Hesaplamalı Dil Bilimleri ve Uygur Dili Araştırmaları

Bu makalede hesaplamalı dil bilimleri kısaca anlatılmıştır ve Uygurca ile ilgili yapılan güncel hesaplamalı dil bilim araştırmaları özetlenmiştir. Teknolojinin ilerlemesi ile farklı dillere yönelik bilgisayar destekli çalışmalarda büyük başarılar elde edilmiştir. Örneğin, metinlerde içerik yönetme, bilgi edinme, konuşma sistemleri, dosya kümeleme, metin madenciliği, yazı kontrolü, yazıyı sese çevirme, sesi yazıya çevirme ve farklı diller arasında otomatik (bilgisayarlı çeviri) gibi uygulamalar geliştirilmiştir ve gerçek hayata kullanılmaktadır. Gerçi Fince, Japonca, Macarca ve Türkçe gibi Ural-Altay dilleri grubuna ait bazı diller ile ilgili birçok çalışmalar yapılsa bile, ancak yine bazı diller, örneğin Uygurca, ile ilgili yapılan çalışmalar çok az bilinmektedir. Hesaplamalı dil bilimi ile ilgili araştırmaları geliştirmek ve farklı diller arasındaki ilişkileri analiz edebilmek için, bu makalede, Uygurca ile ilgili yapılan bilgisayar destekli araştırmalar, özellik ile bilgisayarlı çeviri ile ilgili yapılan en son temel niteliğindeki çalışmalar toparlanmıştır. Aynı anda dil bilimcileri ile hesaplamalı dil bilimleri arasındaki bağıntı analiz edilmiştir

Multiethnic Societies of Central Asia and Siberia Represented in Indigenous Oral and Written Literature

Central Asia and Siberia are characterized by multiethnic societies formed by a patchwork of often small ethnic groups. At the same time large parts of them have been dominated by state languages, especially Russian and Chinese. On a local level the languages of the autochthonous people often play a role parallel to the central national language. The contributions of this conference proceeding follow up on topics such as: What was or is collected and how can it be used under changed conditions in the research landscape, how does it help local ethnic communities to understand and preserve their own culture and language? Do the spatially dispersed but often networked collections support research on the ground? What contribution do these collections make to the local languages and cultures against the backdrop of dwindling attention to endangered groups? These and other questions are discussed against the background of the important role libraries and private collections play for multiethnic societies in often remote regions that are difficult to reach

Cross-Lingual and Low-Resource Sentiment Analysis

Identifying sentiment in a low-resource language is essential for understanding opinions internationally and for responding to the urgent needs of locals affected by disaster incidents in different world regions. While tools and resources for recognizing sentiment in high-resource languages are plentiful, determining the most effective methods for achieving this task in a low-resource language which lacks annotated data is still an open research question. Most existing approaches for cross-lingual sentiment analysis to date have relied on high-resource machine translation systems, large amounts of parallel data, or resources only available for Indo-European languages. This work presents methods, resources, and strategies for identifying sentiment cross-lingually in a low-resource language. We introduce a cross-lingual sentiment model which can be trained on a high-resource language and applied directly to a low-resource language. The model offers the feature of lexicalizing the training data using a bilingual dictionary, but can perform well without any translation into the target language. Through an extensive experimental analysis, evaluated on 17 target languages, we show that the model performs well with bilingual word vectors pre-trained on an appropriate translation corpus. We compare in-genre and in-domain parallel corpora, out-of-domain parallel corpora, in-domain comparable corpora, and monolingual corpora, and show that a relatively small, in-domain parallel corpus works best as a transfer medium if it is available. We describe the conditions under which other resources and embedding generation methods are successful, and these include our strategies for leveraging in-domain comparable corpora for cross-lingual sentiment analysis. To enhance the ability of the cross-lingual model to identify sentiment in the target language, we present new feature representations for sentiment analysis that are incorporated in the cross-lingual model: bilingual sentiment embeddings that are used to create bilingual sentiment scores, and a method for updating the sentiment embeddings during training by lexicalization of the target language. This feature configuration works best for the largest number of target languages in both untargeted and targeted cross-lingual sentiment experiments. The cross-lingual model is studied further by evaluating the role of the source language, which has traditionally been assumed to be English. We build cross-lingual models using 15 source languages, including two non-European and non-Indo-European source languages: Arabic and Chinese. We show that language families play an important role in the performance of the model, as does the morphological complexity of the source language. In the last part of the work, we focus on sentiment analysis towards targets. We study Arabic as a representative morphologically complex language and develop models and morphological representation features for identifying entity targets and sentiment expressed towards them in Arabic open-domain text. Finally, we adapt our cross-lingual sentiment models for the detection of sentiment towards targets. Through cross-lingual experiments on Arabic and English, we demonstrate that our findings regarding resources, features, and language also hold true for the transfer of targeted sentiment

Multiethnic Societies of Central Asia and Siberia Represented in Indigenous Oral and Written Literature

Central Asia and Siberia are characterized by multiethnic societies formed by a patchwork of often small ethnic groups. At the same time large parts of them have been dominated by state languages, especially Russian and Chinese. On a local level the languages of the autochthonous people often play a role parallel to the central national language. The contributions of this conference proceeding follow up on topics such as: What was or is collected and how can it be used under changed conditions in the research landscape, how does it help local ethnic communities to understand and preserve their own culture and language? Do the spatially dispersed but often networked collections support research on the ground? What contribution do these collections make to the local languages and cultures against the backdrop of dwindling attention to endangered groups? These and other questions are discussed against the background of the important role libraries and private collections play for multiethnic societies in often remote regions that are difficult to reach

UniMorph 4.0:Universal Morphology

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements made on several fronts over the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 67 new languages, including 30 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g. missing gender and macron information. We have also amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet

Computational Linguistics and Adaptation of Turkic Languages to Computer

This article describes computational linguistics briefly, and explains Turkic language studies in this field using Uyghur language as an example. With developing computer technologies, many software has been implemented in order to complete some tasks in place of human. For example, translate from one language to another, or translate from one language to more than one languages at the same time, correcting or editing texts, analyzing documents, converting speeches into texts or converting texts into speeches etc. Until now, there are many successful researches have been done on different languages such as English, Japanese, Arabic, Turkish, Chinese, French and Russian etc. In Turkic languages, especially in Turkey Turkish, though there are some important researches have been done, other Turkic languages still at a beginning stage. Though, Uyghur language belongs to Turkic language family and it has common properties with other languages, however research results about other Turkic languages cannot be applied to Uyghur language directly. As a natural language, Uyghur language has many special properties those (are) different from other Turkic languages. This paper summarizes some computer based researches about Uyghur language and use them as a part of general machine translation system of the Turkic world