You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey of Text Mining Techniques and Applications 
Chapter Title:  B. Topic Tracking 
1. Abstract of Cited Paper (BIB001):  The aim of topic tracking is to monitor the stream of news stories to find additional stories on a topic that was identified using several sample stories. We propose a method that using NER information for improved topic tracking. We call it multi-vector. We extract proper names, locations and normal terms into distinct sub-vectors of the document representation. Measuring the similarity of two documents is conducted by comparing two sub-vectors at a time. We use TDT4 corpus as test corpus and compare the topic tracking system performance between the system based on multi-vector model and the system based on traditional vector space model. We also analyze the number of features that effect topic tracking performance. The experimental result shows that the tracking performance will be improved by using multi-vector model.

Literature Review:
A topic tracking system works by keeping user profiles and, based on the documents the user views, predicts other documents of interest to the user. Yahoo offers a free topic tracking tool (www.alerts.yahoo.com) that allows users to choose keywords and notifies them when news relating to those topics becomes available. Topic tracking technology does have limitations, however. For example, if a user sets up an alert for "text mining", s/he will receive several news stories on mining for minerals, and very few that are actually on text mining. Some of the better text mining tools let users select particular categories of interest or the software automatically can even infer the user's interests based on his/her reading history and click-through information. There are many areas where topic tracking can be applied in industry. It can be used to alert companies anytime a competitor is in the news. This allows them to keep up with competitive products or changes in the market. Similarly, businesses might want to track news on their own company and products. It could also be used in the medical industry by doctors and other people looking for new treatments for illnesses and who wish to keep up on the latest advancements. Individuals in the field of education could also use topic tracking to be sure they have the latest references for research in their area of interest. Keywords are a set of significant words in an article that gives high-level description of its contents to readers. Identifying keywords from a large amount of on-line news data is very useful in that it can produce a short summary of news articles. As on-line text documents rapidly increase in size with the growth of WWW, keyword extraction BIB001 has become a basis of several text mining applications such as search engine, text categorization, summarization, and topic detection. Manual keyword extraction is an extremely difficult and time consuming task; in fact, it is almost impossible to extract keywords manually in case of news articles published in a single day due to their volume. For a rapid use of keywords, we need to establish an automated process that extracts keywords from news articles. The architecture of keyword extraction system is presented in figure 5 . HTML news pages are gathered from a Internet portal site. And candidate keywords are extracted throw keyword extraction module. And finally keywords are extracted by cross-domain comparison module. Keyword extraction module is described in detail. We make tables for 'document', 'dictionary', 'term occur fact' and 'TF-IDF weight' in relational database. At first the downloaded news documents are stored in 'Document' table and nouns are extracted from the documents in 'Document table. Term occur fact' table. Next, TF-IDF  weights for each word are calculated using 'Term occur   fact' table and the result are updated to 'TF-IDF weight'  table. Finally, using 'TF-IDF weight' table, 'Candidate  keyword list' for each news domain with words is ranked high. Keyword extraction module is given in figure 6 . Lexical chaining  is a method of grouping lexically related terms into so called lexical chains. Topic tracking involves tracking a given news event in a stream of news stories i.e. finding all the subsequent stories in the news stream. In multi vector BIB002 topic tracking system proper names, locations and normal terms are extracted into distinct sub vectors of document representation. Measuring the similarity of two documents is conducted by comparing two sub-vectors at a time. Number of features that effect the performance of topic tracking system are analyzed. First choice is to choose one characteristic, such as the choice of words, words or phrases such as string as a feature in this term to make features as an example. that discuss the given event.