The annotation of words and phrases by ontology concepts is extremely helpful for semantic interpretation. However many ontologies, e.g. WordNet, are too fine-grained and even human annotators often have disagreements about the precise word sense. Therefore we use coarse-grained supersenses of WordNet. We employ conditional random fields (CRFs) to predict these supersenses taking into account the interaction of neigboring words. As the annotation of training data is costly we modify the CRF algorithm to process lumped labels, i.e. a set of possible labels for each training example, one of which is the correct label. This information can be derived automatically from WordNet. By this and the use of new features we are able to increase the f-value for about 8% compared to previous results. If we use training data without annotations it turns out that the resulting F-value is only slightly lower than for a fully labeled training data. Therefore unlabeled data may be employed in an unsupervised way to increase the quality of predicted supersenses. Most of the information accessible in the internet and intranets is stored as text in different formats. A major obstacle of using this information by computers is the inherent polysemy: words can have more than one distinct meaning. For example, the 121 most frequent English nouns, which account for about 20% of the words in real text, have on average 7.8 meanings each [1] . In spite of this ambiguity humans are nearly unconciously able to determine the correct word sense. Word sense disambiguation (WSD) is the process of identifying, which sense of a word is used in a given context. Usually word senses are taken from a given sense inventory, e.g. WordNet [12] . Then WSD is essentially a task of classification: word senses are the classes, the context, i.e.
For instance, REF apply Conditional Random Fields to model the sequential context of words and their relation to SuperSenses.
Exploiting semantic constraints for estimating supersenses with CRFs
In Proc. SDM 2009. Frank Reichartz, Hannes
Computer Science