Improving NMF clustering by leveraging contextual relationships among words

Refinement of Document Clustering by Using NMF

In this paper, we use non-negative matrix factorization (NMF) to refine the document clustering results. NMF is a dimensional reduction method and effective for document clustering, because a term-document matrix is high-dimensional and sparse. The initial matrix of the NMF algorithm is regarded as a clustering result, therefore we can use NMF as a refinement method. First we perform min-max cu...

Improving Newsgroup Clustering by Filtering Author-Specific Words

Introduction. This paper describes the first step in a project for topic identification in help-desk applications. In this step, we apply a clustering mechanism to identify the topics of newsgroup discussions. We have used newsgroup discussions as our testbed, as they provide a good approximation to our target application, while obviating the need for manual tagging of topics. We have found tha...

"clustering Words" Clustering Words Clustering Words

KEY WORDS-Statistical Parsing, Grammar Acquisition, Clustering Analysis, Local Contextual

This paper proposes a new method for learning a context-sensitive conditional probability context-free grammar from an unlabeled bracketed corpus based on clustering analysis and describes a natural language parsing model which uses a probability-based scoring function of the grammar to rank parses of a sentence. By grouping brackets in a corpus into a number of similar bracket groups based on ...

Shifted Nmf with Group Sparsity for Clustering Nmf Basis Functions

Recently, Non-negative Matrix Factorisation (NMF) has found application in separation of individual sound sources. NMF decomposes the spectrogram of an audio mixture into an additive parts based representation where the parts typically correspond to individual notes or chords. However, there is a need to cluster the NMF basis functions to their sources. Although, many attempts have been made to...