Abstract not found

We consider the use of language models whose size and accuracy are intermediate between different order n-gram models.

This paper describes an experimental com- parison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text.

Fourteen linguistically-motivated numeri- cal indicators are evaluated for their abil- ity to categorize verbs as either states or events. The values for each indicator are computed automatically across a corpus of text. To improve classification performance, machine learning techniques are employed to combine multiple indicators. Three machine learning methods are compared for this task: decision tree induction, a genetic algorithm, and log-linear regres- sion.

This paper presents the use of prot)abilistie class-based lexica tbr dismnbiguati(m in targetwoxd selection. Our method emlfloys nfinimal 1)llt; precise contextual information for disambiguation. That is, only information provided by the target-verb, enriched by the condensed information of a probabilistic class-based lexicon, is used. Induction of classes and fine-tuning to verbal arguments is done in an unsupervised manner by EM-lmsed clustering techniques. The method shows pronlising results in an evaluation on real-world translations. 1

Fourteen indicators that measure the frequency of lexico-syntactic phenomena linguistically related to aspectual class are applied to aspectual classification. This group of indicators is shown to improve classification performance for two aspectual distinctions, stativity and com- pletedness (i.e., tellcity), over unrestricted sets of verbs from two corpora. Several of these indicators have not previously been discovered to correlate with aspect.

We present an unsupervised method for word sense disambiguation that exploits translation correspondences in parallel corpora. The technique takes advantage of the fact that crosslanguage lexicalizations of the same concept tend to be consistent, preserving some core element of its semantics, and yet also variable, reflecting differing translator preferences and the influence of context. Working with parallel corpora introduces an extra complication for evaluation, since it is difficult to find a corpus that is both sense tagged and parallel with another language; therefore we use pseudotranslations, created by machine translation systems, in order to make possible the evaluation of the approach against a standard test set. The results demonstrate that word-level transla- tion correspondences are a valuable source of information for sense disam- biguation.