Named Entity Recognition is an essential task in natural language processing to detect entities and classify them into predetermined categories. An entity is a meaningful word, or phrase that refers to proper nouns. Named Entities play an important role in different NLP tasks such as Information Extraction, Question Answering and Machine Translation. In Machine Translation, named entities often cause translation failures regardless of local context, affecting the output quality of translation. Annotating named entities is a time-consuming and expensive process especially for low-resource languages. One solution for this problem is to use word alignment methods in bilingual parallel corpora in which just one side has been annotated. The goal is to extract named entities in the target language by using the annotated corpus of the source language. In this paper, we compare the performance of two alignment methods, Grow-diag-final-and and Intersect Symmetrisation heuristics, to exploit the annotation projection of English-Brazilian Portuguese bilingual corpus to detect named entities in Brazilian Portuguese. A NER model that is trained on annotated data extracted from the alignment methods, is used to evaluate the performance of aligners. Experimental results show the Intersect Symmetrisation is able to achieve superior performance scores compared to the Grow-diag-final-and heuristic in Brazilian Portuguese.

The inclusion of documentation as a core subject in the curriculum of Translation and Interpretation degrees clearly underlines its importance to translators. Training in this discipline is considered essential for a translator given that only sufficient and conscientious work on documentation will allow an adequate translation of a specialised text. The sources of information that may be utilised by the translator are extremely varied, ranging from an oral consultation with an expert to a search using specialised glossaries and dictionaries. However, in the field of translation perhaps the most relevant documentation activity today involves the use of the Internet and, closely related to this, the compilation and management of virtual corpora. In this chapter, we present a systematic methodology for corpus compilation based on electronic resources available on the Internet. The methodology is illustrated through the creation of a virtual corpus of travel insurance in English and Spanish, whose representativeness is subsequently determined by using a computer programme-called ReCor specifically designed for this purpose. Finally, some specific examples of possible uses in direct and inverse translations of this type of document are given.

Language varieties should be taken into account in order to enhance fluency and naturalness of translated texts. In this paper we will examine the collocational verbal range for prima-facie translation equivalents of words likedecisionanddilemma, which in both languages denote the act or process of reaching a resolution after consideration, resolving a question or deciding something. We will be mainly concerned with diatopic variation in Spanish. To this end, we set out to develop a giga-token corpus-based protocol which includes a detailed and reproducible methodology sufficient to detect collocational peculiarities of transnational languages. To our knowledge, this is one of the first observational studies of this kind. The paper is organised as follows. Section 1 introduces some basic issues about the translation of collocations against the background of languages’ anisomorphism. Section 2 provides a feature characterisation of collocations. Section 3 deals with the choice of corpora, corpus tools, nodes and patterns. Section 4 covers the automatic retrieval of the selected verb + noun (object) collocations in general Spanish and the co-existing national varieties. Special attention is paid to comparative results in terms of similarities and mismatches. Section 5 presents conclusions and outlines avenues of further research.