Academic journal article Library Philosophy and Practice

A Study on Adapting Natural Language Processing for Library Services Delivery

Academic journal article Library Philosophy and Practice

A Study on Adapting Natural Language Processing for Library Services Delivery

Article excerpt

Objectives

1. To study the need for adaption of technologies in the delivery of library services.

2. To study the key aspects and features of Natural Language Processing Tools and how their adaption will be good for libraries.

3. To suggest an interface for existing library solutions to integrate with NLP solutions for the delivery of library services

4. To provide a brief overview of the existing Open Source NLP Tools presently available and widely used in production.

Review of Literature

Gobinda G. Chowdhury (2000) Natural Language Processing (NLP) is an area of research and application that explores how computers can be used to understand and manipulate natural language text or speech to do useful things. NLP researchers aim to gather knowledge on how human beings understand and use language so that appropriate tools and techniques can be developed to make computer systems understand and manipulate natural languages to perform the desired tasks. The foundations of NLP lie in a number of disciplines, viz. computer and information sciences, linguistics, mathematics, electrical and electronic engineering, artificial intelligence and robotics, psychology, etc. Applications of NLP include a number of fields of studies, such as machine translation, natural language text processing and summarization, user interfaces, multilingual and cross language information retrieval (CLIR), speech recognition, artificial intelligence and expert systems, and so on.

Jeonghee Yi, Tetsuya Nasukawa, Razvan Bunescu & Wayne Niblack (2003) We present Sentiment Analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document about a subject, SA detects all references to the given subject, and determines sentiment in each of the references using natural language processing (NLP) techniques. Our sentiment analysis consists of 1) a topic specific feature term extraction, 2) sentiment extraction, and 3) (subject, sentiment) association by relationship analysis. SA utilizes two linguistic resources for the analysis: the sentiment lexicon and the sentiment pattern database. The performance of the algorithms was verified on online product review articles ("digital camera" and "music" reviews), and more general documents including general web pages and news articles.

MIT Press (2000) Increasingly businesses, government agencies and individuals are confronted with large amounts of text that are critical for working and living, but not well enough understood to get the enormous value out of them that they potentially hide. At the same time, the availability of large text corpora has changed the scientific approach to language in linguistics and cognitive science. Phenomena that were not detectable or seemed uninteresting in studying toy domains and individual sentences have moved into the center field of what is considered important to explain. Whereas as recently as the early 1990s quantitative methods were seen as so inadequate for linguistics that an important textbook for mathematical linguistics did not cover them in any way, they are now increasingly seen as crucial for linguistic theory. In this book we have tried to achieve a balance between theory and practice, and between intuition and rigor.

Technological Advancements - Current Scenario

We live in a time where technologies are dynamically changing and fast eliminating their earlier siblings. Change has become the order of the day and anyone who doesn't innovate must necessarily hang his boot. This is prominently in service based industries/sectors. This rapid updating phenomena has created a user population whose demands too are constantly on the rise, and are easily unsatisfied with static versions of things which either fail to update preferring their constant mode, or their pace of updations are slow. While product developers or the service providers reel under the illusions that their products/services are free of any glitches and are offering best user experiences, the actual users will be just waiting for their time to switch to other similar products/services which not only offer their expected performance levels but also provide the latest state-of-the-art technological implementations. …

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