Abstract Traditionally, linguists have organized languages of the world as language families, such Indo-European, Dravidian and Sino-Tibetan. Within Indo-European family, they further into sub-families Germanic, Celtic Indo-Iranian. They do this by looking at similar-sounding words across commonality rules word formation sentence construction. In work, we make use computational approaches that ...

In the majority of cases, the pronoun it illustrates nominal anaphora, tending to refer back to another noun phrase in the text. However, in a significant minority of cases, the pronoun is used in exceptional ways that fail to demonstrate strict nominal anaphora. The identification of these uses of it is important in all fields where pronoun resolution has an impact. Following a survey of previ...

We describe a generalization of the multiple-instance learning model in which a bag’s label is not based on a single instance’s proximity to a single target point. Rather, a bag is positive if and only if it contains a collection of instances, each near one of a set of target points. We list potential applications of this model (robot vision, content-based image retrieval, protein sequence iden...

In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...

Multi-instance learning (MIL) is a useful tool for tackling labeling ambiguity in learning because it allows a bag of instances to share one label. Bag mapping transforms a bag into a single instance in a new space via instance selection and has drawn significant attention recently. To date, most existing work is based on the original space, using all instances for bag mapping, and the selected...

Instance-based learning methods explicitly remem­ ber all the data that they receive They usually have no training phase and only at prediction time do they perform computation Then they take a query search the database for similar datapoints and build an on-line local model (such as a local average or local regression) with which to predict an output value In this paper we review the advantage...

This paper proposes a new algorithm for acquisition of preference predicates by a learning apprentice, termed Compositional Instance-Based Learning (CIBL), that permits multiple instances of a preference predicate to be composed, directly exploiting the transitivity of preference predicates. In an empirical evaluation, CIBL was consistently more accurate than a I-NN instance-based learning stra...