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:  1) Questioning in Natural Language: 
1. Abstract of Cited Paper (BIB001):  Automatic question answering system is a hot issue in the field of natural language processing, and is playing an increasingly important role in the long-distance teaching through networks. This paper proposes an ontology-based automatic question answering system model, at first, build restricted area ontology, then take advantage of the accurate description of concept as well as the definition of the relationship of the concepts, to expand keywords and improve the accuracy and recall rates.

Literature Review:
The most important case of application of the linguistic competences developed in the TM context is the construction of websites that support systems of questioning in natural language. The need to make sites cater as much as possible for the needs of customers who are not necessarily expert in computers or web search is common also to those companies that have an important part of their business on the web. The system architecture BIB002 of an automatic question answering system is shown in figure 20 . In the user interface, users can question using natural language and then process automatic word segmentation. For the automatic question answering system, the user's question is for a specific course. The proposed question answering system is based on a specific course. Therefore, it is easy to extract keywords from the results of automatic word segmentation. The ontology-based knowledge base defines the concepts and the relationship between the concepts in the field of curriculum. The concepts have been clearly defined and given the computer understandable semantics. With this domain ontology, the system can expand the keywords, increasing the search area for the problem, improve the system's recall rate. Then, the system uses the expanded key words to query in the FAQ base and return the handled answers to users. When working on a multilingual speech recognition system BIB001 , a good deal of attention must be paid to the languages to be recognized by the system. In this application, a recognition system for Italian and German is built, thus the properties of both languages are of importance. From the acoustic point of view, the Italian language presents a significantly smaller number of phones with respect to German -e.g. 5 vowels in Italian versus 25 in German. Moreover, recognition experiments on Italian large vocabulary dictation conducted at IRST showed that only minor improvements are achieved with context-dependent (CD) phone models with respect to context-independent (CI) ones. German is characterized by a higher variety of flexions and cases, a large use of compounding words, and the usage of capital and small letters to specify role of words. All these features heavily reflect on the vocabulary size and on out-of-vocabulary rate, that are in general higher for German. For the German language, it can be said, that pronunciation and lexicon strongly depend on the region. South Tyrolean German uses different words and pronunciation rules than standard German. Moreover, the land register experts have either Italian or German mother language and may thus have an accent whenever they enter data in a non-native language. Therefore, the recognition system must not only cope with dialectal variations, but also with a certain amount of accent by the speaker. The architecture for SpeeData data entry module is shown in figure21. This comprises four modules, namely the central manager (CM), the user interface (UI), the data base interface (DBI) and the speech recognizer (SR).