Exploring the use of parallel corpora in the complilation of specialised bilingual dictionaries of technical terms: a case study of English and isiXhosa

Text in EnglishAbstracts in English, isiXhosa and AfrikaansThe Constitution of the Republic of South Africa, Act 108 of 1996, mandates the state to take practical and positive measures to elevate the status and the use of indigenous languages. The implementation of this pronouncement resulted in a growing demand for specialised translations in fields like technology, science, commerce, law and finance. The lack of terminology and resources such as specialised bilingual dictionaries in indigenous languages, particularly isiXhosa remains a growing concern that hinders the translation and the intellectualisation of isiXhosa. A growing number of African scholars affirm the importance of specialised dictionaries in the African languages as tools for language and terminology development so that African languages can be used in the areas of science and technology. In the light of the background above, this study explored how parallel corpora can be interrogated using a bilingual concordancer, ParaConc to extract bilingual terminology that can be used to create specialised bilingual dictionaries. A corpus-based approach was selected due to its speed, efficiency and accuracy in extracting bilingual terms in their immediate contexts. In enhancing the research outcomes, Descriptive Translations Studies (DTS) and Corpus-based translation studies (CTS) were used in a complementary manner. Because the study is interdisciplinary, the function theories of lexicography that emphasise the function and needs of users were also applied. The analysis and extraction of bilingual terminology for dictionary making was successful through the use of the following ParaConc features, namely frequencies, hot word lists, hot words, search facility and concordances (Key Word in Context), among others. The findings revealed that English-isiXhosa Parallel Corpus is a repository of translation equivalents and other information categories that can make specialised dictionaries more user-friendly and multifunctional. The frequency lists were revealed as an effective method of selecting headwords for inclusion in a dictionary. The results also unraveled the complex functions of bilingual concordances where information on collocations and multiword units, sense distinction and usage examples could be easily identifiable proving that this approach is more efficient than the traditional method. The study contributes to the knowledge on corpus-based lexicography, standardisation of finance terminology resource development and making of user-friendly dictionaries that are tailor-made for different needs of users.Umgaqo-siseko weli loMzantsi Afrika ukhululele uRhulumente ukuba athabathe amanyathelo abonakalayo ekuphuhliseni nasekuphuculeni iilwimi zesiNtu. Esi sindululo sibangele ukwanda kokuguqulelwa kwamaxwebhu angezobuchwepheshe, inzululwazi, umthetho, ezemali noqoqosho angesiNgesi eguqulelwa kwiilwimi ebezifudula zingasiwe-so ezinjengesiXhosa. Ukunqongophala kwesigama kunye nezichazi-magama kube yingxaki enkulu ekuguquleleni ngakumbi izichazi-magama ezilwimi-mbini eziqulethe isigama esikhethekileyo. Iingcali ezininzi ziyangqinelana ukuba olu hlobo lwezi zichazi-magama luyimfuneko kuba ludlala iindima enkulu ekuphuhlisweni kweelwimi zesiNtu, ekuyileni isigama, nasekusetyenzisweni kwazo kumabakala obunzululwazi nobuchwepheshe. Olu phando ke luvavanya ukusetyenziswa kwekhophasi equlethe amaxwebhu esiNgesi neenguqulelo zawo zesiXhosa njengovimba wokudimbaza isigama sezemali esinokunceda ekuqulunqweni kwesichazi-magama esilwimi-mbini. Isizathu esibangele ukukhetha le ndlela yophando esebenzisa ikhompyutha kukuba iyakhawuleza, ulwazi oluthathwe kwikhophasi luchanekile, yaye isigama kwikhophasi singqamana ngqo nomxholo wamaxwebhu nto leyo eyenza kube lula ukufumana iintsingiselo nemizekelo ephilayo. Ukutyebisa olu phando indlela yekhophasi iye yaxhaswa zezinye iindlela zophando ezityunjiweyo: ufundo lwenguguqulelo oluchazayo (DTS) kunye neendlela zokuguqulela ezijoliswe kumsebenzi nakuhlobo lwabasebenzisi zinguqulelo ezo. Kanti ke ziqwalaselwe neenkqubo zophando lobhalo-zichazi-magama eziinjongo zokuqulunqa izichazi-magama ezesebenzisekayo neziluncedo kuninzi lwabasebenzisi zichazi-magama ngakumbi kwisizwe esisebenzisa iilwimi ezininzi. Ukuhlalutya nokudimbaza isigama kwikhophasi kolu phando kusetyenziswe isixhobo sekhompyutha esilungiselelwe ikhophasi enelwiimi ezimbini nangaphezulu ebizwa ngokuba yiParaConc. Iziphumo zolu phando zibonise mhlophe ukuba ikhophasi eneenguqulelo nguvimba weendidi ngendidi zamagama nolwazi olunokuphucula izichazi-magama zeli xesha. Kaloku abaguquleli basebenzise amaqhinga ngamaqhinga ukunika iinguqulelo bekhokelwa yimigomo nemithetho yoguqulelo enxuse abasebenzisi bamaxwebhu aguqulelweyo. Ubuchule beParaConc bokukwazi ukuhlela amagama ngokwendlela afumaneka ngayo kunye neenkcukacha zamanani budandalazise indlela eyiyo yokukhetha imichazwa enokungena kwisichazi-magama. Iziphumo zikwabonakalise iintlaninge yolwazi olufumaneka kwiKWIC, lwazi olo olungelula ukulufumana xa usebenzisa undlela-ndala wokwakha isichazi-magama. Esi sifundo esihlanganyele uGuqulelo olusekelwe kwiKhophasi noQulunqo-zichazi-magama zobuchwepheshe luya kuba negalelo elingathethekiyo kwindlela yokwakha izichazi-magama kwilwiimi zeSintu ngokubanzi nancakasana kwisiXhosa, nto leyo eya kothula umthwalo kubaqulunqi-zichazi-magama. Ukwakha nokuqulunqa izichazi-magama ezilwimi-mbini zezemali kuya kwandisa imithombo yesigama esinqongopheleyo kananjalo sivelise izichazi-magama eziluncedo kwisininzi sabantu.Die Grondwet van die Republiek van Suid-Afrika, Wet 108 van 1996, gee aan die staat die mandaat om praktiese en positiewe maatreëls te tref om die status en gebruik van inheemse tale te verhoog. Die implementering van hierdie uitspraak het gelei tot ’n toenemende vraag na gespesialiseerde vertalings in domeine soos tegnologie, wetenskap, handel, regte en finansies. Die gebrek aan terminologie en hulpbronne soos gespesialiseerde woordeboeke in inheemse tale, veral Xhosa, wek toenemende kommer wat die vertaling en die intellektualisering van Xhosa belemmer. ’n Toenemende aantal vakkundiges in Afrika beklemtoon die belangrikheid van gespesialiseerde woordeboeke in die Afrikatale as instrumente vir taal- en terminologie-ontwikkeling sodat Afrikatale gebruik kan word in die areas van wetenskap en tegnologie. In die lig van die voorafgaande agtergrond het hierdie studie ondersoek ingestel na hoe parallelle korpora deursoek kan word deur ’n tweetalige konkordanser (ParaConc) te gebruik om tweetalige terminologie te ontgin wat gebruik kan word in die onwikkeling van tweetalige gespesialiseerde woordeboeke. ’n Korpusgebaseerde benadering is gekies vir die spoed, doeltreffendheid en akkuraatheid waarmee dit tweetalige terme uit hulle onmiddellike kontekste kan onttrek. Beskrywende Vertaalstudies (DTS) en Korpusgebaseerde Vertaalstudies (CTS) is op ’n aanvullende wyse gebruik om die navorsingsuitkomste te verbeter. Aangesien die studie interdissiplinêr is, is die funksieteorieë van leksikografie wat die funksie en behoeftes van gebruikers beklemtoon, ook toegepas. Die analise en ontginning van tweetalige terminologie om woordeboeke te ontwikkel was suksesvol deur, onder andere, gebruik te maak van die volgende ParaConc-eienskappe, naamlik, frekwensies, hotword-lyste, hot words, die soekfunksie en konkordansies (Sleutelwoord-in-Konteks). Die bevindings toon dat ’n Engels-Xhosa Parallelle Korpus ’n bron van vertaalekwivalente en ander inligtingskategorieë is wat gespesialiseerde woordeboeke meer gebruikersvriendelik en multifunksioneel kan maak. Die frekwensielyste is geïdentifiseer as ’n doeltreffende metode om hoofwoorde te selekteer wat opgeneem kan word in ’n woordeboek. Die bevindings het ook die komplekse funksies van tweetalige konkordansers ontknoop waar inligting oor kollokasies en veelvuldigewoord-eenhede, betekenisonderskeiding en gebruiksvoorbeelde maklik identifiseer kon word wat aandui dat hierdie metode viii doeltreffender is as die tradisionele metode. Die studie dra by tot die kennisveld van korpusgebaseerde leksikografie, standaardisering van finansiële terminologie, hulpbronontwikkeling en die ontwikkeling van gebruikersvriendelike woordeboeke wat doelgemaak is vir verskillende behoeftes van gebruikers.Linguistics and Modern LanguagesD. Litt. et Phil. (Linguistics (Translation Studies)

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Application of pre-training and fine-tuning AI models to machine translation: a case study of multilingual text classification in Baidu

With the development of international information technology, we are producing a huge amount of information all the time. The processing ability of information in various languages is gradually replacing information and becoming a rarer resource. How to obtain the most effective information in such a large and complex amount of multilingual textual information is a major goal of multilingual information processing. Multilingual text classification helps users to break the language barrier and accurately locate the required information and triage information. At the same time, the rapid development of the Internet has accelerated the communication among users of various languages, giving rise to a large number of multilingual texts, such as book and movie reviews, online chats, product introductions and other forms, which contain a large amount of valuable implicit information and urgently need automated tools to categorize and process those multilingual texts. This work describes the Natural Language Process (NLP) sub-task known as Multilingual Text Classification (MTC) performed within the context of Baidu, a Chinese leading AI company with a strong Internet base, whose NLP division led the industry in deep learning technology to go online in Machine Translation (MT) and search. Multilingual text classification is an important module in NLP machine translation and a basic module in NLP tasks. It can be applied to many fields, such as Fake Reviews Detection, News Headlines Categories Classification, Analysis of positive and negative reviews and so on. In the following work, we will first define the AI model paradigm of 'pre-training and fine-tuning' in deep learning in the Baidu NLP department. Then investigated the application scenarios of multilingual text classification. Most of the text classification systems currently available in the Chinese market are designed for a single language, such as Alibaba's text classification system. If users need to classify texts of the same category in multiple languages, they need to train multiple single text classification systems and then classify them one by one. However, many internationalized products do not have a single text language, such as AliExpress cross-border e-commerce business, Airbnb B&B business, etc. Industry needs to understand and classify users’ reviews in various languages, and have conducted in-depth statistics and marketing strategy development, and multilingual text classification is particularly important in this scenario. Therefore, we focus on interpreting the methodology of multilingual text classification model of machine translation in Baidu NLP department, and capture sets of multilingual data of reviews, news headlines and other data for manual classification and labeling, use the labeling results for fine-tuning of multilingual text classification model, and output the quality evaluation data of Baidu multilingual text classification model after fine-tuning. We will discuss if the pre-training and fine-tuning of the large model can substantially improve the quality and performance of multilingual text classification. Finally, based on the machine translation-multilingual text classification model, we derive the application method of pre-training and fine-tuning paradigm in the current cutting-edge deep learning AI model under the NLP system and verify the generality and cutting-edge of the pre-training and fine-tuning paradigm in the deep learning-intelligent search field.Com o desenvolvimento da tecnologia de informação internacional, estamos sempre a produzir uma enorme quantidade de informação e o recurso mais escasso já não é a informação, mas a capacidade de processar informação em cada língua. A maior parte da informação multilingue é expressa sob a forma de texto. Como obter a informação mais eficaz numa quantidade tão considerável e complexa de informação textual multilingue é um dos principais objetivos do processamento de informação multilingue. A classificação de texto multilingue ajuda os utilizadores a quebrar a barreira linguística e a localizar com precisão a informação necessária e a classificá-la. Ao mesmo tempo, o rápido desenvolvimento da Internet acelerou a comunicação entre utilizadores de várias línguas, dando origem a um grande número de textos multilingues, tais como críticas de livros e filmes, chats, introduções de produtos e outros distintos textos, que contêm uma grande quantidade de informação implícita valiosa e necessitam urgentemente de ferramentas automatizadas para categorizar e processar esses textos multilingues. Este trabalho descreve a subtarefa do Processamento de Linguagem Natural (PNL) conhecida como Classificação de Texto Multilingue (MTC), realizada no contexto da Baidu, uma empresa chinesa líder em IA, cuja equipa de PNL levou a indústria em tecnologia baseada em aprendizagem neuronal a destacar-se em Tradução Automática (MT) e pesquisa científica. A classificação multilingue de textos é um módulo importante na tradução automática de PNL e um módulo básico em tarefas de PNL. A MTC pode ser aplicada a muitos campos, tais como análise de sentimentos multilingues, categorização de notícias, filtragem de conteúdos indesejados (do inglês spam), entre outros. Neste trabalho, iremos primeiro definir o paradigma do modelo AI de 'pré-treino e afinação' em aprendizagem profunda no departamento de PNL da Baidu. Em seguida, realizaremos a pesquisa sobre outros produtos no mercado com capacidade de classificação de texto — a classificação de texto levada a cabo pela Alibaba. Após a pesquisa, verificamos que a maioria dos sistemas de classificação de texto atualmente disponíveis no mercado chinês são concebidos para uma única língua, tal como o sistema de classificação de texto Alibaba. Se os utilizadores precisarem de classificar textos da mesma categoria em várias línguas, precisam de aplicar vários sistemas de classificação de texto para cada língua e depois classificá-los um a um. No entanto, muitos produtos internacionalizados não têm uma única língua de texto, tais como AliExpress comércio eletrónico transfronteiriço, Airbnb B&B business, etc. A indústria precisa compreender e classificar as revisões dos utilizadores em várias línguas. Esta necessidade conduziu a um desenvolvimento aprofundado de estatísticas e estratégias de marketing, e a classificação de textos multilingues é particularmente importante neste cenário. Desta forma, concentrar-nos-emos na interpretação da metodologia do modelo de classificação de texto multilingue da tradução automática no departamento de PNL Baidu. Colhemos para o efeito conjuntos de dados multilingues de comentários e críticas, manchetes de notícias e outros dados para classificação manual, utilizamos os resultados dessa classificação para o aperfeiçoamento do modelo de classificação de texto multilingue e produzimos os dados de avaliação da qualidade do modelo de classificação de texto multilingue da Baidu. Discutiremos se o pré-treino e o aperfeiçoamento do modelo podem melhorar substancialmente a qualidade e o desempenho da classificação de texto multilingue. Finalmente, com base no modelo de classificação de texto multilingue de tradução automática, derivamos o método de aplicação do paradigma de pré-formação e afinação no atual modelo de IA de aprendizagem profunda de ponta sob o sistema de PNL, e verificamos a robustez e os resultados positivos do paradigma de pré-treino e afinação no campo de pesquisa de aprendizagem profunda

Foundation, Implementation and Evaluation of the MorphoSaurus System: Subword Indexing, Lexical Learning and Word Sense Disambiguation for Medical Cross-Language Information Retrieval

Im medizinischen Alltag, zu welchem viel Dokumentations- und Recherchearbeit gehört, ist mittlerweile der überwiegende Teil textuell kodierter Information elektronisch verfügbar. Hiermit kommt der Entwicklung leistungsfähiger Methoden zur effizienten Recherche eine vorrangige Bedeutung zu. Bewertet man die Nützlichkeit gängiger Textretrievalsysteme aus dem Blickwinkel der medizinischen Fachsprache, dann mangelt es ihnen an morphologischer Funktionalität (Flexion, Derivation und Komposition), lexikalisch-semantischer Funktionalität und der Fähigkeit zu einer sprachübergreifenden Analyse großer Dokumentenbestände. In der vorliegenden Promotionsschrift werden die theoretischen Grundlagen des MorphoSaurus-Systems (ein Akronym für Morphem-Thesaurus) behandelt. Dessen methodischer Kern stellt ein um Morpheme der medizinischen Fach- und Laiensprache gruppierter Thesaurus dar, dessen Einträge mittels semantischer Relationen sprachübergreifend verknüpft sind. Darauf aufbauend wird ein Verfahren vorgestellt, welches (komplexe) Wörter in Morpheme segmentiert, die durch sprachunabhängige, konzeptklassenartige Symbole ersetzt werden. Die resultierende Repräsentation ist die Basis für das sprachübergreifende, morphemorientierte Textretrieval. Neben der Kerntechnologie wird eine Methode zur automatischen Akquise von Lexikoneinträgen vorgestellt, wodurch bestehende Morphemlexika um weitere Sprachen ergänzt werden. Die Berücksichtigung sprachübergreifender Phänomene führt im Anschluss zu einem neuartigen Verfahren zur Auflösung von semantischen Ambiguitäten. Die Leistungsfähigkeit des morphemorientierten Textretrievals wird im Rahmen umfangreicher, standardisierter Evaluationen empirisch getestet und gängigen Herangehensweisen gegenübergestellt

Language technologies for a multilingual Europe

This volume of the series “Translation and Multilingual Natural Language Processing” includes most of the papers presented at the Workshop “Language Technology for a Multilingual Europe”, held at the University of Hamburg on September 27, 2011 in the framework of the conference GSCL 2011 with the topic “Multilingual Resources and Multilingual Applications”, along with several additional contributions. In addition to an overview article on Machine Translation and two contributions on the European initiatives META-NET and Multilingual Web, the volume includes six full research articles. Our intention with this workshop was to bring together various groups concerned with the umbrella topics of multilingualism and language technology, especially multilingual technologies. This encompassed, on the one hand, representatives from research and development in the field of language technologies, and, on the other hand, users from diverse areas such as, among others, industry, administration and funding agencies. The Workshop “Language Technology for a Multilingual Europe” was co-organised by the two GSCL working groups “Text Technology” and “Machine Translation” (http://gscl.info) as well as by META-NET (http://www.meta-net.eu)

Proceedings

Proceedings of the Workshop CHAT 2011: Creation, Harmonization and Application of Terminology Resources. Editors: Tatiana Gornostay and Andrejs Vasiļjevs. NEALT Proceedings Series, Vol. 12 (2011). © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/16956

Proceedings of the COLING 2004 Post Conference Workshop on Multilingual Linguistic Ressources MLR2004

International audienceIn an ever expanding information society, most information systems are now facing the "multilingual challenge". Multilingual language resources play an essential role in modern information systems. Such resources need to provide information on many languages in a common framework and should be (re)usable in many applications (for automatic or human use). Many centres have been involved in national and international projects dedicated to building har- monised language resources and creating expertise in the maintenance and further development of standardised linguistic data. These resources include dictionaries, lexicons, thesauri, word-nets, and annotated corpora developed along the lines of best practices and recommendations. However, since the late 90's, most efforts in scaling up these resources remain the responsibility of the local authorities, usually, with very low funding (if any) and few opportunities for academic recognition of this work. Hence, it is not surprising that many of the resource holders and developers have become reluctant to give free access to the latest versions of their resources, and their actual status is therefore currently rather unclear. The goal of this workshop is to study problems involved in the development, management and reuse of lexical resources in a multilingual context. Moreover, this workshop provides a forum for reviewing the present state of language resources. The workshop is meant to bring to the international community qualitative and quantitative information about the most recent developments in the area of linguistic resources and their use in applications. The impressive number of submissions (38) to this workshop and in other workshops and conferences dedicated to similar topics proves that dealing with multilingual linguistic ressources has become a very hot problem in the Natural Language Processing community. To cope with the number of submissions, the workshop organising committee decided to accept 16 papers from 10 countries based on the reviewers' recommendations. Six of these papers will be presented in a poster session. The papers constitute a representative selection of current trends in research on Multilingual Language Resources, such as multilingual aligned corpora, bilingual and multilingual lexicons, and multilingual speech resources. The papers also represent a characteristic set of approaches to the development of multilingual language resources, such as automatic extraction of information from corpora, combination and re-use of existing resources, online collaborative development of multilingual lexicons, and use of the Web as a multilingual language resource. The development and management of multilingual language resources is a long-term activity in which collaboration among researchers is essential. We hope that this workshop will gather many researchers involved in such developments and will give them the opportunity to discuss, exchange, compare their approaches and strengthen their collaborations in the field. The organisation of this workshop would have been impossible without the hard work of the program committee who managed to provide accurate reviews on time, on a rather tight schedule. We would also like to thank the Coling 2004 organising committee that made this workshop possible. Finally, we hope that this workshop will yield fruitful results for all participants

D-TERMINE : data-driven term extraction methodologies investigated

Automatic term extraction is a task in the field of natural language processing that aims to automatically identify terminology in collections of specialised, domain-specific texts. Terminology is defined as domain-specific vocabulary and consists of both single-word terms (e.g., corpus in the field of linguistics, referring to a large collection of texts) and multi-word terms (e.g., automatic term extraction). Terminology is a crucial part of specialised communication since terms can concisely express very specific and essential information. Therefore, quickly and automatically identifying terms is useful in a wide range of contexts. Automatic term extraction can be used by language professionals to find which terms are used in a domain and how, based on a relevant corpus. It is also useful for other tasks in natural language processing, including machine translation. One of the main difficulties with term extraction, both manual and automatic, is the vague boundary between general language and terminology. When different people identify terms in the same text, it will invariably produce different results. Consequently, creating manually annotated datasets for term extraction is a costly, time- and effort- consuming task. This can hinder research on automatic term extraction, which requires gold standard data for evaluation, preferably even in multiple languages and domains, since terms are language- and domain-dependent. Moreover, supervised machine learning methodologies rely on annotated training data to automatically deduce the characteristics of terms, so this knowledge can be used to detect terms in other corpora as well. Consequently, the first part of this PhD project was dedicated to the construction and validation of a new dataset for automatic term extraction, called ACTER – Annotated Corpora for Term Extraction Research. Terms and Named Entities were manually identified with four different labels in twelve specialised corpora. The dataset contains corpora in three languages and four domains, leading to a total of more than 100k annotations, made over almost 600k tokens. It was made publicly available during a shared task we organised, in which five international teams competed to automatically extract terms from the same test data. This illustrated how ACTER can contribute towards advancing the state-of-the-art. It also revealed that there is still a lot of room for improvement, with moderate scores even for the best teams. Therefore, the second part of this dissertation was devoted to researching how supervised machine learning techniques might contribute. The traditional, hybrid approach to automatic term extraction relies on a combination of linguistic and statistical clues to detect terms. An initial list of unique candidate terms is extracted based on linguistic information (e.g., part-of-speech patterns) and this list is filtered based on statistical metrics that use frequencies to measure whether a candidate term might be relevant. The result is a ranked list of candidate terms. HAMLET – Hybrid, Adaptable Machine Learning Approach to Extract Terminology – was developed based on this traditional approach and applies machine learning to efficiently combine more information than could be used with a rule-based approach. This makes HAMLET less susceptible to typical issues like low recall on rare terms. While domain and language have a large impact on results, robust performance was reached even without domain- specific training data, and HAMLET compared favourably to a state-of-the-art rule-based system. Building on these findings, the third and final part of the project was dedicated to investigating methodologies that are even further removed from the traditional approach. Instead of starting from an initial list of unique candidate terms, potential terms were labelled immediately in the running text, in their original context. Two sequential labelling approaches were developed, evaluated and compared: a feature- based conditional random fields classifier, and a recurrent neural network with word embeddings. The latter outperformed the feature-based approach and was compared to HAMLET as well, obtaining comparable and even better results. In conclusion, this research resulted in an extensive, reusable dataset and three distinct new methodologies for automatic term extraction. The elaborate evaluations went beyond reporting scores and revealed the strengths and weaknesses of the different approaches. This identified challenges for future research, since some terms, especially ambiguous ones, remain problematic for all systems. However, overall, results were promising and the approaches were complementary, revealing great potential for new methodologies that combine multiple strategies