Spela Vintar
A Parallel Corpus as a Translation Aid: Exploring EU Terminology in the ELAN Slovene-English Parallel Corpus

Abstract

A Slovene-English parallel corpus of 1 million words was compiled within the framework of the EU MLIS project ELAN. It consists of several subcorpora, the largest of which contains legal and other texts pertaining to the Slovenian accession to the EU. As the corpus is among the first such resources for Slovene, the paper briefly describes the process of corpus compilation and the tools used for sentence alignment, tokenization and annotation. We then focus on possible uses of the corpus for translation purposes, especially for parallel terminology extraction. 
The concept of a parallel corpus is closely related to translation memories; the former can be converted into the latter or vice versa, depending on the method we choose for further studies of translation equivalence. In our case, a range of different tools were used to explore the possibilities of semi-automatic terminology extraction on a statistical basis. The aim is to show that translators, with some computational enthusiasm and some basic corpus-processing tools, can successfully exploit parallel corpora to create their own lexical resources. 

1 Introduction

Over the past few years Slovenia has been undergoing deep political and economic changes, causing among other things a substantial increase in interlingual contacts. As a result of the process of transition as well as the trends towards globalisation and localisation, the need for translations is growing in almost all fields. An especially large portion of translation work derives from the government and political sector as part of Slovenian preparations for the accession to the EU. These documents are mainly legislation and other texts pertaining to Slovenian aspired membership in the EU, their total amount being estimated at over 200,000 pages.
The translation of these texts is already under way, managed by the Government Office of European Affairs, together with the implementation of translation technologies and tools to facilitate and speed up this enormous task. The tools chosen were those by Trados (Translator's Workbench, MultiTerm and WinAlign), in accordance with the recommendations of the European Commission. However, due to lack of qualified staff and work overload in the translation unit of this government office, few translators are actually using translation memories and the building of TM and terminological databases is lagging behind. 
In translating legally binding texts from various fields, from agriculture to military affairs or economic agreements, terminological consistency and accuracy are of utmost importance. They can only be achieved through efficient terminology management which produces well-structured and up-to-date term banks. Although some tools for terminology management are already being used, most notably MultiTerm, the search for terms and their translation equivalents is still done manually. In the long run, the management of textual and terminological resources will have to be improved.
A parallel corpus is a useful linguistic resource for various purposes, especially for lexical and terminological studies. The ELAN Slovene-English corpus is the first step towards providing such a resource and presents a testbed for exploring possibilities of automatic or semi-automatic term extraction.

2 Making the ELAN parallel corpus

Although the compilation of the corpus served, in a broad sense, to develop multilingual technologies for the Slovene language, the primary motivation was terminological research, especially the exploration of different statistical or linguistic methods for term extraction.  The making of the corpus was guided by practical principles – as the project was quite constrained as regards duration and financing, the primary aim was to collect as much textual data as possible covering a wide range of domains. Our criteria for text selection could thus be summed up as follows:

•	terminological relevance
•	"digital" availability – both original and translation could be easiliy obtained in digital form
•	"loose copyright" – the texts either belong to public domain or the government; in some cases special copyright agreements were signed
•	length – to save on processing time, only texts over 10,000 words were included

The ELAN Slovene-English parallel corpus was compiled at the Jožef Stefan Institute in Ljubljana (Erjavec, 1999) within the framework of the EU ELAN project (European Language Activity Network). It consists of 15 original texts and their translations, together amounting to 1 million words.  The texts can be divided into the following groups:

•	legislation, texts pertaining to the Slovenian accession to the EU (33%)
•	political speeches of the Slovenian president Mr Kučan (6%)
•	Slovenian Economic Mirror – a periodical (23%) 
•	Linux user’s guide, and a glossary of computer terms (18%)
•	“1984”, novel by George Orwell (18%)
•	Lek Vademecum – catalogue of medical products (2%)

These proportions clearly show that the texts come from various fields and adhere to different text types, but – with the exception of Orwell's novel - they nevertheless share one characteristic: both the original and the translation are recent - not older than 10 years, and all texts are terminologically relevant because they contain a large percentage of domain specific lexical items. 

A further aim was to produce a basic resource that could be accessed and distributed freely on the Internet. For this purpose we prepared a special copyright agreement regulating the relationship between the text providers and ourselves that was signed if necessary.

2.1 Corpus processing and annotation

Each of the parallel texts was first converted into plain text format and "cleaned up", which also meant removing graphical elements such as pictures and tables from text, and then aligned. For the alignment two different tools were used – the Unix-based Vanilla aligner (Danielsson/Ridings) and the Windows-based aligner component of the Translation Memory System DejaVu by Atril; both require hand-validation of the results. The former was preferred by the more computationally oriented project collaborators,  whereas the latter was selected  chiefly on the grounds of its simple and flexible user interface and reasonable price and proved especially suitable for non-expert users.  
The texts were then tokenised using mtseg and converted to SGML according to the TEI guidelines. The texts were kept in 15 separate files, with the header containing all administrative data about the text, e.g. title, source, length, provider, tolls used for processing etc. The body of the text is constituted of translation units. Each translation unit contains two language segments, Slovene and English or vice versa, depending on which of the two was the source language.  
The organisation of language segments into translation units is a feature related to the notion of a translation memory and was chosen deliberately so as to facilitate the conversion of the parallel text into a TM format. Some of our text providers, especially the Government Office of European Affairs, were very eager to benefit from this kind of exchange and we indeed returned all the texts we obtained from them in the TRADOS translation memory format .tmw. 
The last step in corpus annotation was the lemmatisation of the Slovene part. For highly inflectional languages like Slovene this is a prerequisite for almost all further corpus studies and at the same time a very difficult operation due to the morphological complexity and ambiguity. The lemmatisation was kindly provided by Amebis d.o.o., a Slovene company specialised in language technologies. However, since we were dealing with terminologically rich texts, many words were unknown to the program and all ambiguities remained unresolved. 

2.2 Lexical analysis of the corpus

After we had reached the limit desired – 1 million words – and the corpus had been annotated and encoded according to our intentions and purposes, the need appeared for an analysis of the data collected. As described above, the process of corpus compilation, particularly the text selection phase, was guided by ad hoc strategies and most decisions were made on the basis of what was available at that moment rather than what would have been ideal. Before any serious exploitation of the corpus could be undertaken it was therefore necessary to analyse the material gathered.
The analysis included the following phases (Vintar, 1999):

•	general corpus statistics – number of words, sentences, average word and sentence length, type/token ratio, comparison across all 15 texts
•	word frequencies – producing frequency tables for the whole corpus as well as individual texts, producing lists of stopwords both for Slovene and English
•	keywords – lists of domain specific words for each text, obtained by matching the text frequency lists against a reference (corpus) wordlist and extracting only those items, where the relative frequency in the text is higher than the one in the corpus 
•	translation equivalents – a study of polysemous words and their various translation possibilities 
•	multi-word units – the collocational behaviour of eight highly frequent nouns was observed mainly to establish the ratio between occurrences of the word as a single-word item and occurrences of the word within a larger multi-word lexical unit

Several corpus tools were used for this task: Wordsmith, MonoConc, ParaConc and CQP. 
Through the process of analysing the corpus many interesting features were observed either due to general differences between the two languages or, more importantly, due to the lexical inventory of the texts. To exemplify the former we list a few general qunatitative differences between English and Slovene: The type/token ratio in Slovene is much higher (9.05) than English (3.1) due to morphological variety, the average length of Slovene words is greater than English, but on the other hand English sentences tend to contain more words (25.13) than Slovene (19.88). 
The lexical inventory of the texts can be represented very clearly through lists of keywords, moreover these can even be used as provisional lists of term candidates, since they contain mostly domain or field specific words that also constitute the core terminology of the text. As we inspected those lists we saw that most terms that could be of interest in translation-oriented terminographical work had indeed been identified through this simple procedure, however with the exception of hapaxes. 
For the terminological analysis presented here, a subcorpus of EU-related texts was chosen comprising the following texts:

•	<spor> Europe Agreement. Extent: 589 Kb, 34 kW 
•	<anx2> Europe Agreement - Annex II. Extent: 483 Kb, 25 kW 
•	<stra> National Strategy for Integration into EU. Extent: 1511 Kb, 89 kW 
•	<kmet> Slovenia's programme for accession to EU – agriculture. Extent: 543 Kb, 29 kW
•	<ekon> Slovenia's programme for accession to EU – economy. Extent: 394 Kb, 23 kW
•	<usta> Constitution of the Republic of Slovenia. Extent: 364 Kb, 20 kW
•	<vino> EC Council Regulation No 3290/94 – agriculture. Extent: 1182 Kb, 69 kW 

3 Towards a methodology for bilingual term extraction

The lists of keywords were therefore taken as a starting point. If the keyword list for each individual text is combined with the list of words unknown to the lemmatizer (see Figure 1) a provisional list of (monolingual)  term candidates is obtained. The next step required is the word-to-word alignment of parallel texts to extract translation equivalents, and for this we used the 21 software, a freely available statistical word alignment tool produced at the University of Twente (Hiemstra, 1998). However, if Twente is run on Slovene-English texts as they are its output will be next to useless, so prior to the word alignment some pre-processing steps were necessary. 

Figure 1: Words unknown to the lemmatizer

The first was removing stopwords from the corpus, i.e. functional words that tend to be highly frequent but carry little meaning. Lists of stopwords were obtained mostly on the basis of the general frequency list and supplemented according to grammatical rules. The second step was the replacement of wordforms by lemmas for the purpose of increasing their relative frequency in the corpus; this was performed only for the Slovene part.
Given such normalised input Twente produces much better results, however, with still far too much noise (see Figure 2). An analysis revealed that the results are only reliable for those items that occur more than 4 times in the corpus and are at the same time matched together as translation equivalents with a probability of over 0.50. We therefore implement a Perl filter over the initial term list that extracted only the word pairs matching these criteria. The result is a bilingual text-specific glossary of single-word terms (see Figure 3). 

Figure 2: Twente word alignment output

The majority of translation equivalents obtained through this multi-level filtering are correct  – the precision was estimated to 98 %. Taking a closer look at our sample text gives the following figures:
Sample text: National programme of the Republic of Slovenia for accession to the European Union – agriculture
Total words (Slovene): 13,383
Words after pre-processing: 10,902
Types: 1,837
Terms extracted: 304
Precision: 98%

Freq.	Slovene	English	Match	
45	agencija	Agency	0.58	
9	agraren	agrarian	0.78	
9	akt	regulations	0.79	
18	AKTRP	AAMRD	0.84	
5	analiza	analysis	0.69	
5	C	C	1.00	
6	carinski	Tariff	0.83	
8	celovit	integrated	1.00	
5	cena	prices	0.96	
15	center	Centre	0.57	
10	časoven	Timescale	0.70	
26	članica	Member	0.94	
8	človek	persons	0.63	
8	človeški	Staff	1.00	
14	dejavnost	activities	0.80	

Figure 3: Sample from the bilingual term glossary

4 Limitations, problems and possible solutions

Although this method allows us to automatically extract bilingual text specific terminological glossaries with a releatively high precision, it has, on the other hand, some severe limitations. First of all, not all extracted word pairs are terms, because neither the keyword list nor the list of unknown words can really guarantee that a text specific item is actually a term. Secondly, the accuracy of this method depends on the frequency of the item extracted in the corpus and works reliably only with words that occur over four times. For less frequent words and hapaxes alternative extraction methods should be devised. On the other hand, many terms may actually occur over four times, but if the lemmatizer does not know the word it is retained in its various wordforms and counts as several different types. Consequently, such items cannot be extracted. 

Probably the greatest limitation of the methodology described is the fact that only single-word items are extracted. Although many terms consist of a single word, an even greater proportion of terms are composed of two or more words. By observing and extracting just single-word items we thus lose a significant part of the terminological and idiomatic inventory of the text. A brief (manual) analysis of 8 highly frequent words showed that in over 40 per cent of the cases the word appears as part of a larger – idiomatic – unit, i.e. the unit itself occurs more than once in the entire corpus. We can therefore conclude that such a bilingual glossary of terms can probably serve mainly as a basis for multi-word term extraction or as resource for bilingual corpus querying, while its use for translation purposes remains rather limited. 

4.1 Improving the results

The results presented above could be improved in various ways, as none of the steps in the methodology proposed had really been developed to its optimum. In the corpus pre-processing phase, much could be gained by lemmatizing the English part as well and by improving the stoplists. This would render a "cleaner" corpus and would probably result in higher precision of the Twente raw output. 

Another possibility to considerably improve the word-to-word alignment procedure would be the identification of cognates. Especially in legal texts, and more generally in the majority of terminologically rich texts, we encounter many internationalisms and words derived from a common (Latin or Greek) stem, so the implementation of a "fuzzy matching" module that would recognize such pairs could improve the precision and recall of the Twente software, apart from being a valuable aid in the sentence alignment process.

4.2 Identifying multi-word terms

For the identifiication of multi-word units several methods could be used. The first would be the identification of typical part-of-speech patterns of multi-word terms in Slovene and English and establishing statistically significant correspondences (Heid, 1999). For the legal domain, an existing database of legal terms containing over 10.000 entries is maintained at the Office of the Government of Slovenia for European Affairs, and it might serve as a basis for such an analysis. If we then retrieve such typical patterns and check for single-word terms, we might arrive at multi-word term units. 

Another approach was taken by Dias et al (1999), where the identification of multi-word units is based solely on the statistical evaluation of relations between words in the corpus. Some preliminary tests have already been run also on the ELAN corpus which showed that purely statistical methods sometimes give very accurate results in detecting relevant collocations even in unprocessed (non-lemmatised) texts.

5 Conclusions

Terminology, sometimes regarded as a branch of applied science, but actually having a scope much wider that this, is a field developing at a pace that – at least theoretically – equals that of the most rapid developments in technology, society and science. It is therefore next to impossible that a person – say a terminologist – could effectively keep track of all these changes and developments, much less record and document them adequately, solely on the basis of "manual" observation and intuition. What is needed is a methodology (and technology) for efficient processing of large quantities of textual material, because these not only reflect the state-of-the-art within a certain domain but constitute it.

For this reason and the fact that terminology is nowadays no longer coined only by terminologists and standardising committees but increasingly by translators themselves, it is essential that technical, legal and/or scientific translators and interpreters understand their role and responsibility as term creators and therefore learn to use the appropriate tools that will facilitate this task. Extracting terms, relevant collocations, variants and contexts from parallel corpora is certainly an important step towards this goal, and building terminological databases on the basis of such resources is the next step towards which the methodology proposed here wishes to contribute.

The aim of this paper was to show firstly how an important resource for translators, interpreters and temrinologists can be created and processed and, secondly, how significant terminological data can be automatically extracted, interpreted and used. Although the procedure described here is very basic compared to some existing methods of term identification and retrieval, it shows that a combination of available corpus processing tools and some self-devised filters can produce useful results. A bilingual text specific glossary of terms may not be highly relevant as a translator's resource, but it is certainly useful for further identification of terms, translation equivalents and translation-oriented text mining. 

Apart from the ways of overcoming limitations described above, several other plans are included in our wishlist for the future. From the perspective of those involved in the training of translators and interpreters, more attention should be paid to developing the skills needed to exploit corpora for translation purposes in creative and innovative ways. This also means developing a methodology of how to teach corpus linguistics, how to encourage students to view language as a set of computer-analysable patterns and how to use the knowledge of such patterns in translation. 

Another area of future development is the provision of appropriate training for active translators and terminologists, who can also be included in the process of building multilingual databases and corpora. Although the present situation in translation technologies and especially terminography leaves a lot to hope for, things are beginning to change both on the technological and the "awareness" side. We hope that this process will eventually result in better translations and happier translators.

6 References

Dias, Gael et al (1999) Multilingual Aspects of Multiword Lexical Units. In: Proceedings of the workshop Language Technologies – Multilingual Aspects. Ljubljana: Faculty of Arts.
Erjavec, Tomaž (1999) A TEI Encoding of Aligned Corpora as Translation Memories. In: Proceedings of the EACL Workshop on Linguistically Interpreted Corpora. Bergen: ACL.
Heid, Ulrich (1999) Extracting Terminologically Relevan Collocations from German Technical Texts. In: Sandrini, Peter (ed.) Terminology and Knowledge Engineering (TKE '99). Vienna: TermNet.
Hiemstra, Djoerd (1998) Multilingual Domain Modelling in Twenty-One: automatic creation of a bi-directional translation lexicon from a parallel corpus. In: Peter-Arno Coppen et al (eds.) Proceedings of the eighth CLIN meeting, pp. 41-58.  
Vintar, Spela (1999) A Lexical Analysis of the IJS-ELAN Slovene-English Parallel Corpus. In: Proceedings of the workshop Language Technologies – Multilingual Aspects. Ljubljana: Faculty of Arts.