Bitext Alignment (Synthesis Lectures on Human Language by Jörg Tiedemann

By Jörg Tiedemann

This publication offers an summary of varied concepts for the alignment of bitexts. It describes normal ideas and methods that may be utilized to map corresponding components in parallel records on a variety of degrees of granularity. Bitexts are helpful linguistic assets for lots of diverse learn fields and sensible purposes. the main important program is desktop translation, specifically, statistical laptop translation. besides the fact that, there are lots of different threads that may be that may be supported through the wealthy linguistic wisdom implicitly kept in parallel assets. Bitexts were explored in lexicography, note experience disambiguation, terminology extraction, computer-aided language studying and translation reviews to call quite a few. The e-book covers the fundamental projects that experience to be conducted whilst construction parallel corpora ranging from the gathering of translated records as much as sub-sentential alignments. particularly, it describes quite a few ways to rfile alignment, sentence alignment, observe alignment and tree constitution alignment. additionally it is an inventory of assets and a finished overview of the literature on alignment recommendations. desk of Contents: creation / simple ideas and Terminology / development Parallel Corpora / Sentence Alignment / notice Alignment / word and Tree Alignment / Concluding comments

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Alignment is then established through the relations of each bitext half to the hidden interlingual structure. 5. ALIGNMENT MODELS AND SEARCH ALGORITHMS 19 Asymmetric alignment models often do not make much sense, considering the fact that the original translation direction of the bitext is usually ignored when aligning parallel data. In many cases, the source language is unknown or not even included, for example, when aligning two translations of a common source with each other. 1. 2. OTHER ALIGNMENT MODELS There are other approaches to alignment that do not rely on explicit statistical alignment models.

Term translations are taken from a probabilistic translation lexicon that can be generated from a parallel corpus using standard word alignment techniques (see Chapter 5). They refine the selection by adding the constraint that the publication date of the selected news article pairs must not differ by more than five days. • The second step is the selection of candidate sentence pairs from the selected article pairs. For this, Munteanu et al. [2004] use a simple word-overlap filter in combination with a length ratio filter.

They assume that the number of characters generated follows a pre-defined distribution, independent of type and context. 06 for French/English). They also plotted the frequencies of length differences in their aligned parallel data in order to check the density distribution, which in their case was approximately normal. 8) estimated from the same data. For simplicity, these values are fixed in the general algorithm proposed by Gale and Church [1991b]; therefore, no additional training data is required to optimize those parameters when ap- 40 4.

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