By Jörg Tiedemann
This ebook presents an outline of varied thoughts for the alignment of bitexts. It describes common ideas and methods that may be utilized to map corresponding components in parallel files on a number of degrees of granularity. Bitexts are important linguistic assets for plenty of diverse learn fields and functional purposes. the main principal program is computing device translation, particularly, statistical laptop translation. in spite of the fact that, there are many different threads that may be that may be supported by means of the wealthy linguistic wisdom implicitly kept in parallel assets. Bitexts were explored in lexicography, notice experience disambiguation, terminology extraction, computer-aided language studying and translation reports to call quite a few. The booklet covers the basic projects that experience to be conducted whilst development parallel corpora ranging from the gathering of translated files as much as sub-sentential alignments. particularly, it describes numerous ways to record alignment, sentence alignment, note alignment and tree constitution alignment. additionally it is an inventory of assets and a finished evaluation of the literature on alignment suggestions. desk of Contents: advent / easy strategies and Terminology / development Parallel Corpora / Sentence Alignment / observe Alignment / word and Tree Alignment / Concluding comments
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Extra info for Bitext Alignment (Synthesis Lectures on Human Language Technologies)
Several strategies can be used to create bitexts. Parallel documents can be downloaded directly from the sources that are known to contain such resources. This makes it possible to control the contents of the extracted material. Another strategy is to use web crawling techniques to fetch parallel 28 3. BUILDING PARALLEL CORPORA websites and other parallel documents automatically from a wide variety of locations. However, this may lead to much nosier data sets which require substantial cleaning efforts.
For example, Nie and Cai  apply automatic sentence alignment to filter multilingual text collections. A large proportion of empty alignments indicate an unlikely candidate in their system. Content-based algorithms are used by Ma and Liberman . They propose the use of lexical matchings to compute the similarity between texts. For this, they apply string similarity measures to identify cognates and suggest the use of bilingual dictionaries to extend the coverage. Resnik and Smith  also explore the use of lexical matchings applying hand-crafted and automatically generated probabilistic translation lexicons.
1: Sentence alignment types and their relative frequencies found by Gale and Church [1991b]. 011 substitution insertion or deletion expansion or contraction swap or merge Note that these alignment types intuitively fit quite well to editing operations as indicated in the last column of the table. Using these results, Gale and Church [1991b] added these estimated 42 4. 4: The probability density function of a standard normal distribution. 485.