Java Reference
In-Depth Information
Why use NLP?
NLP is used in a wide variety of disciplines to solve many different types of problems. Text
analysis is performed on text that ranges from a few words of user input for an Internet
query to multiple documents that need to be summarized. We have seen a large growth in
the amount and availability of unstructured data in recent years. This has taken forms such
as blogs, tweets, and various other social media. NLP is ideal for analyzing this type of in-
formation.
Machine learning and text analysis are used frequently to enhance an application's utility. A
brief list of application areas follow:
Searching : This identifies specific elements of text. It can be as simple as finding
the occurrence of a name in a document or might involve the use of synonyms and
alternate spelling/misspelling to find entries that are close to the original search
string.
Machine translation : This typically involves the translation of one natural lan-
guage into another.
Summation : Paragraphs, articles, documents, or collections of documents may
need to be summarized. NLP has been used successfully for this purpose.
Named Entity Recognition ( NER ): This involves extracting names of locations,
people, and things from text. Typically, this is used in conjunction with other NLP
tasks such as processing queries.
Information grouping : This is an important activity that takes textual data and
creates a set of categories that reflect the content of the document. You have prob-
ably encountered numerous websites that organize data based on your needs and
have categories listed on the left-hand side of the website.
Parts of Speech Tagging ( POS ): In this task, text is split up into different gram-
matical elements such as nouns and verbs. This is useful in analyzing the text fur-
ther.
Sentiment analysis : People's feelings and attitudes regarding movies, books, and
other products can be determined using this technique. This is useful in providing
automated feedback with regards to how well a product is perceived.
Answering queries : This type of processing was illustrated when IBM's Watson
successfully won a Jeopardy competition. However, its use is not restricted to win-
ning game shows and has been used in a number of other fields including medi-
cine.
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