Document Analysis Using Latent Semantic Indexing With Robust Principal

This week we went a little bit more into the actual process of constructing an inverted index and how to implement the handling of misspellings, the accuracy of retrievals based on context, and the structures of resulting indexes for the purpose of searching them. For the discussion assignment, I needed to cover the differences between BSBI and SPIMI. For our programming assignment, we needed to make some alterations to a SPIMI python program to create an index and list of documents.

RI 1 Cite textual evidence to support analysis of what the text says explicitly as well as inferences drawn from the text. RI 2 Determine a central idea of a text and analyze how it emerges and is shaped and refined by specific details. RI 3 Analyze how the author unfolds a series of ideas or events. RI 4 Determine the meaning of words as they are used in a text. L 5 Demonstrate understanding of word relationships.

1.2 Background . . . . . . . . . . . . . .. . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .

Text Retrieval and techniques are modified by many commercial and many open sources in the domain of information retrieval. Text retrieval refers to the process of searching for texts, information within collections, or metadata about documents. It is assigned to answer for relevant documents, not just simple matches to patterns. When considering indexing and searching applications, users may find and consider among many products available on the market. Mainly, the products can be grouped into two categories. The first of these categories are information retrieval libraries that can be easily developed and embedded into application. The second consists of ready to apply indexing and searching applications that are basically designed to work with particular types of data, and are therefore less flexible (Molková 2011). Some of the famous libraries cover text retrieval topic are introduced in next section.

Text mining is generally identical to content examination it is the way toward getting brilliant data from text. Fantastic data is ordinarily determined through the concocting of examples and patterns through means, for example, factual example learning. Content mining as a rule includes the way toward organizing the information message typically parsing, alongside the expansion of some determined phonetic highlights and the expulsion of others, and ensuing inclusion into a database, inferring designs inside the organized information, lastly assessment and elucidation of the yield. 'High caliber' in content mining more often than not alludes to some blend of pertinence, oddity, and interesting .Text examination programming can help by transposing words and expressions in unstructured information into numerical esteems which would then be able to be connected with organized information in a database and broke down with conventional information mining techniques. Text mining is a minor departure from a field called information mining that tries to discover

Collect, analyse and investigate the data which were collected from many sources from 1979 to 2015.

The next step in the sentiment analysis is to extract and select text features. Here feature selection technique treat the documents as group of words (Bag of Words (BOWs)) which ignores the position of the word in the document.Here feature selection method used is Chi-square (x2).

A several measures have been proposed for computing the similarity between two document vectors. The Kullback-Leibler divergence [3] proposed a non-symmetric measure of the difference between the probability distributions associated with the two vectors. Euclidean distance [5] is a recognized similarity metric taken from the Euclidean geometry field. Manhattan distance [11], is very similar to Euclidean distance and also recognized as the taxicab metric, is another

Where P(zl) denotes the probability of topic zl and probability factors P(di|zl) and P(tj|zl) denote how often document di and term tj are associated with topic variable zl, respectively. The aforementioned probability factors are used to maximize the log-likelihood

The recent years have seen a huge increase in the number of online documents. This has resulted in a huge amount of information being available at the click of a mouse. But, at the same time, the retrieval of relevant information from this collection of unstructured documents has emerged as a challenging task and is a topic of research. A major part of retrieving information out of a document is finding out the words or phrases of significance in the article like the persons, organization, location,

Like other computer-driven profiling tools such as RANGE or Antword, VocabProfiler is a simple and useful equivalent that can be used to process texts and provide the categories of textual lexis based on frequency (Cobb, 2007). To run the Vocabprofiler, one of the seven available frequency schemes has to be selected (see http://www.lextutor.ca/vp/). The chosen scheme in this paper is the BNC-COCA-25 list. It is a frequency framework, developed by Nation (2013), that combines the British National Corpus with the Corpus of Contemporary American English and reaches an adapted list of 25,000 word families.

It is important to understand how the theory led to the computational practice, as well as the limitations of the process given the statistical and theoretical background of the subject. This review will only cover a handful of statistical NLP and pattern recognition processes, focusing on the methods most likely to be helpful given the problem and skill set of the research team.

First, we categorize questions based on interrogatives second, for the rest questions; we perform a classification using a naive Bayes classifier. Table I Representative interrogative words 1.2 Answer-Based Classification: Apart from questions, answer can also be an important clue. For answer classification bigram text features and verbs are extracted. With the help of verb it will be easy to judge whether the answer can be related with video content.

Stemming becomes a part of most of text processing applications. Stemmers are used in text categorization, info retrieval, and search engines.[5,6]

In the past ten years’ management of document grounded contents (together known as information retrieval – IR) have become very popular in the information systems field, due to the better availability of documents in digital form and subsequent necessity to access them in supple and effectual ways. Text categorization (TC), the movement of labeling natural language scripts with one or more types from a predefined set, is one such job. Machine learning (ML) methodology, according to which we can spontaneously build an automatic text classifier by learning, from a set of pre-classified text documents established on the characteristics of the categories of interest. The gains of this approach is accuracy as compared to that achieved by human beings, and an extensive savings in terms of expert manpower, since no involvement from either domain experts or knowledge engineers is required for the construction of the classifier.