Context-Based Rules for Grammatical Disambiguation in the Tatar Language

© 2017, Springer International Publishing AG. The paper is dedicated to the problem of grammatical ambiguity in the Tatar National Corpus and describes the methodology and software used for automation of the disambiguation process. Grammatical ambiguity is widely represented in agglutinative languages like Turkic or Finno-Ugric. Disambiguation in the corpus is based on the context-oriented classification of ambiguity types which has been carried out on corpus data in the Tatar language for the first time. In this study the corpus is used as a source for the research and at the same time as a destination for implementing the results. The grammatical ambiguity types are detected automatically using the finite-state morphological analyzer and then classified. In order to build up the grammatically disambiguated subcorpus, a special software module was developed. It searches for ambiguous tokens in the corpus, collects statistical information and allows creating and implementing the formal context-based disambiguation rules for different ambiguity types

Infinitive Forms and Conditional Mood of Verbs as a Means of Expressing a Concretizing Relation

A concretizing link is an expression of a specific relationship between concepts using a unique means of relation. Given that, affixes and forms of verbs have a particular place. This article considers the conditions of the infinitive (-ырга/-ергә/-рга) and the conditional mood of the verb (-са/-сә). These affixes in Tatar grammars as part of a subordinate predicate clause are inflectional forms, that is, linking words. The authors study these verb forms to express a concretizing relation and analyze the shades of meanings. A more profound analysis shows that these affixes express different relations between phenomena and connect the verb with other words. They are added to all the verbs and some parts of speech when in speaking. It must be said that the affixes of the conditional mood and, to some extent, the infinitive perform the same functions for the verb as the cases do for the noun. The primary research method is descriptive-analytical with its main components: observation, generalization, and interpretation. For a detailed analysis of the linguistic features of speech, a comparative historical method is also used, which allows for the determination of some tendencies in the development of the grammatical system of the national literary language. In the studies, comparative-typological and statistical methods are also applied

Autenttisiin teksteihin perustuva tietokoneavusteinen kielen oppiminen: sovelluksia italian kielelle

Computer-Assisted Language Learning (CALL) is one of the sub-disciplines within the area of Second Language Acquisition. Clozes, also called fill-in-the-blank, are largely used exercises in language learning applications. A cloze is an exercise where the learner is asked to provide a fragment that has been removed from the text. For language learning purposes, in addition to open-end clozes where one or more words are removed and the student must fill the gap, another type of cloze is commonly used, namely multiple-choice cloze. In a multiple-choice cloze, a fragment is removed from the text and the student must choose the correct answer from multiple options. Multiple-choice exercises are a common way of practicing and testing grammatical knowledge. The aim of this work is to identify relevant learning constructs for Italian to be applied to automatic exercises creation based on authentic texts in the Revita Framework. Learning constructs are units that represent language knowledge. Revita is a free to use online platform that was designed to provide language learning tools with the aim of revitalizing endangered languages including several Finno-Ugric languages such as North Saami. Later non-endangered languages were added. Italian is the first majority language to be added in a principled way. This work paves the way towards adding new languages in the future. Its purpose is threefold: it contributes to the raising of Italian from its beta status towards a full development stage; it formulates best practices for defining support for a new language and it serves as a documentation of what has been done, how and what remains to be done. Grammars and linguistic resources were consulted to compile an inventory of learning constructs for Italian. Analytic and pronominal verbs, verb government with prepositions, and noun phrase agreement were implemented by designing pattern rules that match sequences of tokens with specific parts-of-speech, surfaces and morphological tags. The rules were tested with test sentences that allowed further refining and correction of the rules. Current precision of the 47 rules for analytic and pronominal verbs on 177 test sentences results in 100%. Recall is 96.4%. Both precision and recall for the 5 noun phrase agreement rules result in 96.0% in respect to the 34 test sentences. Analytic and pronominal verb, as well as noun phrase agreement patterns, were used to generate open-end clozes. Verb government pattern rules were implemented into multiple-choice exercises where one of the four presented options is the correct preposition and the other three are prepositions that do not fit in context. The patterns were designed based on colligations, combinations of tokens (collocations) that are also explained by grammatical constraints. Verb government exercises were generated on a specifically collected corpus of 29074 words. The corpus included three types of text: biography sections from Wikipedia, Italian news articles and Italian language matriculation exams. The last text type generated the most exercises with a rate of 19 exercises every 10000 words, suggesting that the semi-authentic text met best the level of verb government exercises because of appropriate vocabulary frequency and sentence structure complexity. Four native language experts, either teachers of Italian as L2 or linguists, evaluated usability of the generated multiple-choice clozes, which resulted in 93.55%. This result suggests that minor adjustments i.e., the exclusion of target verbs that cause multiple-admissibility, are sufficient to consider verb government patterns usable until the possibility of dealing with multiple-admissible answers is addressed. The implementation of some of the most important learning constructs for Italian resulted feasible with current NLP tools, although quantitative evaluation of precision and recall of the designed rules is needed to evaluate the generation of exercises on authentic text. This work paves the way towards a full development stage of Italian in Revita and enables further pilot studies with actual learners, which will allow to measure learning outcomes in quantitative term

Automatic rule learning exploiting morphological features for named entity recognition in Turkish

Named entity recognition (NER) is one of the basic tasks in automatic extraction of information from natural language texts. In this paper, we describe an automatic rule learning method that exploits different features of the input text to identify the named entities located in the natural language texts. Moreover, we explore the use of morphological features for extracting named entities from Turkish texts. We believe that the developed system can also be used for other agglutinative languages. The paper also provides a comprehensive overview of the field by reviewing the NER research literature. We conducted our experiments on the TurkIE dataset, a corpus of articles collected from different Turkish newspapers. Our method achieved an average F-score of 91.08% on the dataset. The results of the comparative experiments demonstrate that the developed technique is successfully applicable to the task of automatic NER and exploiting morphological features can significantly improve the NER from Turkish, an agglutinative language. © The Author(s) 2011

Mari morpheme order revisited: A corpus-based analysis

Morpheme order in Mari declension has been extensively studied in the past but attempts to explain the large amounts of alternation found here have been constrained by the difficulty of accessing sufficient data to properly elucidate the complexities in this domain. The paper at hand examines the prospect of using the Corpus of Literary Mari, created by an international workgroup around Trond Trosterud and his colleagues and hosted by Giellatekno, and other recently published resources on Mari to efficiently access vast amounts of data to quantitatively study this subject in a manner that had not previously been possible