Automatic Text Aligning in a Parallel Text Corpus

Parallel text corpora supply researchers with very important data for multilingual lexicography and translation studies as well as for language typology. The crucial problem in compiling parallel corpora is aligning the texts. Manual aligning is impossible for large corpora, so ways of automatic aligning are to be found. The aim of the research project at the University of Tampere is to compile a Russian-Finnish parallel corpus and to develop the software for automatic aligning of the Russian and Finnish subcorpora.

1. General

Since the 60s and 70s text corpora development has become much easier. Electronic texts in a large variety of languages can be obtained on the Internet; scanning and OCR technologies have been much improved during the last ten years. Associations like TELRI and ELRA are helping linguists from different countries to join their efforts in collecting language resources in electronic form. The number of corpus-based projects is rapidly growing while the number of scholars that are skeptical about this innovation is reducing at the same speed. In most lexicographic projects text corpora are being used. Applied linguistic research is another field where text corpora are welcome as an inexhaustible source of empirical information, polygon for testing various linguistic tools - spell-checkers, OCRs, machine translation systems, NLP systems etc. At the same time, the corpora are quite useful for theoretical, 'armchair linguistics' [Fillmore, 1992] as well. Nowadays text corpora are quite widely used for compiling monolingual dictionaries. Nevertheless it is still a problem to use text corpora in bilingual lexicography. Of course it is possible to use two text corpora but it would have been more useful to have parallel texts and tools for looking up words and their translations as well as parallel contexts. Furthermore, the use of bilingual and multilingual text corpora is by no means limited to multilingual lexicography.

2. The Project

The aim of the research project running at the Department of Translation Studies of the University of Tampere is to collect a bilingual corpus of parallel texts (Russian and Finnish). The texts will be Russian classical or fiction texts and their translations into Finnish. The corpus will not be very big (4-5 million running words) but it will be equipped with efficient search tools for analysis of parallel texts. At present we have a substantial corpus of Russian prose (4.5 million words) and have started to collect the translations of Russian texts into Finnish and to modify the software for running the parallel text corpus. We have equipped the above mentioned text corpus of Russian prose with certain tools for building word lists and concordances. The present task is to collect Russian fiction texts and their translations into Finnish. As a result we shall have authentic Russian texts (normal Russian language) and Finnish texts influenced by the Russian original. It is quite evident that the language of translations is different from the original prose: the translator is under the influence of the language from which he/she is translating (that is why when I was a student our professors told us we should not use examples from translations for our research). Grammar forms, syntactic patterns, word frequencies in the Russian subcorpus will be more or less representative for the standard Russian language. This will not be so for the Finnish subcorpus. Grammar, sentence structure, and vocabulary of the translations are influenced by the original text. This means that the Corpus will be 'asymmetrical', centered on the Russian language.

3. Maintenance of the Corpus

The basic idea is to separate the texts from the tags. Usually the corpus software is 'anti-intellectual' - all those programs can do is to find strings of characters, show them or perform calculations on them. The corpora developers therefore have to make explicit all relevant information, i.e. to tag the texts. In our Corpus the texts are 'clean'. They are stored as ordinary text files. All relevant information is registered in the Microsoft Access database. The database is used for data processing as well. The user can get concordances for specified word(s) or word combination(s). He/she can also use the word list for query-making. It is quite easy to specify context size (in sentences) and comparison for the main and second search key (whole word / start of word / end of word / any part of word) as well as the second search key position (same sentence / next word). The approach for corpus compiling we use has many reserves - we are planning to add to the program lemmatizing routines which will make it possible to build another index - a grammatical one. This will make searching for grammar forms also possible.

4. Parallel Concordancing

However, the most difficult and most interesting part of the project will be to find out whether automated parallel concordancing is possible. The starting point was the idea that although the translator changes a lot in the translation in comparison to the original text - he may join or split the sentences, change clauses into phrases, omit or add certain words, use broader or narrower equivalents - still he translates something literally. Certain words cannot be skipped in the translation; otherwise we shall have an entirely new text. The words that in most cases are translated literally we shall call keywords. So, we presume that if equivalents for more than half of keywords of extract A from the original were found in extract B of the same size from the translation, extract B is likely to be the translation of extract A. What word classes shall be keywords? Of course we have to exclude prepositions, conjunctions, pronouns, etc. We also have to exclude words with very broad meanings (e.g. idti - 'to go'). Some words are parts of idioms and therefore unpredictable in translations (bog - 'god', tchert - 'devil'). From what is left we also have to exclude words having high-frequency homonyms. E.g. we cannot include the Russian word 'petchen' - 'liver' - in the Russian-Finnish glossary of keywords because the Finnish equivalent for this word is 'maksa' which in many forms is homonymous with the verb 'maksaa' - 'to pay'. Another criterion is word frequency. Frequently used words may cause problems because they are everywhere. Words that occur only once also have to be excluded. Most useful for our research are words that have a frequency in the range of 2 to 6 occurrences. This is about 35% of the words in the analyzed text (Dostoyevski, Notes from the cellar). Most of these words and some of the more frequently used words will be included in the list of keywords. Together with Finnish dictionary equivalents they form the core of the system. The system works as follows:

1) Extract A from the original is split into words.
2) Keywords are selected; weight of the sample is calculated.
3) Finnish equivalents B1, B2, ... Bn for the keywords are looked up.
4) Contexts for every keyword are looked up and checked by other keywords.

For each context the weight is calculated. If the weight of the context Bx is more than 60% of the weight of the extract A, Bx is considered a translation of A and presented to the user. If our hypothesis is true, the program will be able to find parallel places if a) the context is long enough (we cannot say at present what 'long enough' means); b) enough keywords were found; c) the translation is close enough to the original.

5. Applications of the Parallel Text Corpus

The parallel text corpus would be very useful in the fields of comparative studies, translation studies, and bilingual lexicography. It would make it possible to find how the word is actually translated, which is sometimes quite different from what is expected according to the dictionaries. It will be easy to find translations of quotations. It would also be quite possible to monitor usage of certain grammatical forms or constructions and ways of translating them into another language.

1. General

2. The Project

3. Maintenance of the Corpus

4. Parallel Concordancing

5. Applications of the Parallel Text Corpus

References