GWC 2008

Verification of Valency Frame Structures by Means of Automatic… 37
• morphological and syntactic features, i.e. frequent surface expression (e.g. the
particular preposition for nouns, the aspect form for infinitive, an adverb from the
particular group, etc.).
The described structure is used for automatic disambiguation [3]: the parsing of the
phrase or sentence is mapped against valency frames of words in the construction. In
case of matching between a parse structure and obligatory valency frames, it is
considered verified, otherwise, optional valency frames may show preferred analysis.
The open question is whether inheritance of valency frame parameters in
troponymic verbal trees exists, in what form and for what parameters. We had a
preliminary research basing on three semantic verb trees in RussNet [8] and, though
didn’t receive unequivocal confirmation of the inheritance scheme, it may be stated
that context features of the major part of verbs from a particular semantic tree share a
lot of statistically stable parameters.
In order to prove this stability we investigated the automatic clustering of verb
contexts as representatives of WMs in RussNet. This research is presented below.
3 Motivation: How to Use a Morphological Marking up of the
Text for Sense Disambiguation
In our research we chose for a starting point the approach of [4]. In this work the
disambiguation procedure for a verb serve was described, in which 3 distributions
were used: main POS tags, additional markers (e.g. punctuation marks, prepositions,
etc.), and lexical items. This investigation demonstrated that POS tags and some other
features afford to reliably (80–83%) differentiate meanings of this polysemious verb.
The results depend on the width of the analysis window, and the substantial amount of
contexts in the training set.
The authors drew conclusions comparing their approach with similar ones
1. initial processing of the text (e.g. syntactically connected fragments) doesn’t affect
the results crucially;
2. it was unachievable to differentiate low frequency WMs, because it was hardly
possible to compile quality training set;
3. it was easier to differentiate homonyms (or contrast WMs) than similar WMs;
4. the huge amount of a training set improves the results but not to the same extent as
processing time increased for preparing these sets.
3.1 Preliminary Results: the POS Tag Distribution for Different Semantic Verb
Groups
The mentioned research might be valid only for languages with a fixed word order,
and hardly applicable for languages with freer word order. We decided for the
beginning to fulfil “pilot” check: compare distributions of 9 verbs from two groups:
verbs of movement (идти ‘to go’, пойти ‘to start walking’, выйти ‘to go out’,
вернуться ‘to return’, ходить ‘to walk’) and communication verbs (сказать ‘to
say’, говорить ‘to speak’, спросить ‘to ask’, ответить ‘to answer’, просить ‘to