Proceedings of the Conference on Natural Language Processing 2010

This book contains state-of-the-art contributions to the 10th conference on Natural Language Processing, KONVENS 2010 (Konferenz zur Verarbeitung natürlicher Sprache), with a focus on semantic processing. The KONVENS in general aims at offering a broad perspective on current research and developments within the interdisciplinary field of natural language processing. The central theme draws specific attention towards addressing linguistic aspects ofmeaning, covering deep as well as shallow approaches to semantic processing. The contributions address both knowledgebased and data-driven methods for modelling and acquiring semantic information, and discuss the role of semantic information in applications of language technology. The articles demonstrate the importance of semantic processing, and present novel and creative approaches to natural language processing in general. Some contributions put their focus on developing and improving NLP systems for tasks like Named Entity Recognition or Word Sense Disambiguation, or focus on semantic knowledge acquisition and exploitation with respect to collaboratively built ressources, or harvesting semantic information in virtual games. Others are set within the context of real-world applications, such as Authoring Aids, Text Summarisation and Information Retrieval. The collection highlights the importance of semantic processing for different areas and applications in Natural Language Processing, and provides the reader with an overview of current research in this field

Mono- and cross-lingual paraphrased text reuse and extrinsic plagiarism detection

Text reuse is the act of borrowing text (either verbatim or paraphrased) from an earlier written text. It could occur within the same language (mono-lingual) or across languages (cross-lingual) where the reused text is in a different language than the original text. Text reuse and its related problem, plagiarism (the unacknowledged reuse of text), are becoming serious issues in many fields and research shows that paraphrased and especially the cross-lingual cases of reuse are much harder to detect. Moreover, the recent rise in readily available multi-lingual content on the Web and social media has increased the problem to an unprecedented scale. To develop, compare, and evaluate automatic methods for mono- and crosslingual text reuse and extrinsic (finding portion(s) of text that is reused from the original text) plagiarism detection, standard evaluation resources are of utmost importance. However, previous efforts on developing such resources have mostly focused on English and some other languages. On the other hand, the Urdu language, which is widely spoken and has a large digital footprint, lacks resources in terms of core language processing tools and corpora. With this consideration in mind, this PhD research focuses on developing standard evaluation corpora, methods, and supporting resources to automatically detect mono-lingual (Urdu) and cross-lingual (English-Urdu) cases of text reuse and extrinsic plagiarism This thesis contributes a mono-lingual (Urdu) text reuse corpus (COUNTER Corpus) that contains real cases of Urdu text reuse at document-level. Another contribution is the development of a mono-lingual (Urdu) extrinsic plagiarism corpus (UPPC Corpus) that contains simulated cases of Urdu paraphrase plagiarism. Evaluation results, by applying a wide range of state-of-the-art mono-lingual methods on both corpora, shows that it is easier to detect verbatim cases than paraphrased ones. Moreover, the performance of these methods decreases considerably on real cases of reuse. A couple of supporting resources are also created to assist methods used in the cross-lingual (English-Urdu) text reuse detection. A large-scale multi-domain English-Urdu parallel corpus (EUPC-20) that contains parallel sentences is mined from the Web and several bi-lingual (English-Urdu) dictionaries are compiled using multiple approaches from different sources. Another major contribution of this study is the development of a large benchmark cross-lingual (English-Urdu) text reuse corpus (TREU Corpus). It contains English to Urdu real cases of text reuse at the document-level. A diversified range of methods are applied on the TREU Corpus to evaluate its usefulness and to show how it can be utilised in the development of automatic methods for measuring cross-lingual (English-Urdu) text reuse. A new cross-lingual method is also proposed that uses bilingual word embeddings to estimate the degree of overlap amongst text documents by computing the maximum weighted cosine similarity between word pairs. The overall low evaluation results indicate that it is a challenging task to detect crosslingual real cases of text reuse, especially when the language pairs have unrelated scripts, i.e., English-Urdu. However, an improvement in the result is observed using a combination of methods used in the experiments. The research work undertaken in this PhD thesis contributes corpora, methods, and supporting resources for the mono- and cross-lingual text reuse and extrinsic plagiarism for a significantly under-resourced Urdu and English-Urdu language pair. It highlights that paraphrased and cross-lingual cross-script real cases of text reuse are harder to detect and are still an open issue. Moreover, it emphasises the need to develop standard evaluation and supporting resources for under-resourced languages to facilitate research in these languages. The resources that have been developed and methods proposed could serve as a framework for future research in other languages and language pairs

The TXM Portal Software giving access to Old French Manuscripts Online

Texte intégral en ligne : http://www.lrec-conf.org/proceedings/lrec2012/workshops/13.ProceedingsCultHeritage.pdfInternational audiencehttp://www.lrec-conf.org/proceedings/lrec2012/workshops/13.ProceedingsCultHeritage.pdf This paper presents the new TXM software platform giving online access to Old French Text Manuscripts images and tagged transcriptions for concordancing and text mining. This platform is able to import medieval sources encoded in XML according to the TEI Guidelines for linking manuscript images to transcriptions, encode several diplomatic levels of transcription including abbreviations and word level corrections. It includes a sophisticated tokenizer able to deal with TEI tags at different levels of linguistic hierarchy. Words are tagged on the fly during the import process using IMS TreeTagger tool with a specific language model. Synoptic editions displaying side by side manuscript images and text transcriptions are automatically produced during the import process. Texts are organized in a corpus with their own metadata (title, author, date, genre, etc.) and several word properties indexes are produced for the CQP search engine to allow efficient word patterns search to build different type of frequency lists or concordances. For syntactically annotated texts, special indexes are produced for the Tiger Search engine to allow efficient syntactic concordances building. The platform has also been tested on classical Latin, ancient Greek, Old Slavonic and Old Hieroglyphic Egyptian corpora (including various types of encoding and annotations)

Supralingualism and the Translatability Industry

2019, peer reviewed. “Supralingualism and the Translatability Industry.” Applied Linguistics, special issue on Translating Culture, edited by Zhu Hua and Claire Kramsch, 1–20