A Statistical Model for Domain-Independent Text Segmentation

Using Multiple Discriminant Analysis Approach for Linear Text Segmentation

Research on linear text segmentation has been an on-going focus in NLP for the last decade, and it has great potential for a wide range of applications such as document summarization, information retrieval and text understanding. However, for linear text segmentation, there are two critical problems involving automatic boundary detection and automatic determination of the number of segments in ...

Building A Chinese Text Summarizer with Phrasal Chunks and Domain Knowledge

This paper introduces a Chinese summarizier called ThemePicker. Though the system incorporates both statistical and text analysis models, the statistical model plays a major role during the automated process. In addition to word segmentation and proper names identification, phrasal chunk extraction and content density calculation are based on a semantic network pre-constructed for a chosen doma...

Sequence segmentation for statistical machine translation

In the last decade, while statistical machine translation has advanced significantly, there is still much room for further improvements relating to many natural language processing tasks such as word segmentation, word alignment and parsing. Human language is composed of sequences of meaningful units. These sequences can be words, phrases, sentences or even articles serving as basic elements in...

A Modified Character Segmentation Algorithm for Farsi Printed Text Using Upper Contour Labelling

In this paper, a modified segmentation algorithm for printed Farsi words is presented. This algorithm is based on a previous work by Azmi that uses the conditional labeling of the upper contour to find the segmentation points. The main objective is to improve the segmentation results for low quality prints. To achieve this, various modifications on local baseline detection, contour labeling an...

A New Segmentation Technique for Omnifont Farsi Text