Building Search Applications: Lucene, LingPipe, and Gate by Manu Konchady

By Manu Konchady

Lucene, LingPipe, and Gate are renowned open resource instruments to construct robust seek purposes. construction seek purposes describes services from Lucene that come with indexing, looking, rating, and spelling correction to construct se's. Use LingPipe and Gate to discover the which means of textual content to make seek functions extra priceless.

With this ebook you'll discover ways to:

- Extract tokens from textual content utilizing customized tokenizers and analyzers from Lucene, LingPipe, and Gate.
- build a seek engine index with an not obligatory backend database to control huge rfile collections.
- discover the wide variety of Lucene queries to go looking an index, comprehend the rating set of rules for a question, and recommend spelling corrections.
- locate the names of individuals, areas, and different entities in textual content utilizing LingPipe and Gate.
- Categorize records via subject utilizing classifiers and construct teams of self-organized files utilizing clustering algorithms from LingPipe.
- Create an internet crawler to test the net, Intranet, or computer utilizing Nutch.
- song the sentiment of articles released on the net with LingPipe
- notice plagiarism of records utilizing a registered record collection.

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Additional resources for Building Search Applications: Lucene, LingPipe, and Gate

Example text

Email is also legally discoverable, which simply means that in a lawsuit a litigant could ask for all emails exchanged over a year or longer. Collecting this information and providing it can be expensive unless there was an email management system in place. In some cases, permanently deleting emails may not be viable since copies may exist in the recipient’s server. It may also imply a potential cover up of evidence. The benefits of email are universally accepted, but like any other application, it can be used for malicious purposes.

You can customize this list depending on your requirements. For example, if you have a large patent database, then terms such as - invention, claim, and herein may be additional stop words that are added to the standard stop word list. These stop words can be provided to the analyzer using a file or a list of words. Stop words are selected based on the utility of such words in a query. Words that appear in practically every document in a collection will have little value in a search engine index.

The collected information needs to be concisely summarized for developers to build a better version of the product. Manual scanning of complaints does not always reveal a hidden pattern in the text. These patterns can only be seen when the entire collection of the text is summarized and presented in some visual form. It is tedious (and impractical) for an individual to scan a large volume of text looking for particular terms or phrases to build a global view of the set of complaints. In this particular application, a plot or visual chart that accompanies the text analysis will quickly reveal the reasons for most complaints.

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