To Normalize, or Not to Normalize: The Impact of Normalization on Part-of-Speech Tagging

Does normalization help Part-of-Speech (POS) tagging accuracy on noisy, non-canonical data? To the best of our knowledge, little is known on the actual impact of normalization in a real-world scenario, where gold error detection is not available. We investigate the effect of automatic normalization on POS tagging of tweets. We also compare normalization to strategies that leverage large amounts of unlabeled data kept in its raw form. Our results show that normalization helps, but does not add consistently beyond just word embedding layer initialization. The latter approach yields a tagging model that is competitive with a Twitter state-of-the-art tagger.Comment: In WNUT 201

Kompiliranje korpusa u digitalnim humanističkim znanostima u jezicima s ograničenim resursima: o praksi kompiliranja tematskih korpusa iz digitalnih medija za srpski, hrvatski i slovenski

The digital era has unlocked unprecedented possibilities of compiling corpora of social discourse, which has brought corpus linguistic methods into closer interaction with other methods of discourse analysis and the humanities. Even when not using any specific techniques of corpus linguistics, drawing on some sort of corpus is increasingly resorted to for empirically–grounded social–scientific analysis (sometimes dubbed ‘corpus–assisted discourse analysis’ or ‘corpus–based critical discourse analysis’, cf. Hardt–Mautner 1995; Baker 2016). In the post–Yugoslav space, recent corpus developments have brought table–turning advantages in many areas of discourse research, along with an ongoing proliferation of corpora and tools. Still, for linguists and discourse analysts who embark on collecting specialized corpora for their own research purposes, many questions persist – partly due to the fast–changing background of these issues, but also due to the fact that there is still a gap in the corpus method, and in guidelines for corpus compilation, when applied beyond the anglophone contexts. In this paper we aim to discuss some possible solutions to these difficulties, by presenting one step–by–step account of a corpus building procedure specifically for Croatian, Serbian and Slovenian, through an example of compiling a thematic corpus from digital media sources (news articles and reader comments). Following an overview of corpus types, uses and advantages in social sciences and digital humanities, we present the corpus compilation possibilities in the South Slavic language contexts, including data scraping options, permissions and ethical issues, the factors that facilitate or complicate automated collection, and corpus annotation and processing possibilities. The study shows expanding possibilities for work with the given languages, but also some persistently grey areas where researchers need to make decisions based on research expectations. Overall, the paper aims to recapitulate our own corpus compilation experience in the wider context of South–Slavic corpus linguistics and corpus linguistic approaches in the humanities more generallyDigitalno doba otvorilo je nove mogućnosti za sastavljanje korpusa društvenog diskursa, što je korpusnolingvističke metode približilo drugim metodama analize diskursa te humanističkim znanostima. Čak i kada se ne koriste nikakve specifične tehnike korpusne lingvistike, danas je za empirijski utemeljenu društveno–znanstvenu analizu sve učestalije korištenje neke vrste korpusa (‘korpusno–asistirana analiza diskursa’ ili ‘kritička korpusna analiza’, Hardt–Mautner 1995; Baker 2016). U postjugoslavenskom prostoru, nedavni razvoj korpusne lingvistike donio je prednosti u mnogim područjima istraživanja. Ipak, za lingviste i analitičare diskursa koji se upuštaju u prikupljanje specijaliziranih korpusa za vlastite istraživačke svrhe, i dalje ostaju otvorena mnoga pitanja – djelomično zbog pozadine korpusne lingvistike koja se brzo mijenja, ali i zbog činjenice da još uvijek postoji rascjep u poznavanju korpusnih metoda, kao i metodologije sastavljanja korpusa izvan anglofonskog konteksta. Ovim radom pokušavamo smanjiti spomenuti rascjep predstavljajući jedan postupni prikaz postupka izgradnje korpusa za hrvatski, srpski i slovenski, kroz primjer sastavljanja tematskog korpusa iz digitalnih medija (novinski članci i komentari čitatelja). Nakon pregleda tipova korpusa, korištenja i prednosti u društvenim znanostima i digitalnim humanističkim znanostima, predstavljamo mogućnosti sastavljanja korpusa u južnoslavenskim jezičnim kontekstima, uključujući opcije preuzimanja podataka s mreže, dozvola i etičkih pitanja, čimbenika koji olakšavaju ili otežavaju automatizirano prikupljanje i označavanje korpusa i mogućnosti obrade. Studija otkriva sve veće mogućnosti za rad s danim jezicima, ali i neka uporno siva područja u kojima istraživači trebaju donositi odluke na temelju istraživačkih očekivanja. Općenito, rad ima za cilj rekapitulirati vlastito iskustvo sastavljanja korpusa u širem kontekstu južnoslavenske korpusne lingvistike i korpusnih lingvističkih pristupa u humanističkim znanostima općenito

Neural morphosyntactic tagging for Rusyn

The paper presents experiments on part-of-speech and full morphological tagging of the Slavic minority language Rusyn. The proposed approach relies on transfer learning and uses only annotated resources from related Slavic languages, namely Russian, Ukrainian, Slovak, Polish, and Czech. It does not require any annotated Rusyn training data, nor parallel data or bilingual dictionaries involving Rusyn. Compared to earlier work, we improve tagging performance by using a neural network tagger and larger training data from the neighboring Slavic languages.We experiment with various data preprocessing and sampling strategies and evaluate the impact of multitask learning strategies and of pretrained word embeddings. Overall, while genre discrepancies between training and test data have a negative impact, we improve full morphological tagging by 9% absolute micro-averaged F1 as compared to previous research.Peer reviewe

Building English-to-Serbian machine translation system for IMDb movie reviews

This paper reports the results of the first experiment dealing with the challenges of building a machine translation system for user-generated content involving a complex South Slavic language. We focus on translation of English IMDb user movie reviews into Serbian, in a low-resource scenario. We explore potentials and limits of (i) phrase-based and neural machine translation systems trained on out-of-domain clean parallel data from news articles (ii) creating additional synthetic in-domain parallel corpus by machine-translating the English IMDb corpus into Serbian. Our main findings are that morphology and syntax are better handled by the neural approach than by the phrase-based approach even in this low-resource mismatched domain scenario, however the situation is different for the lexical aspect, especially for person names. This finding also indicates that in general, machine translation of person names into Slavic languages (especially those which require/allow transcription) should be investigated more systematically

Natural Language Processing for Under-resourced Languages: Developing a Welsh Natural Language Toolkit

Language technology is becoming increasingly important across a variety of application domains which have become common place in large, well-resourced languages. However, there is a danger that small, under-resourced languages are being increasingly pushed to the technological margins. Under-resourced languages face significant challenges in delivering the underlying language resources necessary to support such applications. This paper describes the development of a natural language processing toolkit for an under-resourced language, Cymraeg (Welsh). Rather than creating the Welsh Natural Language Toolkit (WNLT) from scratch, the approach involved adapting and enhancing the language processing functionality provided for other languages within an existing framework and making use of external language resources where available. This paper begins by introducing the GATE NLP framework, which was used as the development platform for the WNLT. It then describes each of the core modules of the WNLT in turn, detailing the extensions and adaptations required for Welsh language processing. An evaluation of the WNLT is then reported. Following this, two demonstration applications are presented. The first is a simple text mining application that analyses wedding announcements. The second describes the development of a Twitter NLP application, which extends the core WNLT pipeline. As a relatively small-scale project, the WNLT makes use of existing external language resources where possible, rather than creating new resources. This approach of adaptation and reuse can provide a practical and achievable route to developing language resources for under-resourced languages

Natural language processing for similar languages, varieties, and dialects: A survey

There has been a lot of recent interest in the natural language processing (NLP) community in the computational processing of language varieties and dialects, with the aim to improve the performance of applications such as machine translation, speech recognition, and dialogue systems. Here, we attempt to survey this growing field of research, with focus on computational methods for processing similar languages, varieties, and dialects. In particular, we discuss the most important challenges when dealing with diatopic language variation, and we present some of the available datasets, the process of data collection, and the most common data collection strategies used to compile datasets for similar languages, varieties, and dialects. We further present a number of studies on computational methods developed and/or adapted for preprocessing, normalization, part-of-speech tagging, and parsing similar languages, language varieties, and dialects. Finally, we discuss relevant applications such as language and dialect identification and machine translation for closely related languages, language varieties, and dialects.Non peer reviewe

Norm It! Lexical Normalization for Italian and Its Downstream Effects forDependency Parsing

Lexical normalization is the task of translating non-standard social media data to a standard form. Previous work has shown that this is beneficial for many downstream tasks in multiple languages. However, for Italian, there is no benchmark available for lexical normalization, despite the presence of many benchmarks for other tasks involving social media data. In this paper, we discuss the creation of a lexical normalization dataset for Italian. After two rounds of annotation, a Cohen’s kappa score of 78.64 is obtained. During this process, we also analyze the inter-annotator agreement for this task, which is only rarely done on datasets for lexical normalization,and when it is reported, the analysis usually remains shallow. Furthermore, we utilize this dataset to train a lexical normalization model and show that it can be used to improve dependency parsing of social media data. All annotated data and the code to reproduce the results are available at: http://bitbucket.org/robvanderg/normi

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

Peer reviewe

Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information

This thesis explores methods to rapidly bootstrap automatic speech recognition systems for languages, which lack resources for speech and language processing. We focus on finding approaches which allow using data from multiple languages to improve the performance for those languages on different levels, such as feature extraction, acoustic modeling and language modeling. Under application aspects, this thesis also includes research work on non-native and Code-Switching speech