Exploiting parallel texts in the creation of multilingual semantically annotated resources: the MultiSemCor Corpus (original) (raw)
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Evaluating cross-language annotation transfer in the multisemcor corpus
… of the 20th international conference on …, 2004
In this paper we illustrate and evaluate an approach to the creation of high quality linguistically annotated resources based on the exploitation of aligned parallel corpora. This approach is based on the assumption that if a text in one language has been annotated and its translation has not, annotations can be transferred from the source text to the target using word alignment as a bridge. The transfer approach has been tested in the creation of the MultiSemCor corpus, an English/Italian parallel corpus created on the basis of the English SemCor corpus. In MultiSemCor texts are aligned at the word level and semantically annotated with a shared inventory of senses. We present some experiments carried out to evaluate the different steps involved in the methodology. The results of the evaluation suggest that the cross-language annotation transfer methodology is a promising solution allowing for the exploitation of existing (mostly English) annotated resources to bootstrap the creation of annotated corpora in new (resourcepoor) languages with greatly reduced human effort.
2007
Multilingual technologies, which to a large extent are language independent, provide a powerful support for easier building of annotated linguistic resources for languages where such resources are scarce or missing. All these technologies require parallel corpora in order to achieve their ends. Parallel texts encode extremely valuable linguistic knowledge because the linguistic decisions made by the human translators in order to faithfully convey the meaning of the source text can be traced and used as evidence on linguistic facts which, in a monolingual context, might be unavailable to or overlooked by a computer program. In this paper we will briefly present some underlying multilingual technologies and methodologies we developed for exploiting parallel corpora and we will discuss their relevance for cross-linguistic annotation transfer and applications.
Interlingual annotation of parallel text corpora: a new framework for annotation and evaluation
Natural Language …, 2010
This paper focuses on an important step in the creation of a system of meaning representation and the development of semantically annotated parallel corpora, for use in applications such as machine translation, question answering, text summarization, and information retrieval. The work described below constitutes the first effort of any kind to annotate multiple translations of foreign-language texts with interlingual content. Three levels of representation are introduced: deep syntactic dependencies (IL0), intermediate semantic representations (IL1), and a normalized representation that unifies conversives, nonliteral language, and paraphrase (IL2). The resulting annotated, multilingually induced, parallel corpora will be useful as an empirical basis for a wide range of research, including the development and evaluation of interlingual NLP systems and paraphrase-extraction systems as well as a host of other research and development efforts in theoretical and applied linguistics, foreign language pedagogy, translation studies, and other related disciplines.
AlphaMWE: Construction of Multilingual Parallel Corpora with MWE Annotations
MWE-LEX, 2020
In this work, we present the construction of multilingual parallel corpora with annotation of multiword expressions (MWEs). MWEs include verbal MWEs (vMWEs) defined in the PARSEME shared task that have a verb as the head of the studied terms. The annotated vMWEs are also bilingually and multilingually aligned manually. The languages covered include English, Chinese, Polish, and German. Our original English corpus is taken from the PARSEME shared task in 2018. We performed machine translation of this source corpus followed by human post editing and annotation of target MWEs. Strict quality control was applied for error limitation, i.e., each MT output sentence received first manual post editing and annotation plus second manual quality rechecking. One of our findings during corpora preparation is that accurate translation of MWEs presents challenges to MT systems. To facilitate further MT research, we present a categorisation of the error types encountered by MT systems in performing MWE related translation. To acquire a broader view of MT issues, we selected four popular state-of-the-art MT models for comparisons namely: Microsoft Bing Translator, GoogleMT, Baidu Fanyi and DeepL MT. Because of the noise removal, translation post editing and MWE annotation by human professionals, we believe our AlphaMWE dataset will be an asset for cross-lingual and multilingual research, such as MT and information extraction. Our multilingual corpora are available as open access at github.com/poethan/AlphaMWE
Towards a Multilingual Aligned Parallel Corpus
—Nowadays, there are a large number of satisfying studies on monolingual corpora and the amount of its available data grew significantly over the last years. Unfortunately, not all types of corpora have benefited equally from this growth. An example of such corpora is the multilingual aligned parall el corpus, where there are just a few cases in the cross-language research area. Thus, the goal behind this work is to produce a new aligned multilingual parallel corpus and increase the amount of work in being carried out on the building of such corpora. In this paper, we highlight ongoing work of creating a multilingual aligned parallel corpus of subtitles from TEDx Talks events. The corpus currently contains roughly 6,000 multilingual of aligned subtitles covering 200 video talks in different languages (Arabic, English, French, S panish, Italian, etc) and it covers a variety of topics such as Business, Education, En vironment, etc. Our corpus is divided into two sub corpora. The first one contains about 200 files for each 15 languages and the second one is available in 30 languages with an average size of roughly 100 files per language.
Parallel Corpora for WordNet Construction: Machine Translation vs. Automatic Sense Tagging
In this paper we present a methodology for WordNet construction based on the exploitation of parallel corpora with semantic annotation of the English source text. We are using this methodology for the enlargement of the Spanish and Catalan versions of WordNet 3.0, but the methodology can also be used for other languages. As big parallel corpora with semantic annotation are not usually available, we explore two strategies to overcome this problem: to use monolingual sense tagged corpora and machine translation, on the one hand; and to use parallel corpora and automatic sense tagging on the source text, on the other. With these resources, the problem of acquiring a WordNet from parallel corpora can be seen as a word alignment task. Fortunately, this task is well known, and some aligning algorithms are freely available.
1. The LLI-UAM Multilingual Parallel Corpus: A New Resource *
2008
This paper presents the results (1st phase) of the on-going research in the Computational Linguistics Laboratory at Autónoma University of Madrid (LLI-UAM) aiming at the development of a multi-lingual parallel corpus (Arabic-Spanish-English) aligned on the sentence level and tagged on the POS level. A multilingual parallel corpus which brings together Arabic, Spanish and English is a new resource for the NLP community that completes the present panorama of parallel corpora. In the first part of this study, we introduce the novelty of our approach and the challenges encountered to create such a corpus. This introductory part highlights the main features of the corpus and the criteria applied during the selection process. The second part focuses on two main stages: basic processing (tokenization and segmentation) and alignment. Methodology of alignment is explained in detail and results obtained in the three different linguistic pairs are compared. POS tagging and tools used in this s...
QTLeap WSD/NED Corpora: Semantic Annotation of Parallel Corpora in Six Languages
2016
This work presents parallel corpora automatically annotated with several NLP tools, including lemma and part-of-speech tagging, named-entity recognition and classification, named-entity disambiguation, word-sense disambiguation, and coreference. The corpora comprise both the well-known Europarl corpus and a domain-specific question-answer troubleshooting corpus on the IT domain. English is common in all parallel corpora, with translations in five languages, namely, Basque, Bulgarian, Czech, Portuguese and Spanish. We describe the annotated corpora and the tools used for annotation, as well as annotation statistics for each language. These new resources are freely available and will help research on semantic processing for machine translation and cross-lingual transfer.
Building The Sense-Tagged Multilingual Parallel Corpus
Sense-annotated parallel corpora play a crucial role in natural language processing. This paper introduces our progress in creating such a corpus for Asian languages using English as a pivot, which is the first such corpus for these languages (Chinese, Japanese and Indonesian). Two sets of tools have been developed for sequential and targeted tagging, which are also easy to be set up for any new languages. This paper also briefly presents the general guidelines for doing this project. The current results of the monolingual sensetagging and multilingual linking are illustrated, which indicate the differences among genres and language pairs. All the tools, guidelines and the manually annotated corpus will be freely available at http://compling.ntu.edu.sg/ntumc.