Development of a Dependency Treebank for Russian and its Possible Applications in NLP

The paper describes a tagging scheme designed for the Russian Treebank and presents tools used for corpus creation. 1. Introductory Remarks The present paper describes a project aimed at developing the first annotated corpus of Russian texts. Large text corpora have been used in the computational linguistics community for quite a long time now; at present, over 20 large corpora for the main European languages are available, the largest of them containing hundreds of millions of words (Language Resources (1997); Marcus, Santorini and Marcinkiewicz (1993); Kurohashi, Nagao (1998)). For Russian, annotated corpora had been nonexistent until 2000 when the first part of the corpus reported here was compiled (Boguslavsky et al., 2000). Since then, Russian corpus linguistics has been evolving rapidly, and several groups of researchers announced their intent to create corpora. Of these, the CLD-MSU Corpus project looks particularly promising. It aims at building a morphologically tagged corpus, and an upgrade to syntactic annotation is envisaged in the future even though it is not pursued at the present stage (Sichinava, 2001). Different tasks require different annotation levels that entail different amount of information about text structure. The corpus that is being created in the framework of the project under discussion consists of several subcorpora that differ in the level of annotation. The following three levels are envisaged: lemmatized texts: for every word, its normal form (lemma) and part of speech are indicated; morphologically tagged texts: for every word, along with the lemma and the part of speech, a full set of inflectional morphological attributes is specified; syntactically tagged texts: apart from the full morphological markup at the word level, every sentence is assigned a syntactic structure. We annotate Russian texts with dependency structures – a formalism which we consider more suitable for Slavonic languages with their relatively free word order than constituent structures. The structure not only contains information as to which words of the sentence are syntactically linked, but also relegates each link to one of the several dozen syntactic types (at present, we use 78 syntactic relations). This is an important feature, since the majority of syntactically annotated corpora, both those already available and under construction, represent the syntactic structure by means of constituents. The closest analogue to our work is Prague Dependency Treebank (PDT) – an annotated corpus of Czech collected at Charles University in Prague (see Hajicova, Panevova, Sgall, 1998). In this corpus, the syntactic data are also expressed in the dependency formalism, although the inventory of syntactic functional relations is much smaller than ours as it only has 23 relations. Our corpus therefore gives a more fine-grained representation of syntactic phenomena. On the other hand, Czech researchers made an extremely interesting attempt to incorporate into their annotation information on discourse structure (topic-focus opposition) (Bemova et al., 1999). Besides PDT, several other corpus-related projects use some kind of dependency structures; worth noticing are NEGRA for German (Brants et al, 1999) and Alpino Dependency Treebank for Dutch (Van der Beek et al., 2001). In what follows, we describe the types of texts used to create the corpus (Section 2), markup format (Section 3), annotation tools and procedures (Section 4), and types of linguistic data included in the markup (Section 5). 2. Source Text Selection The well-known Uppsala University Corpus of contemporary Russian prose has been chosen as the primary source for the first part of our corpus, which has already been completed. This part contains about 10,000 sentences. The Uppsala Corpus is well balanced between fiction and journalistic genre, with a smaller percentage of scientific and popular science texts. The Corpus includes samples of contemporary Russian prose, as well as excerpts from newspapers and magazines of the last few decades of the 20 century, and gives a representative coverage of written Russian in modern use. Conversational examples are scarce and appear as dialogues inside fiction texts. The second part of the corpus consists of several hundred short texts published in 2001-2002 on various Internet news portals. The bulk of the texts come from the following newswires: www.yandex. ru, www.rbc. ru, www.polit. ru, www.lenta. ru, www.strana. ru, www.news. ru. Each text is a small story (up to 30 sentences) about a single event. Their themes include political, financial, cultural, and sports news, both domestic and international; and a certain amount of texts deal with hi-tech achievements. We have done our best to make source text selection a representative sample of Internet news delivery in Russian.