The construction of a linguistic linked data framework for bilingual lexicographic resources

Little-known lexicographic resources can be of tremendous value to users once digitised. By extending the digitisation efforts for a lexicographic resource, converting the human readable digital object to a state that is also machine-readable, structured data can be created that is semantically interoperable, thereby enabling the lexicographic resource to access, and be accessed by, other semantically interoperable resources. The purpose of this study is to formulate a process when converting a lexicographic resource in print form to a machine-readable bilingual lexicographic resource applying linguistic linked data principles, using the English-Xhosa Dictionary for Nurses as a case study. This is accomplished by creating a linked data framework, in which data are expressed in the form of RDF triples and URIs, in a manner which allows for extensibility to a multilingual resource. Click languages with characters not typically represented by the Roman alphabet are also considered. The purpose of this linked data framework is to define each lexical entry as “historically dynamic”, instead of “ontologically static” (Rafferty, 2016:5). For a framework which has instances in constant evolution, focus is thus given to the management of provenance and linked data generation thereof. The output is an implementation framework which provides methodological guidelines for similar language resources in the interdisciplinary field of Library and Information Science

Plan Optimization for Creating Bilingual Dictionaries of Low-Resource Languages

The constraint-based approach has been proven useful for inducing bilingual lexicons for closely-related low- resource languages. When we want to create multiple bilingual dictionaries linking several languages, we need to consider manual creation by bilingual language experts if there are no available machine-readable dictionaries are available as input. To overcome the difficulty in planning the creation of bilingual dictionaries, the consideration of various methods and costs, plan optimization is essential. We adopt the Markov Decision Process (MDP) in formalizing plan optimization for creating bilingual dictionaries; the goal is to better predict the most feasible optimal plan with the least total cost before fully implementing the constraint-based bilingual dictionary induction framework. We define heuristics based on input language characteristics to devise a baseline plan for evaluating our MDP-based approach with total cost as an evaluation metric. The MDP-based proposal outperformed heuristic planning on the total cost for all datasets examined

Knowledge Management and Cultural Heritage Repositories. Cross-Lingual Information Retrieval Strategies

In the last years important initiatives, like the development of the European Library and Europeana, aim to increase the availability of cultural content from various types of providers and institutions. The accessibility to these resources requires the development of environments which allow both to manage multilingual complexity and to preserve the semantic interoperability. The creation of Natural Language Processing (NLP) applications is finalized to the achievement of CrossLingual Information Retrieval (CLIR). This paper presents an ongoing research on language processing based on the LexiconGrammar (LG) approach with the goal of improving knowledge management in the Cultural Heritage repositories. The proposed framework aims to guarantee interoperability between multilingual systems in order to overcome crucial issues like cross-language and cross-collection retrieval. Indeed, the LG methodology tries to overcome the shortcomings of statistical approaches as in Google Translate or Bing by Microsoft concerning Multi-Word Unit (MWU) processing in queries, where the lack of linguistic context represents a serious obstacle to disambiguation. In particular, translations concerning specific domains, as it is has been widely recognized, is unambiguous since the meanings of terms are mono-referential and the type of relation that links a given term to its equivalent in a foreign language is biunivocal, i.e. a one-to-one coupling which causes this relation to be exclusive and reversible. Ontologies are used in CLIR and are considered by several scholars a promising research area to improve the effectiveness of Information Extraction (IE) techniques particularly for technical-domain queries. Therefore, we present a methodological framework which allows to map both the data and the metadata among the language-specific ont

Creating Lexical Resources in TEI P5 : a Schema for Multi-purpose Digital Dictionaries

Although most of the relevant dictionary productions of the recent past have relied on digital data and methods, there is little consensus on formats and standards. The Institute for Corpus Linguistics and Text Technology (ICLTT) of the Austrian Academy of Sciences has been conducting a number of varied lexicographic projects, both digitising print dictionaries and working on the creation of genuinely digital lexicographic data. This data was designed to serve varying purposes: machine-readability was only one. A second goal was interoperability with digital NLP tools. To achieve this end, a uniform encoding system applicable across all the projects was developed. The paper describes the constraints imposed on the content models of the various elements of the TEI dictionary module and provides arguments in favour of TEI P5 as an encoding system not only being used to represent digitised print dictionaries but also for NLP purposes

Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection

Many important forms of data are stored digitally in XML format. Errors can occur in the textual content of the data in the fields of the XML. Fixing these errors manually is time-consuming and expensive, especially for large amounts of data. There is increasing interest in the research, development, and use of automated techniques for assisting with data cleaning. Electronic dictionaries are an important form of data frequently stored in XML format that frequently have errors introduced through a mixture of manual typographical entry errors and optical character recognition errors. In this paper we describe methods for flagging statistical anomalies as likely errors in electronic dictionaries stored in XML format. We describe six systems based on different sources of information. The systems detect errors using various signals in the data including uncommon characters, text length, character-based language models, word-based language models, tied-field length ratios, and tied-field transliteration models. Four of the systems detect errors based on expectations automatically inferred from content within elements of a single field type. We call these single-field systems. Two of the systems detect errors based on correspondence expectations automatically inferred from content within elements of multiple related field types. We call these tied-field systems. For each system, we provide an intuitive analysis of the type of error that it is successful at detecting. Finally, we describe two larger-scale evaluations using crowdsourcing with Amazon's Mechanical Turk platform and using the annotations of a domain expert. The evaluations consistently show that the systems are useful for improving the efficiency with which errors in XML electronic dictionaries can be detected.Comment: 8 pages, 4 figures, 5 tables; published in Proceedings of the 2016 IEEE Tenth International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, pages 79-86, February 201

English/Arabic/English Machine Translation: A Historical Perspective

This paper examines the history and development of Machine Translation (MT) applications for the Arabic language in the context of the history and machine translation in general. It starts with a discussion of the beginnings of MT in the US and then, depending on the work of MT historians, surveys the decline of the work on MT and drying up of funding; then the revival with globalization, development of information technology and the rising needs for breaking the language barriers in the world; and last on the dramatic developments that came with the advances in computer technology. The paper also examined some of the major approaches for MT within a historical perspective. The case of Arabic is treated along the same lines focusing on the work that was done on Arabic by Western research institutes and Western profit motivated companies. Special attention is given to the work of the one Arab company, Sakr of Al-Alamiyya Group, which was established in 1982 and has seriously since then worked on developing software applications for Arabic under the umbrella of natural language processing for the Arabic language. Major available software applications for Arabic/English Arabic MT as well as MT related software were surveyed within a historical framework.Cet article examine l’histoire et l’évolution des applications de la traduction automatique (TA) en langue arabe, dans le contexte de l’histoire de la TA en général. Il commence par décrire les débuts de la TA aux États-Unis et son déclin dû à l’épuisement du financement ; ensuite, son renouveau suscité par la mondialisation, le développement des technologies de l’information et les besoins croissants de lever les barrières linguistiques. Finalement, il aborde les progrès vertigineux réalisés grâce à l’informatique. L’article étudie aussi les principales approches de la TA dans une perspective historique. Le cas de l’arabe est traité dans cette perspective, compte tenu des travaux effectués par les instituts de recherche occidentaux et quelques sociétés privées occidentales. Un accent particulier est mis sur les recherches de la société arabe Sakr, fondée dès 1982, qui a mis au point plusieurs logiciels de traitement de langues naturelles pour l’arabe. Ces divers logiciels de TA arabe-anglais-arabe ainsi que des applications associées sont présentés dans un cadre historique

Creating Lexical Resources in TEI P5

Although most of the relevant dictionary productions of the recent past have relied on digital data and methods, there is little consensus on formats and standards. The Institute for Corpus Linguistics and Text Technology (ICLTT) of the Austrian Academy of Sciences has been conducting a number of varied lexicographic projects, both digitising print dictionaries and working on the creation of genuinely digital lexicographic data. This data was designed to serve varying purposes: machine-readability was only one. A second goal was interoperability with digital NLP tools. To achieve this end, a uniform encoding system applicable across all the projects was developed. The paper describes the constraints imposed on the content models of the various elements of the TEI dictionary module and provides arguments in favour of TEI P5 as an encoding system not only being used to represent digitised print dictionaries but also for NLP purposes

Automatic Extraction of Linguistic Data from Digitized Documents

BLS 39: General Session and Special Session on Space and Directionalit