This paper presents a corpus-based approach to word sense disambiguation where a decision tree assigns a sense to an ambiguous word based on the bigrams that occur nearby. This approach is evaluated using the sense-tagged corpora from the 1998 SENSEVAL word sense disambiguation exercise. It is more accurate than the average results reported for 30 of 36 words, and is more accurate than the best results for 19 of 36 words.
The decision list is a closely related approach that has also been applied to word sense disambiguation (e.g., @cite_6 , @cite_14 , @cite_4 ). Rather than building and traversing a tree to perform disambiguation, a list is employed. In the general case a decision list may suffer from less fragmentation during learning than decision trees; as a practical matter this means that the decision list is less likely to be over--trained. However, we believe that fragmentation also reflects on the feature set used for learning. Ours consists of at most approximately 100 binary features. This results in a relatively small feature space that is not as likely to suffer from fragmentation as are larger spaces.
@cite_14 @cite_4 @cite_6
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This paper describes a supervised algorithm for word sensedisambiguation based on hierarchies of decision lists. This algorithmsupports a useful degree of conditional branching while minimizing thetraining data fragmentation typical of decision trees. Classificationsare based on a rich set of collocational, morphological and syntacticcontextual features, extracted automatically from training data andweighted sensitive to the nature of the feature and feature class. Thealgorithm is evaluated comprehensively in the SENSEVAL framework,achieving the top performance of all participating supervised systems onthe 36 test words where training data is available. Decision trees are probably the most popular and commonly used classification model. They are recursively built following a top-down approach (from general concepts to particular examples) by repeated splits of the training dataset. When this dataset contains numerical attributes, binary splits are usually performed by choosing the threshold value which minimizes the impurity measure used as splitting criterion (e.g. C4.5 gain ratio criterion or CART Gini's index). In this paper we propose the use of multi-way splits for continuous attributes in order to reduce the tree complexity without decreasing classification accuracy. This can be done by intertwining a hierarchical clustering algorithm with the usual greedy decision tree learning. Decision trees are the commonly applied tools in the task of data stream classification. The most critical point in decision tree construction algorithm is the choice of the splitting attribute. In majority of algorithms existing in literature the splitting criterion is based on statistical bounds derived for split measure functions. In this paper we propose a totally new kind of splitting criterion. We derive statistical bounds for arguments of split measure function instead of deriving it for split measure function itself. This approach allows us to properly use the Hoeffding's inequality to obtain the required bounds. Based on this theoretical results we propose the Decision Trees based on the Fractions Approximation algorithm (DTFA). The algorithm exhibits satisfactory results of classification accuracy in numerical experiments. It is also compared with other existing in literature methods, demonstrating noticeably better performance. Decision trees are attractive classifiers due to their high execution speed. But trees derived with traditional methods often cannot be grown to arbitrary complexity for possible loss of generalization accuracy on unseen data. The limitation on complexity usually means suboptimal accuracy on training data. Following the principles of stochastic modeling, we propose a method to construct tree-based classifiers whose capacity can be arbitrarily expanded for increases in accuracy for both training and unseen data. The essence of the method is to build multiple trees in randomly selected subspaces of the feature space. Trees in, different subspaces generalize their classification in complementary ways, and their combined classification can be monotonically improved. The validity of the method is demonstrated through experiments on the recognition of handwritten digits.