Creating and validating multilingual semantic representations for six languages:expert versus non-expert crowds

Creating high-quality wide-coverage multilingual semantic lexicons to support knowledge-based approaches is a challenging time-consuming manual task. This has traditionally been performed by linguistic experts: a slow and expensive process. We present an experiment in which we adapt and evaluate crowdsourcing methods employing native speakers to generate a list of coarse-grained senses under a common multilingual semantic taxonomy for sets of words in six languages. 451 non-experts (including 427 Mechanical Turk workers) and 15 expert participants semantically annotated 250 words manually for Arabic, Chinese, English, Italian, Portuguese and Urdu lexicons. In order to avoid erroneous (spam) crowdsourced results, we used a novel taskspecific two-phase filtering process where users were asked to identify synonyms in the target language, and remove erroneous senses

Creating and validating multilingual semantic representations for six languages:expert versus non-expert crowds

Creating high-quality wide-coverage multilingual semantic lexicons to support knowledge-based approaches is a challenging time-consuming manual task. This has traditionally been performed by linguistic experts: a slow and expensive process. We present an experiment in which we adapt and evaluate crowdsourcing methods employing native speakers to generate a list of coarse-grained senses under a common multilingual semantic taxonomy for sets of words in six languages. 451 non-experts (including 427 Mechanical Turk workers) and 15 expert participants semantically annotated 250 words manually for Arabic, Chinese, English, Italian, Portuguese and Urdu lexicons. In order to avoid erroneous (spam) crowdsourced results, we used a novel taskspecific two-phase filtering process where users were asked to identify synonyms in the target language, and remove erroneous senses

Proceedings

Proceedings of the Workshop on Annotation and Exploitation of Parallel Corpora AEPC 2010. Editors: Lars Ahrenberg, Jörg Tiedemann and Martin Volk. NEALT Proceedings Series, Vol. 10 (2010), 98 pages. © 2010 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/10062/15893

Towards error annotation in a learner corpus of Portuguese

In this article, we present COPLE2, a new corpus of Portuguese that encompasses written and spoken data produced by foreign learners of Portuguese as a foreign or second language (FL/L2). Following the trend towards learner corpus research applied to less commonly taught languages, it is our aim to enhance the learning data of Portuguese L2. These data may be useful not only for educational purposes (design of learning materials, curricula, etc.) but also for the development of NLP tools to support students in their learning process. The corpus is available online using TEITOK environment, a web-based framework for corpus treatment that provides several built-in NLP tools and a rich set of functionalities (multiple orthographic transcription layers, lemmatization and POS, normalization of the tokens, error annotation) to automatically process and annotate texts in xml format. A CQP-based search interface allows searching the corpus for different fields, such as words, lemmas, POS tags or error tags. We will describe the work in progress regarding the constitution and linguistic annotation of this corpus, particularly focusing on error annotation.info:eu-repo/semantics/publishedVersio

Compiling and annotating a learner corpus for a morphologically rich language: CzeSL, a corpus of non-native Czech

Learner corpora, linguistic collections documenting a language as used by learners, provide an important empirical foundation for language acquisition research and teaching practice. This book presents CzeSL, a corpus of non-native Czech, against the background of theoretical and practical issues in the current learner corpus research. Languages with rich morphology and relatively free word order, including Czech, are particularly challenging for the analysis of learner language. The authors address both the complexity of learner error annotation, describing three complementary annotation schemes, and the complexity of description of non-native Czech in terms of standard linguistic categories. The book discusses in detail practical aspects of the corpus creation: the process of collection and annotation itself, the supporting tools, the resulting data, their formats and search platforms. The chapter on use cases exemplifies the usefulness of learner corpora for teaching, language acquisition research, and computational linguistics. Any researcher developing learner corpora will surely appreciate the concluding chapter listing lessons learned and pitfalls to avoid