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The availability of large multi-parallel corpora offers an enormous wealth of material to contrastive corpus linguists, translators and language learners, if we can exploit the data properly. Necessary preparation steps include sentence and word alignment across multiple languages. Additionally, linguistic annotation such as partof- speech tagging, lemmatisation, chunking, and dependency parsing facilitate precise querying of linguistic properties and can be used to extend word alignment to sub-sentential groups. Such highly interconnected data is stored in a relational database to allow for efficient retrieval and linguistic data mining, which may include the statistics-based selection of good example sentences. The varying information needs of contrastive linguists require a flexible linguistic query language for ad hoc searches. Such queries in the format of generalised treebank query languages will be automatically translated into SQL queries.
We discovered several recurring errors in the current version of the Europarl Corpus originating both from the web site of the European Parliament and the corpus compilation based thereon. The most frequent error was incompletely extracted metadata leaving non-textual fragments within the textual parts of the corpus files. This is, on average, the case for every second speaker change. We not only cleaned the Europarl Corpus by correcting several kinds of errors, but also aligned the speakers’ contributions of all available languages and compiled every- thing into a new XML-structured corpus. This facilitates a more sophisticated selection of data, e.g. querying the corpus for speeches by speakers of a particular political group or in particular language combinations.
Text corpora come in many different shapes and sizes and carry heterogeneous annotations, depending on their purpose and design. The true benefit of corpora is rooted in their annotation and the method by which this data is encoded is an important factor in their interoperability. We have accumulated a large collection of multilingual and parallel corpora and encoded it in a unified format which is compatible with a broad range of NLP tools and corpus linguistic applications. In this paper, we present our corpus collection and describe a data model and the extensions to the popular CoNLL-U format that enable us to encode it.