A Unified Probabilistic Generative Model

Analyzes the documents by grouping them in as many appropriate ways as possible. Does not simply summarize the documents individually.

• Analyzes the documents by grouping them in as many appropriate ways as possible and does not simply summarize the documents individually.

RI.3.5 Use text features and search tools (e.g., key words, sidebars, hyperlinks) to locate information relevant to a given topic efficiently.

topic spotting: categorize news articles (say) and differentiate them such that whether they are about politics, sports, entertainment, etc.

The main purpose is to detect topics automatically and track related documents from a stream of documents temporally so that readers can understand. First stage, Theme Generation process tries to identify the theme of the topic. Next Event Segmentation and Summarization models the documents as a symmetric block association matrix. Eigen vectors are then drawn to examine and extract summaries. Finally, Temporal Similarity (TS) function is used to calculate the event dependencies. This had given me an opportunity to expose my knowledge in Software Engineering and Data Mining. This also helped us to gain domain knowledge and also enhance technical skills like Servlets and JSP, used for implementing main logic, while JDBC for back end database connection and performing basic operations of database and Html for UI

Including these features, Web analytics may incorporate following the clickthrough and drilldown conduct of clients inside the Web website, deciding the destinations from which clients frequently arrive, and speaking with programs to track and analyze online conduct. The output of Web analytics are given as tables, diagrams, and charts.

Highlight extraction/choice distinguishes essential words in a content report. This is done utilizing strategies like TF-IDF (term recurrence opposite report recurrence) [14], LSI (dormant

Some research uses the pattern mining techniques to overcome these issues related to the phase mining techniques. But there are two significant issue when considering pattern based mining approach over the phrase based approach which are misinterpretation and low frequency [1 – Effective Pattern Discovery]. Misinterpretations in patter mining are the measures which are not appropriate to respond the user needs in the patters discovered. Frequency in pattern mining is to categorize whether the given topic is highly frequent or low frequent pattern. The major issue here is to decide on how to use pattern discovered that could be

Text document processing plays a key role in data mining as well as web search for information retrieval. In text processing, the commonly used model is bag-of-words model [5]. In this model each document is typically represented in vector form in which each element indicates the value of the analogous feature in the document. The feature value can be selected by finding number of occurrences of a term in the document. However relative term frequency can be defined as the ratio between the term frequency and the total number of occurrences of all the terms in the document set. Frequently, the dimensionality of a document is large and the resulting vector is sparse, i.e., most of the selected feature values in the vector are zero. Such high-dimensionality and sparsity is a challenge for similarity measure and thus it is a very important operation in text processing algorithms.

Li, Kitsuregawa [2007] also proposed a similar adaptive approach based on user behaviors. Instead of using ODP as the taxonomy, Google Directory3 is used as the predefined taxonomy to construct user profiles.

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

clusters. The clusters are represented as an average of all documents contained within the cluster. This average can be thought of

There are two main approaches to IR, which are statistical approach and semantic approach. In a statistical approach, documents are analyzed and broken down into chunks of text and each word or phrase is counted, weighted, and measured for relevance or importance. Meanwhile, semantic approaches to IR use knowledge-based techniques of retrieval the broadly rely on the syntactic, lexical, sentential, discourse-based, and pragmatic levels of knowledge understanding. In practice, semantic approaches also apply some form of statistical analysis to improve the retrieval process.

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,

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.