Document Clustering

. Document clustering has many uses in natural language tools and applications. For instance, summarizing sets of documents that all describe the same event requires first identifying and grouping those …...

. This paper presents a new document clustering method based on frequent co-occurring words. We first employ the Singular Value Decomposition, and then group the words into clusters called word …...

We propose a new document vector represen- tation specifically designed for the document clustering task. Instead of the traditional term- based vectors, a document is represented as an �-dimensional vector, …...

In unsupervised learning, where no train- ing takes place, one simply hopes that the unsupervised learner will work well on any unlabeled test collection. How- ever, when the variability in …...

. 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 …...

We present V-measure, an external entropy- based cluster evaluation measure. V- measure provides an elegant solution to many problems that affect previously de- fined cluster evaluation measures includ- ing 1) …...

Document clustering and classification is usually done by representing the documents using a bag of words scheme. This scheme ignores many of the linguistic and semantic features contained in text …...

Semantic similarity is a central concept that extends across numerous fields such as artificial intelligence, natural language processing, cognitive science and psychol- ogy. Accurate measurement of semantic similarity between words …...

Models of latent document semantics such as the mixture of multinomials model and La- tent Dirichlet Allocation have received sub- stantial attention for their ability to discover topical semantics in …...

There is considerable interest in interdis- ciplinary combinations of automatic speech recognition (ASR), machine learning, natu- ral language processing, text classification and information retrieval. Many of these boxes, especially ASR, …...

AbstractReflecting the rapid growth of science, technology, and culture, it has become common practice to consult tools on the World Wide Web for various terms. Existing search engines provide an …...

We present a sub-sentential alignment al- gorithm that relies on association scores between words or phrases. This algorithm is inspired by previous work on alignment by recursive binary segmentation and …...

Topic models have great potential for help- ing users understand document corpora. This potential is stymied by their purely un- supervised nature, which often leads to top- ics that are …...

A major challenge in document clustering re- search arises from the growing amount of text data written in different languages. Previ- ous approaches depend on language-specific solutions (e.g., bilingual dictionaries, …...

AbstractProbabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP …...

AbstractWord embeddings have become widely-used in document analysis. While a large number of models for mapping words to vector spaces have been developed, it remains undetermined how much net gain …...

AbstractIn this paper, we describe Alibaba’s participating system in the semEval-2018 Task5: Counting Events and Participants in the Long Tail. We designed and implemented a pipeline system that consists of …...

AbstractShort texts such as tweets often contain insufficient word co-occurrence information for training conventional topic models. To deal with the insufficiency, we propose a generative model that aggregates short texts …...

AbstractApplications such as textual entailment, plagiarism detection or document clustering rely on the notion of semantic similarity, and are usually approached with dimension reduction techniques like LDA or with embedding-based …...

Document Clustering aims to group similar documents based on the distance among them. A wide range of distance measures are available in the literature, and selecting an appropriate distance function …...