new model works best overall, and increases accuracy 4.7% over the best literature result. We also obtain improvements when introducing the tags for prediction of the number of citations for 88k scientific publications that we compiled from the Allen AI S2ORC dataset. For our HAN-system with structure-tags we reach 28.5% explained variance, an improvement of 1.8% over our reimplementation of the BiLSTM-based model as well as 1.0% improvement over plain HANs.Comment: This new version of the paper brings the paper up-to-date with the improved paper, published at the First Workshop on Scholarly Document Processing, at EMNLP 2020. .Additionally, minor corrections were made including addition of color to Figures 1,2. The changes in comparison to the first arXiv version are substantial, including various additional results, and substantial improvements to the tex

Statistilised meetodid ühendverbide tuvastamisel tekstikorpusest

Artiklis võrdlen sõnadevahelise seose tugevuse mõõtmise statistilisi meetodeid, mida kasutatakse arvutilingvistikas püsiühendite tuvastamiseks. Töö põhieesmärk on rakendada viit sümmeetrilist statistikut – t-skoori, vastastikuse informatsiooni väärtust, hii-ruut-statistikut, log-tõepära funktsiooni ja minimaalset tundlikkust – erineva suurusega korpuste peal ja välja selgitada, milline meetod töötab eesti keele ühendverbide automaatsel tuvastamisel kõige paremini. Teine suurem eesmärk on katsetulemuste põhjal uurida, milline on korpuse suuruse mõju statistikute tööle. Lisaks palju testitud nimetatud sümmeetrilistele statistikutele rakendan psühholoogiliselt paremini põhjendatud asümmeetrilisi statistikuid ning toon välja nende eelised sümmeetriliste statistikute ees

Pretrained word and multi-sense embeddings for Estonian

Word and multi-sense embedding for Estonian trained on lemmatized etTenTen: Corpus of the Estonian Web. Word embeddings are trained with word2vec. Sense embeddings are trained with SenseGram. Sense inventory is induced from word embeddings. Models were trained using various parameter settings. The values of architecture, number of dimensions, window size, minimum frequency threshold and number of iterations vary

(Non-)Literalness ratings for Estonian particle verbs

(Non-)literalness dataset of 1481 sentences formed with 184 Estonian particle verbs. Sentences are evaluated by 3 native speakers of Estonian on a 6-point scale [0,5] indicating the degree of compositionality of a particle verb. The first version of the dataset was introduced by Eleri Aedmaa, Maximilian Köper, Sabine Schulte im Walde (2018). Combining Abstractness and Language-specific Theoretical Indicators for Detecting Non-Literal Usage of Estonian Particle Verbs. In Proceedings of the NAACL 2018 Student Research Workshop (NAACL-SRW). New Orleans, LA

Estonian Dependency Treebank and its annotation scheme

<p>In this article, we present Estonian Dependency Treebank, an ongoing corpus annotation project. The size of the treebank, once finished, will be ca 400,000 words. The treebank annotation consists of three layers: morphology, syntactic functions and dependency relations. For each layer, an overview of the labels and the annotation scheme is given.</p><p>As for the actual treebank creation, each text is annotated by two independent annotators, plus a super-annotator, whose task is to solve the discrepancies. The article also gives a short overview of the most frequent sources of dissensions between the annotators.</p&gt