… hierarchical clustering of words and clustering of multiword compounds. A large vocabulary
of English words (70,000 words) is clustered … from 5 million to 50 million words, using mutual …

… each word type with a probability distribution over word … a hierarchical clustering of the word
types: we use an agglomerative clustering algorithm where the distance between clusters is …

… This paper presents a simple trainable algorithm that selects a few 1-3 word labels to
describe each cluster in a document hierarchy. The algorithm dynamically decides how many …

… This leads to an objective function for clustering that is … word clustering, ie, the clustering
that minimizes this objective function, we present a new divisive algorithm for clustering words. …

… We repeated the clustering exercise using 25 to 200 words at a step size of 25. To determine
the number of different topics present in this dataset one can look at the unique MeSH …

… of words cluster will help us understand which words we will … to cluster RSS data using
hierarchical clustering method. Through clustering process analysis we describe the hierarchical …

… Syntactic relations between words are validated in terms of semantic relations between
word classes using a set of semiautomatically acquired selectional rules Basili, (1992a). For …

… We cluster words into a synonym hierarchy using the TreeGCS hierarchical clustering neural
network that we have developed, described in [7], [6] and 3.2 GCS algorithm, 3.3 TreeGCS …

… automatic generation of compound key words using frequency of words in documents. The
… key words, single terms are first clustered into groups of semantically related words. We will …

… of our clustering criterion is that each cluster is identified by some common words, called …
In this section, we explain how to construct a hierarchical cluster tree. The resulting tree has two …