Web Document Clustering Using Document Index Graph

Document Clustering is an important tool for many Information Retrieval (IR) tasks. The huge increase in amount of information present on web poses new challenges in clustering regarding to underlying data model and nature of clustering algorithm. Document clustering techniques mostly rely on single term analysis of document data set. To achieve more accurate document clustering, more informative feature such as phrases are important in this scenario. Hence first part of the paper presents phrase-based model, Document Index Graph (DIG), which allows incremental phrase-based encoding of documents and efficient phrase matching. It emphasizes on effectiveness of phrase-based similarity measure over traditional single term based similarities. In the second part, a Document Index Graph based Clustering (DIGBC) algorithm is proposed to enhance the DIG model for incremental and soft clustering. This algorithm incrementally clusters documents based on proposed clusterdocument similarity measure. It allows assignment of a document to more than one cluster. The DIGBC algorithm is more efficient as compared to existing clustering algorithms such as single pass, K-NN and Hierarchical Agglomerative Clustering (HAC) algorithm

Hierarchical growing neural gas

“The original publication is available at www.springerlink.com”. Copyright Springer.This paper describes TreeGNG, a top-down unsupervised learning method that produces hierarchical classification schemes. TreeGNG is an extension to the Growing Neural Gas algorithm that maintains a time history of the learned topological mapping. TreeGNG is able to correct poor decisions made during the early phases of the construction of the tree, and provides the novel ability to influence the general shape and form of the learned hierarchy

Abstract Meaning Representation for Multi-Document Summarization

Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.Comment: 13 page