Chapter 1: INTRODUCTION 1. Overview . Text Segmentation A text is not just a set of words, but it has some coherent structure. The meaning of each word cannot be determined until it is placed in the structure of the text. Recognizing the structure of text is an essential task in the process of text segmentation. One of the constituents of the text structure is a text segment. A text segment, whether or not it is explicitly marked, as are sentences and paragraphs, is defined as sequence of clauses or sentences that display local coherence. It resembles a scene in a movie, which describes the same objects in the same situation. It is an important process in digital video library. The segmentation task can be conducted on video, speech & text. …show more content…
A lot of research has been done on English literatures where basically C99 (presented by choi) and Topic Tiling are extensively used to segment text in various literature documents but this has done on very few images of documents. Combinations of these two programmes introduce Text Segmentation algorithm called Topic Tiling which is based on Text Tiling but conceptually simpler where in this it is smallest basic unit considered is a sentence. A coherence score cp can calculated between two adjacent sentences for which assigned topic IDs are used to the words by inference here assuming the LDA model with T topics that each block is represented as a T-dimensional vector. Therefore the element of each vector contains the frequency of the topic ID example‘t’ obtained from the respective block. The coherence score is calculated by cosine similarity for each adjacent that is topic vector therefore the values close to zero indicate marginal relatedness between two adjacent blocks whereas values close to one denote a substantial
Structure is essential for both literary text and informative text. The informative text provides facts laid out in
Previous Work: As stated above there has been lot of work done reporting promising performance of topic models like results on text categorization in the original LDA paper(Blei et al.2003). Work done by Wei and Bruce Croft(2006) shows that LDA could improve the state of art information retreival in the language modeling framework. Etc.
is called intertextuality. The intertext is that part or that region of texts that overlap, the border zone by which
character segmentation and recognition. Deskewing the input image is then a crucial step in the
Text painting also known as word painting in vocal music, musical representation of individual words and textual images. For example, the use of high pitches for words like “sky” low pitches for words like deep, and ascending pitches for “rise.” Also, a minor key may describe a said event. The major key will describe a happy and joyful. Text painting was used in madrigals, renaissance and Baroque periods. Two songs I used as examples are “As Vesta was from Latmos hill descending” and Justin Timberlake’s “What goes around”. These two examples, expresses a form of word painting. Both songs you can the descending and ascending pitches.
Knowledge from literary sources is transferred into knowledge case with the help of text understanding programs (text recognition or identification programs).
... do not read every word on each page, but instead scan them to find what we need. We tend to focus on words and phrases that sound like they match what we are looking for. For example, the Web of Services (http://www.w3.org/standards/webofservices/) web page uses headings and text that is grouped together and is easy to scan from top to bottom.
We aim at using Word Sense Disambiguation for text classification in which all the non-stop-words will be tagged with their senses relative to the text in the document and these senses are later selected as classes for the given document. Accordingly, there will be a common class for all the related text and hence can be searched better. We shall use WorldNet to extract the correct senses of the ambiguous non-stop-words. The classification results thus obtained shall later be evaluated by comparing with the classification results of manual disambiguation.
According to Mokhtari and Reichard (2002), global reading strategies are intentional, carefully planned techniques by which readers monitor or manage their reading. There are a total of 13 items (strategies) in this category. Having a purpose in mind, previewing the the text length and organization and also using tables and figures are examples of the strategies These strategies can be taken as a ‘generalized, intentional reading strategies’ which aims for setting up the stage for reading to happen. This means that the strategies helps readers to set purpose of reading the text and also making predictions of the text before they actually read the whole
Throughout history, there have been millions of books written. It is to no one’s surprise that some stories resemble another. Especially when both stories were written by the same author. This is the case with Romeo and Juliet and Midnights Summer Dream, both written by the ingenious William Shakespeare. Mr. Shakespeare had an interest in Mythology and its persons, so several of his stories involved a character from those mythical tales. Some similarities between the two texts include a pair of lovers that want to be together but can’t, a character from the mythical stories written by Homer, and a father’s willingness to disown or even kill his own daughter because she disobeyed him.
Image segmentation plays a vital role in Image Analysis and computer vision which is considered as the obstruction in the development of image processing technology, Image segmentation has been the subject of intensive research and a wide variety of segmentation techniques has been reported in the last two decades. Image segmentation is a classical and fundamental problem in computer vision. It refers to partitioning an image into several disjoint subsets such that each subset corresponds to a meaningful part of the image. As an integral step of many computer vision problems, the quality of segmentation output largely influences the performance of the whole vision system. In general terms, image segmentation divides an image into related sections or regions, consisting of image pixels having related data feature values. It is an essential issue since it is the first step for image understanding and any other, such as feature extraction and recognition, heavily depends on its results. Segmentation algorithms are based on two significant criteria: the homogeneity of a region and the discontinuity
First of all, text is a classic media element in multimedia application. From the pass decades, text evolved vigorously in the human civilization, from artistic sketches on walls of caves to readable words on printed media or electronic devices. According to Estrella (n.d.), “text is the graphic representation of speech. Unlike speech, however, text is silent, easily stored, and easily manipulated”. Nowadays, text is an excellent tool to articulate a notion as it is a visual image itself. Mai and Neo (1999) stated that, “text cannot be used on its own in a multimedia application” (p. 43). Although text can convey direct messages for the user, it has to be integrated with other media elements in consideration of better presentation result. So, the type of fonts and sizes of text contribute a vital aspect in assisting the visual appealing of text applications. Serif fonts are designed with small ticks at the bottom of each character but vice versa for Sans Serif fonts (Estrella, n.d.). Times New Roman and Arial are the popular typefaces which used in the body of an article. Bold, italics, underlined are some of the examples for the text styles. Besides, text is also a basic media as it is the fundamen...
...he image processing, there are many research has been done in this field but we cannot sure that which of the segmentation process are effective than other. There also a tough task to say a particular method or algorithm is applicable to an image or set of image or not. In present era, a commonly used method is subjective evaluation for a segment that evaluates the segmented method. In subjective segmentation evaluation we manually compare the result of different-different segmentation algorithm. This method are effective but more time overshadowing because to judge all methods manually is take too much time. So this method is limited to some methods. Because every person have its own evaluation criteria or its own standard to judge the segment, the result of subjective evaluation method are differs from person to person. So this method can’t give impartial result.
Jurafsky, D. & Martin, J. H. (2009), Speech and Language Processing: International Version: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 2nd ed, Pearson Education Inc, Upper Saddle River, New Jersey.
Different genres and registers employ a range of cohesive devices and for varying communicative functions. For instance, the use of reference, substitution and ellipsis in legal texts is minimal and, synonyms and super-ordinates are generally scanty in scientific and technical texts. While Halliday and Hasan (1976) and other researchers primarily use literary texts, Cook (2001) and Huang (2001) deal with cohesive devices in advertisements, and Yu (2004) investigates cohesive devices in academic introductions from the perspective of genre. Nevertheless, the study of cohesive devices in non-literary texts is still a neglected area which needs further explorations.