Text Mining Essay Example

staff with a single click. Our aim is to improve public access to information by making it more fluid and accessible for political and current events while not limited to this area.

An information mash involves gathering subject-specific data from various sources to create a more comprehensive and beneficial outcome. The process of combining information from multiple sources is essential and valuable. Conversely, an enhanced information mash includes extra steps in the data processing, such as categorizing, indexing, and other techniques. This guarantees that new information is included in the merged data set, which links items from different origins. Furthermore, the creator of the information mash may also contribute unique details not obtainable elsewhere.

Advanced software technologies are often employed to add value, such as language translation, information extraction [JM02], associative indexing and retrieval [LD97], word pattern visualization [DKP00], data mining techniques, text mining techniques [WIZD05], and literature-based discovery (also known as knowledge discovery) techniques [GLF02]. Faceted classification can also be utilized (http://www.kmconnection.com/DOC100100).

Various tools exist for analyzing information, including HTML-based tools like concordances and [BK02]. When it comes to their uses and applications, a more detailed discussion would be appropriate elsewhere. However, the main concept behind these tools is association. An information mashup helps identify significant associations (or lack thereof) among various information items such as data or documents.

Investigators are not only looking for relevant records, but also patterns of information that can be identified through associations between different pieces of information. The Sunlight Foundation is specifically interested in examples of information mashing that demonstrate these patterns. [DKP00] offers a discussion on the comparison between record-oriented and pattern-oriented information retrieval.

Company A sells real

estate to Company B, which promptly sells it to Company C at a significant profit. However, both Company A and Company C are connected to Company D, a defense contractor that has just received a sizable contract. Furthermore, Company B is partially owned by a Congressman with strong ties to the Department of Defense. Similar circumstances led to the conviction and imprisonment of Congressman 'Duke' Cunningham, but he is not the only one who has been caught committing such crimes in history. Cunningham's offense was uncovered, or at least conjectured, through publicly available information. The records of the property sales are accessible to the public.

Public information regarding the contracting records of companies is readily available, as well as extensive details on government officials like Congressmen. By using an information mash, users can access multiple sources to uncover interesting connections. Commercial examples include IP (intellectual property) placement.

According to http://news.bc.co.uk/2/hi/science/nature/4191737, a company has obtained a patent for a type of plastic made entirely from renewable resources.

The firm is seeking to license its IP and is looking for partners or ways to use it. To locate relevant information, descriptors such as 'biodegradable', 'water resistant', 'made from biological materials', 'used in kitchenware', and 'orange peels' will be utilized. This information can be found in various documents such as patents, patent applications, SEC filings, newspaper reports, corporate annual reports, general web documents, as well as in internal technical and marketing studies.

The process of IP placement requires creativity and cannot be completely automated. However, an information mash can aid in the process by quickly producing relevant information from various sources. Additionally, investment analysis requires a wide range of information

with predictive value, although the specific information needed is not yet clear.

The available sources of useful information are varied and diverse. These sources include market data on financial instruments (such as stocks and bonds), regulatory filings, patent filings, annual reports, company-generated data and documents, general news stories, and third-party index or rating information. For example, sustainability ratings from organizations like Social Accountability International (http://www.sa-intl.org/) and the International Organization for Standardization (http://www.iso.org/) are commonly used.

ISO.org/iso/en/ISOOnline.frontpage provides the potential for material assistance in identifying hidden opportunities and risks. While this concept of a more robust information mashup is not entirely new, few vaims currently exist in the expansive form described here.

Although there is a lot of activity in the related fields of research, development, and deployment, web mashups are particularly prevalent and constantly emerging. A digital library (available at http://www.dlib.) is perhaps the most closely linked established concept.

Considering the website, org/, it can be compared to a vaim (or information mash) that serves as a digital form of publishing, similar to the Annenbergs' TV Guide and racing forms from the past. Such publishing involves collecting information and improving the overall offering. Moving ahead, there are four main areas to focus on: development, experimentation, and utilization.

The text discusses various aspects related to an envisioned system. It raises questions about the system's prospective use, the topic that would require attention, the intended users, and the value proposition for the system. Additionally, the text inquires about the type of information that would be aggregated and its content, availability, and usability for the intended purpose. The success and applicability of the system are left as open questions.

What methods of

recovery will be utilized? In what ways will value be added beyond simple information aggregation? How will relationships between the collected information items be established and displayed? Finally, how can costs be recuperated? Social activists, art, sports, and cat enthusiasts, as well as researchers, may have the motivation and ability required to construct and maintain a virtual archive. These people may find publication and recognition sufficient, especially with financial backing from government or private entities (see the Sunlight Foundation mentioned above).

Three main business models have emerged for generating profits: (a) selling advertising, (b) selling subscriptions, and (c) utilizing an open-source approach where the system is offered freely and services related to using it are sold. References [BK02] David C. Blair and Steven O. Kimbrough, "Exemplary documents: a foundation for information retrieval design," Information Processing and Management 38 (2002), no. 3, 363–379, and [DKP00] Garett O.

The Journal of the American Society for Information Science published an article titled "On pattern-directed search of archives and collections" by Steven O. Kimbrough, Chuck Patch, and Dworman in 2000 (volume 51, issue 1, pages 14-23). Additionally, the ACM Transactions on Internet Technology published an article titled "Literature-based discovery on the World Wide Web" by Michael Gordon, Robert K. Lindsay, and Weiguo Fan in 2002 (volume 2, issue 4, pages 261-275).The text, which includes and their contents, can beand unified as follows:

In 2002, John Benjamins Publishing Company in Amsterdam, The Netherlands and Philadelphia, USA published a book titled "Natural Language Processing for Online Applications: Text Retrieval, Extraction and Categorization" by Peter Jackson and Isabelle Moulinier. In the same year, Psychological Review featured an article titled "A Solution to

Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge" by Thomas K. Landauer and Susan T. Dumais.

The text presents a citation for a book on text mining authored by Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, and Fred J. Damerau and published by Springer in New York in 2005. The citation includes a reference number (5) and an identification code ($Id: vaim-faqs). The text is enclosed in a paragraph HTML tag.The following HTML code displays version information:

tex,v 1. 6 2006/05/10 21:45:07 sok Exp $ 6