A Survey of Text Mining Techniques and Applications <s> H. Question Answering <s> The Web has emerged as a huge information repository that can be used for various knowledge based applications, an important one being question answering (QA). The paper discusses the issues involved in using Web as a knowledge base for question answering involving simple factual questions. It also proposes some simple but effective methods to adapt traditional QA methods to deal with efficient these issues and lead to an accurate and efficient question answering system. The paper discusses the architecture and design of our system. The empirical results obtained on the current system are encouraging. <s> BIB001 </s> A Survey of Text Mining Techniques and Applications <s> H. Question Answering <s> This paper presents a Passage Retrieval-based approach for the development of spoken Question Answering systems. Question Answering can be seen as a particular aspect of Information Retrieval where the user's information need are not satisfied by means of a document, but by a portion of text. Currently, the performance of typical Question Answering systems is rather poor, if compared with other Information Retrieval tasks, however, it is predictable that these system will be used in combination with some speech interface when high precision will be achieved. The most important evaluation competitions for Question Answering, such as TREC and CLEF, still do not have a spoken Question Answering track, therefore this work presents a novel approach to this task in order to study the influence of recognition errors over Question Answering systems. <s> BIB002
Another application area of natural language processing is natural language queries, or question answering (Q&A), which deals with how to find the best answer to a given question. Many websites that are equipped with question answering technology, allow end users to "ask" the computer a question and be given an answer. Q&A can utilize multiple text mining techniques. For example, it can use information extraction to extract entities such as people, places, events; or question categorization to assign questions into known types (who, where, when, how, etc.). In addition to web applications, companies can use Q&A techniques internally for employees who are searching for answers to common questions. The education and medical areas may also find uses for Q&A in areas where there are frequently asked questions that people wish to search. Figure14 shows the architecture of question answering system. BIB001 The system takes in a natural language (NL) question in English from the user. This question is then passed to a Part-of-Speech (POS) tagger which parses the question and identifies POS of every word involved in the question. This tagged question is then used by the query generators which generate different types of queries, which can be passed to a search engine. These queries are then executed by a search engine in parallel. The search engine provides the documents which are likely to have the answers we are looking for. These documents are checked for this by the answer extractor. Snippet Extractor extracts snippets which contain the query phrases/words from the documents. These snippets are passed to the ranker which sorts them according to the ranking algorithm. In QUASAR BIB002 System, user provides a question and this is handed over to the Question Analysis and Passage Retrieval modules. Next, the Answer Extraction obtains the answer from the expected type, constraints and passages returned by Question Analysis and Passage Retrieval modules. The architecture is shown in figure15. The main objective of question analysis module is to derive the expected answer type from the question text. This is a crucial step of the processing since the Answer Extraction module uses a different strategy depending on the expected answer type. Another operation performed by this module is to analyze the query with the purpose of identifying the constraints to be used in the AE phase. These constraints are made by sequences of words extracted from the POS-tagged query by means of POS patterns and rules. For instance, any sequence of nouns (such as "ozone hole") is considered as a relevant pattern. In Passage Retrieval module, user question is handed over also to the JIRS Passage Retrieval system, more specifically to its Search Engine and N-grams Extraction components. Passages with the relevant terms (i.e., without stop words) are found by the Search Engine using the classical IR system. Sets of 1-grams, 2-grams,. . ., ngrams are extracted from the extended passages and from the user question. In both cases, n will be the number of question terms. A comparison between the n-gram sets from the passages and the user question is done in order to obtain the weight of each passage. The weight of a passage will be heavier if the passage contains greater ngram structures of the question. The input of answer extraction module is constituted by the n passages returned by the PR module and the constraints (including the expected type of the answer) obtained through the Question Analysis module. A Text Crawler is instantiated for each of the n passages with a set of patterns for the expected type of the answer and a pre-processed version of the passage text. The pre-processing of passage text consists in separating all the punctuation characters from the words and in stripping off the annotations of the passage. It is important to keep the punctuation symbols because we observed that they usually offer important clues for the individuation of the answer: The Text Crawler begins its work by searching all the passage's substrings matching the expected answer pattern. Then a weight is assigned to each found substring s, depending on the positions of the constraints, if s does not include any of the constraint words. The filter module takes advantage of some knowledge resources, such as a mini knowledge base or the web, in order to discard the candidate answers which do not match with an allowed pattern or that do match with a forbidden pattern. For instance, a list of country names in the four languages has been included in the knowledge base in order to filter country names when looking for countries. When the filter rejects a candidate, the Text Crawler provide it with the next best weighted candidate, if there is one. Finally, when all Text Crawlers end their analysis of the text, the Answer Selection module selects the answer to be returned by the system.