Improving Accuracy of Answer Extraction in Question-Answer System – Research Paper Example

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The paper "Improving Accuracy of Answer Extraction in Question-Answer System" is a great example of a research paper on logic and programming. Information technology has grown to the level that requires retrieving information from the Internet necessary. This is because the entire world is accessing information through the Internet, and the information is understood by human beings. Retrieving this information from a search engine such as yahoo and Google requires users who have adequate knowledge on a topic before searching. This limits individuals who have little knowledge of a certain title, thus requiring an accurate way of extracting an answer from such engines.

The current Question answering system (QA system) is a dedicated structure of information retrieval, and when offered with a set of documents, it tries to recover exact answers to questions fronted in the natural idiom. According to Zhenqiu (2012), the Open-domain question response needs question-answering systems to answer questions concerning every imaginable matter, and such systems cannot depend on the hand-designed domain for definite comprehension to stumble on and figure out the accurate answers. Besides that, several QA System has been employed, and diminutive effort has been made on the establishment of an assessment paradigm for them. QA system helps in retrieving information containing documents but does not provide means of locating the exact passages within the documents thus leaving the user with the task of extracting them.

This is a tedious exercise if a number of documents supplied are taken into account. Thus, it is essential to improve the way information is retrieved by reducing the number of documents and text that the user receives. Therefore having the most relevant extracted QA system needs to be improved.

There have been developments that are closer to providing accurate information retrieval systems like Corpus-based question answering, however, they have some shortcomings. In the system the find data or information in the form of natural language which is through the use of surface patterns or lexico-syntactic patterns to extract information from corpus offline.   Figure 1: General Architecture of Question Answering System Therefore the system to be developed here is intended to assist researchers to extract accurate information for their queries. This will combine the semantic-based search engine and accurate answer extraction system which will help in an accurate Question – answering system.

The system should have the ability to include the extraction mechanism and answer extraction since they are interrelated. The isolation of answer extraction and mechanism of extraction will not improve accuracy as expected because they are inseparable thus the need for a new system that will provide improvement is required. Research Field There are many fields of study relating to the QA system and they include query analysis, answer extraction, and information retrieval.

Query analysis involves the processing of information input in a natural language that is acceptable by the system to classify the information needed from the question format. Once the information has been queried, it will be retrieved from the system in a form of answers using information retrieval components into target collection. Yen, Wu, and Yang (2013) reviewed the existing QA system paradigms to review the status of comprehension with regard to comprehending user acceptance of novel information technologies. The procedure of searching answers to a natural language question has been presumed customarily to be in three different phases: first, question processing, which involves the acknowledgment of the information that is required from the question query (Hickl 1265).

Secondly, passage retrieval, which involves the retrieval of relevant information text from definite phrases and keywords mined from the passage of a question, and thirdly, extract parallel text to a question’ s precise answer. Huang and Yao (2004)argue that this is not the merely applicable model for factoid question answering (QA). As an alternative of extracting answers from groups of retrieved texts, the review paper brings in a new model that discerns precise answers to factoid questions by balancing a system of question-answer pairs, which correspond to all of the provided questions and answers that can be gained from a certain text collection.

The review paper analyzes available literature to provide an insight on the developed novel paradigm of answer extraction, which computes the value of candidate answers not just concerning a group of attributes mined from a question, but in the enormous perspective of the information entailed in the corpus all together.

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