Sonali Narang
Computer Applications
Department
sonalinarang9@gmail.com
There
have been high hopes for Natural Language Processing. Natural Language
Processing, also known simply as NLP, is part of the broader field of Artificial
Intelligence, the effort towards making machines think. Computers may appear
intelligent as they crunch numbers and process information with blazing speed.
In truth, computers are nothing but dumb slaves who only understand on or off
and are limited to exact instructions. But since the invention of the computer,
scientists have been attempting to make computers not only appear intelligent
but are intelligent. A truly intelligent computer would not be limited to rigid
computer language commands, but instead be able to process and understand the
English language. This is the concept behind Natural Language Processing. The
phases a message would go through during NLP would consist of message, syntax,
semantics, pragmatics, and intended meaning. (M. A. Fischer,1987) Syntax is the
grammatical structure. Semantics is the literal meaning. Pragmatics is world
knowledge, knowledge of the context, and a model of the sender. Alan Turing
predicted of NLP in 1950 (Daniel Crevier, 1994, page 9):"I believe that in
about fifty years' time it will be possible to program computers to make them
play the imitation game so well that an average interrogator will not have more
than 70 per cent chance of making the right identification after five minutes
of questioning."
But in 1950, the current computer technology was limited. Because of these limitations, NLP programs of that day focused on exploiting the strengths the computers did have. For example, a program called SYNTHEX tried to determine the meaning of sentences by looking up each word in its encyclopedia.
But in 1950, the current computer technology was limited. Because of these limitations, NLP programs of that day focused on exploiting the strengths the computers did have. For example, a program called SYNTHEX tried to determine the meaning of sentences by looking up each word in its encyclopedia.
OBJECTIVES
The goal of the Natural
Language Processing (NLP) group is to design and build software that will
analyze, understand, and generate languages that humans use naturally, so that
eventually you will be able to address your computer as though you were
addressing another person.
This goal is not easy to
reach.
"Understanding" language means,
among other things, knowing what concepts a word or phrase stands for and
knowing how to link those concepts together in a meaningful way. It's ironic
that natural language, the symbol system that is easiest for humans to learn
and use, is hardest for a computer to master.
Long after machines have
proven capable of inverting large matrices with speed and grace, they still
fail to master the basics of our spoken and written languages.
Instructional
Objective
How an intelligent system can be
develop. First step that student must understand is the necessity and
Ambiguities in NLP processing, understanding difference between natural and
formal language and processing the former,
steps
involved in natural language understanding, required information i.e., syntax,
semantics, world-knowledge, phonology, morphology and Basic language operation
such as semantics processing, knowledge representation, parts-of-speech
tagging, Morphology analysis.
METHODOLOGY
Medical language processing (MLP) systems that codify
information in textual patient reports have been developed to help solve the
data entry problem. Some systems have been evaluated in order to assess
performance, but there has been little evaluation of the underlying technology.
Various methodologies are used by the different MLP systems but a comparison of
the methods has not been performed although evaluations of MLP methodologies
would be extremely beneficial to the field. This paper describes a study that
evaluates different techniques. To accomplish this task an existing MLP system
Med LEE was modified and results from a previous study were used. Based on
confidence intervals and differences in sensitivity and specificity between
each technique and all the others combined, the results showed that the two
methods based on obtaining the largest well-formed segment within a sentence
had significantly higher sensitivity than the others by 5% and 6%. The method
based on recognizing a complete sentence had a significantly worse sensitivity
than the others by 7% and a better specificity by .2%. None of the methods had
significantly worse specificity.
MAJOR TASKS IN NLP
Following is
a list of NLP task some of which has direct real-world application, and some
are used to aid in solving larger tasks. These tasks are different from other
potential and actual NPL task because of the volume of research devoted to
these tasks, problem setting, standard metric, corpora to evaluate task and
competition devoted are defined for each specific task.
Natural
language understanding, generation etc
THE FUTURE IN NLP
Human-level
natural language processing is an AI-complete problem. That is, it is equivalent to solving the central artificial
intelligence problem—making computers as intelligent as people, or strong AI. NLP's future is therefore tied closely to the development of AI
in general.
As natural
language understanding improves, computers will be able to learn from the
information online and apply what they learned in the real world. Combined with
natural language generation, computers will become more and more capable of
receiving and giving instructions.
CONCLUSION
This
goal is not easy to reach. "Understanding" language means, among
other things, knowing what concepts a word or phrase stands for and knowing how
to link those concepts together in a meaningful way. It's ironic that natural
language, the symbol system that is easiest for humans to learn and use, is
hardest for a computer to master. Long after machines have proven capable of
inverting large matrices with speed and grace, they still fail to master the
basics of our spoken and written languages.
The
challenges we face stem from the highly ambiguous nature of natural language.
As an English speaker you effortlessly understand a sentence like "Flying
planes can be dangerous". Yet this sentence presents difficulties to a
software program that lacks both your knowledge of the world and your
experience with linguistic structures. Is the more plausible interpretation
that the pilot is at risk, or that the danger is to people on the ground?
Should "can" be analyzed as a verb or as a noun? Which of the many
possible meanings of "plane" is relevant? Depending on context,
"plane" could refer to, among other things, an airplane, a geometric
object, or a woodworking tool. How much and what sort of context needs to be brought
to bear on these questions in order to adequately disambiguate the sentence?
We
address these problems using a mix of knowledge-engineered and
statistical/machine-learning techniques to disambiguate and respond to natural
language input. Our work has implications for applications like text
critiquing, information retrieval, question answering, summarization, gaming,
and translation. The grammar checkers in Office for English, French, German,
and Spanish are outgrowths of our research; Encarta uses our technology to retrieve
answers to user questions; Intellishrink uses natural language technology to
compress cellphone messages; Microsoft Product Support uses our machine
translation software to translate the Microsoft Knowledge Base into other
languages. As our work evolves, we expect it to enable any area where human
users can benefit by communicating with their computers in a natural way.
REFERENCES
·
Munmun De
Choudhury, Scott Counts, Eric Horvitz, and Michael Gamon, Predicting
Depression via Social Media., AAAI, July 2013
·
Michael Gamon,
Martin Chodorow, Claudia Leacock, and Joel Tetreault, Grammatical
Error Detection in Automatic Essay Scoring and Feedback, in Handbook of Automated Essay Evaluation,
Routledge, May 2013
·
Munmun de
Choudhury, Michael Gamon, Aaron Hoff, and Asta Roseway, "Moon
Phrases": A Social Media Facilitated Tool for Emotional Reflection and
Wellness., European Alliance for
Innovation, May 2013
·
Hassan Sajjad,
Patrick Pantel, and Michael Gamon, Underspecified
Query Refinement via Natural, ACL/SIGPARSE, December 2012
·
Patrick
Pantel, Thomas Lin, and Michael Gamon, Mining
Entity Types from Query Logs via User Intent Modeling, Association for Computational Linguistics, July
2012
·
Munmun De
Choudhury, Scott Counts, and Michael Gamon, Not All
Moods are Created Equal! Exploring Human Emotional States in Social Media., Association for the Advancement of Artificial
Intelligence, June 2012
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