¶ … Mining
The process of extracting new information from existing information through the use of computer system is called Text Mining. Text mining retrieves data of available information and establishes the connection between the facts mentioned in that data. This is how, new information is developed. Since it is newly formed information, its validation is conducted through experimentation. The process of web search is often confused with that of text mining, though these are two entirely different processes. In web search, the computers match the keywords in the database and bring the relevant records. The information is written down by somebody and then uploaded on the internet to make it searchable. On the other hand, in text mining, altogether new information is generated out of existing body of knowledge (Berry, 2004).
Text mining finds its roots in data mining. Data mining refers to the process in which the computer system retrieves unique information from the existing database. Hence text mining is also named as Text Data Mining. Other names for text mining are Intelligent Text Analysis and Knowledge-Discovery in Text (KDD). It extracts the interesting information out of unstructured text. Data mining from unstructured information has high value in the emerging field of text mining. It is because of readily availability of unstructured data and its large volume. Text mining enjoys the perception of high commercial value as more than 80% of the information is stored in the form of text and can be explored to generate new body of knowledge. In addition to data extraction, text mining includes computational linguistics, statistics and machine learning as well (Berry, 2004).
Knowledge Discovery from Database (KDD) is enjoying portion of eminence in the field of emerging applications, like Text Understanding. It works through extracting both implicit and explicit concepts from the existing data and then forming semantic relations among the concepts. It is done with the help of Natural Language Processing Techniques commonly known as NLP Techniques. KDD when combined with NLP discovers useful information though knowledge management, information extraction, machine learning, statistics and reasoning (Navathe et al., 2000).
As mentioned earlier, data mining and text mining are somewhat similar concepts. The only difference lies in the type of data explored and the tools used. Data mining works well with highly structured data only, while text mining is applicable for semi-structured or unstructured data as well. The unstructured data includes HTML files, full-text documents and emails. In this perspective, it becomes more preferable to the companies. But there is also an aspect which prevents the use of text mining. This hindrance is the dependence on NLP. It is because natural language was not meant for computer systems initially nor it is developed for this purpose. Because of this issue, structured data and data mining practices are more prevalent in the field of research and development (Navathe et al., 2000).
The obstacles posed by computers system in regard of NLP does not exist in case of human beings. The human beings can easily comprehend the language patterns and can even distinguish between the various ones applied in the same text. The examples are contextual meanings, the slangs and spelling variation in a database. The computer systems are not yet equipped with the capability of linguistic patterns identification quickly (Weiguo, 2005).
A collection of documents is provided to the text mining tool. After exploring them, it selects one particular document to identify its character set and format. After this phase, it starts analyzing the text mentioned in the document. It repeatedly applies various techniques to extract information from the database. The presented example quote three techniques of text analysis, however, there be many others based on the combination of these techniques. It basically depends upon the organizational goals, which provide guidelines about the data to be extracted. The retrieved data is inserted in the organizational management information systems so that the end users may retrieve it for their use (Weiguo, 2005).
Statement of the problem
There is a gap in the literature regarding the text information extraction from a huge database.
Purpose of the study
The study investigates how to extract a specific phrase from a text. It employs survey techniques to interview experts in the field and assesses results using coding techniques.
Rationale of the study
It is important to note that several research studies related to text extraction have been carried out. However, no research has focused on the evaluating text information extraction in large datbases...
4. Transparency, authenticity, and focus are good. Bland is bad. Many people are looking for someone who is in authority to share their ideas, experiences, or suggestions (Bielski, 2007, p. 9). Moreover, just as content analysis of other written and symbolic forms has provided new insights that might have otherwise gone unnoticed, the analysis of blog content may reveal some unexpected findings concerning hot topics and significant social trends that are
The heuristics that are considered are probabilistic machine learning approaches. Such an approach is the 'Alignment Conditional Random Fields' that is designed for a scoring sequence for undirected graphical models. (Bilenko; Mooney, 2005) There are demands for this type of software and there is a vast area of information analysis where text mining is beginning to get important. One field is in the analysis of literature and research reviews. Literary
Pollution From Mining Activities How serious is the pollution that results from mining activities? How clean are the coal mining activities in Kentucky, West Virginia, and other Appalachian areas where mountaintops are stripped away to get at the coal? What other mining activities cause pollution of the air, the land, and the waterways? This paper will delve into those mining activities and report the pollution that results from those strategies. The Pollution
Data Mining The amount of knowledge available in today's world is massive. The information technology specialist who's responsible to his or her organization for maximizing the capacity for practical usage of this knowledge, it is becoming increasingly difficult to have a total grasp of the problem. The purpose of this essay is to discuss the importance of implementing data warehousing and mining systems inside an organization. In order to do this,
Introduction Background The present-day economic development gives rise to a substantially greater magnitude of resettlement in comparison to ten years ago. In the past six decades, the worldwide magnitude of development-induced displacement and resettlement has fully-fledged to an approximated 250 million to over 400 million people (Terminski, 2012). Across the globe, development projects have resulted in approximately 15 million people facing displacement on an annual basis (Van der Ploeg and Vanclay, 2017).
Data Mining Determine the benefits of data mining to the businesses when employing: Predictive analytics to understand the behaviour of customers "The decision science which not only helps in getting rid of the guesswork out of the decision-making process but also helps in finding out the perfect solutions in the shortest possible time by making use of the scientific guidelines is known as predictive analysis" (Kaith, 2011). There are basically seven steps involved
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