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:  D. Categorization 
1. Abstract of Cited Paper (BIB001):  Text categorization has become the key technology in organizing and processing the large amount of text information. It normally involves an extremely high dimensional space, which makes most existing approaches generate highly biased estimates so as to reduce the classification accuracy. These approaches do not consider that the text documents may be intrinsically located on the low-dimensional manifold. This paper presents an approach that performs text categorization on texts manifold with respect to the intrinsic global manifold structure, such as by geodesic distance to measure the distance between two texts. This approach has been applied to improve the KNN for text categorization. This is empirically validated by the conducted experiments. 
2. Abstract of Cited Paper (BIB002):  This paper presents a new text categorization method based on P-L theory and Kohonen network, which called TCBPLK method. The Kohonen network is applied to realizing text categorization, which has a defect of too slowly speed of training. To text vector of high dimension, the defect is more obvious. Even the result of text categorization can not be acquired. The new method establishes vector space model of term weight by the theory of P-L, which enhances the function of the words from the viewpoint of categorization effect, and decreases the dimension of vector through eliminating redundant features. Experimental results confirm that TCBPLK method decreases the number of vector, and enhances the generalization and precision of text categorization.

Literature Review:
Categorization involves identifying the main themes of a document by placing the document into a pre-defined set of topics. When categorizing a document, a computer program will often treat the document as a "bag of words." It does not attempt to process the actual information as information extraction does. Rather, categorization only counts words that appear and, from the counts, identifies the main topics that the document covers. Categorization often relies on a thesaurus for which topics are predefined, and relationships are identified by looking for broad terms, narrower terms, synonyms, and related terms. Categorization tools normally have a method for ranking the documents in order of which documents have the most content on a particular topic. As with summarization, categorization can be used with topic tracking to further specify the relevance of a document to a person seeking information on a topic. The documents returned from topic tracking could be ranked by content weights so that individuals could give priority to the most relevant documents first. Categorization can be used in a number of application domains. Many businesses and industries provide customer support or have to answer questions on a variety of topics from their customers. If they can use categorization schemes to classify the documents by topic, then customers or endusers will be able to access the information they seek much more readily. The goal of text categorization is to classify a set of documents into a fixed number of predefined categories. Each document may belong to more than one class. Using supervised learning algorithms BIB001 , the objective is to learn classifiers from known examples (labeled documents) and perform the classification automatically on unknown examples (unlabeled documents). Figure. The text categorization task is to train the classifier using these documents, and assign categories to new documents. In the training phase, the n documents are arranged in p separate folders, where each folder corresponds to one class. In the next step, the training data set is prepared via a feature selection process. Text data typically consists of strings of characters, which are transformed into a representation suitable for learning. It is observed from previous research that words work well as features for many text categorization tasks. In the feature space representation, the sequences of characters of text documents are represented as sequence of words. Feature selection involves tokenizing the text, indexing and feature space reduction. Text can be tokenized using term frequency (TF), inverse document frequency (IDF), term frequency inverse document frequency (TFIDF) or using binary representation. Using these representations the global feature space is determined from entire training document collection. In text manifold BIB002 categorization method, the text documents are treated as vectors in an n-dimensional space, where every dimension corresponds to a term. Then the metrics such as the cosine of the angle between two documents can be defined. However this space may be intrinsically located on the low dimensional manifold. The metric therefore should be defined according to the properties of manifold so as to improve the text categorization furthermore. The whole process is illustrated as Figure 9 . In TCBPLK BIB003 method of text categorization, texts are automatically assigned to appointed species according to text content. Similar texts are assigned to the same species through calculating the similarity among the texts. After the process of pattern aggregation for the word matrix, the numbers of words are greatly decreased, then TF.IDF method is applied to constructing the VSM. As the dimensions of the text are greatly decreased through the process of the P-L, the method decreases the learning time, and advances the speed and the of text categorization.