The Role Of Basal Ganglia Of Language Processing And...

The Role Of Basal Ganglia Of Language Processing And...
Role of basal ganglia in language processing and comprehension
Xue Fan Wang
ID: 250679376
Motor Neurophysiology 4630B
Instructor: Dr. Stefan Everling
In the early 1900s, English physician Thomas Willis discovered a functional structure lying deep
inside the forebrain. Through anatomical studies in patients with severe chorea and movement
deficit, he found that damage to this particular subcortical structure was very common among these
patients. He then termed the structure "corpus striatum" (the largest component of the basal ganglia)
and linked its functional role with motor control (Parent, 2012). However, Willis also noticed that
subcortical–lesion–associated motor deficit is often accompanied by various degrees of cognitive
impairment. Despite such clinical observations, research emphasis was directed mainly toward the
role of basal ganglia in motor control (Middleton & Strick, 2000). With increasing knowledge of the
anatomical structures of the basal ganglia and its connectivity with cerebral cortex, scientists
revealed several parallel loops between the basal ganglia and multiple non–motor regions of the
brain, such as the prefrontal cortex and temporal cortex. Such anatomical findings, along with the
clinical observations from Parkinson's disease (PD) patients, demonstrated that the basal ganglia
might play a critical role in cognitive function (Alexander et al., 1986; Middleton & Strick, 1994,
1996). Language processing and comprehension is one of the
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Dynamic News Classification Using Machine Learning
Dynamic News Classification using Machine Learning Introduction Why this classification is
needed ? (Ashutosh) The exponential growth of the data may lead us to a time in future where huge
amount of data would not be able to be managed easily. Text Classification is done through Text
Mining study which would help sorting the important texts from the content or a document to
manage the data or information easily. //Give a scenario, where classification would be mandatory.
Advantages of classification of news articles (Ayush) Data classification is all about tagging the data
so that it can be found quickly and efficiently.The amount of disorder data is increasing at an
exponential rate, so if we can build a machine model which can automatically classify data then we
can save time and huge amount of human resources. What you have done in this paper (all) Related
work In this paper [1] , the author has classified online news article using Term Frequency–Inverse
Document Frequency (TF–IDF) algorithm.12,000 articles were gathered & 53 persons were to
manually group the articles on its topics. Computer took 151 hours to implement the whole
procedure completely and it was done using Java Programming Language.The accuracy of this
classifier was 98.3 % . The disadvantages of using this classifier was it took a lot of time due to
large number of words in the dictionary. Sometimes the text contained a lot of words that described
another category since the

Text Analytics And Natural Language Processing
IV. SENTIMENT ANALYSIS A. The Sentiment analysis process i) Collection of data ii)
Preparation of the text iii) Detecting the sentiments iv) Classifying the sentiment v) Output i)
Collection of data: the first step in sentiment analysis involves collection of data from user. These
data are disorganized, expressed in different ways by using different vocabularies, slangs, context of
writing etc. Manual analysis is almost impossible. Therefore, text analytics and natural language
processing are used to extract and classify[11]. ii) Preparation of the text : This step involves
cleaning of the extracted data before analyzing it. Here non–textual and irrelevant content for the
analysis are identified and discarded iii) Detecting the sentiments: All the extracted sentences of the
views and opinions are studied. From this sentences with subjective expressions which involves
opinions, beliefs and view are retained back whereas sentences with objective communication i.e
facts, factual information are discarded iv) Classifying the sentiment: Here, subjective sentences are
classified as positive, negative, or good, bad or like, dislike[1] v) Output: The main objective of
sentiment analysis is to convert unstructured text into meaningful data. When the analysis is
finished, the text results are displayed on graphs in the form of pie chart, bar chart and line graphs.
Also time can be analyzed and can be graphically displayed constructing a sentiment time line with
the chosen

How Does Language Phonotactics Affect Phonological...
p While many previous studies have focused on the production and/or perception of the
epenthesized consonant clusters of non–words either word initially or word finally as a measure to
test how language phonotactics affect phonological processing regardless of the speaker's familiarity
with the target language (Davidson 2006, Davidson, 2010, Fleischhacker, 2001, Tily and Kuperman,
2010, Dupoux, 1998) , none have focused on the role that speaker's experience might play in both
perception and production together in relation to the epenthetic vowel word initially and word
finally at the same time. These studies fall basically on four basic categories: The production and/or
perception of the initial epenthesized consonant clusters ( e.g Silveira, 2000, e.g Broselow 1992;
Kiparsky 2003; Watson 2007) and the production and/or perception of the final consonant clusters
(Kuijpers and van Donselaar (1997), Awbery 1984, Clements 1986; Ní Chiosáin 1995; Bosch and de
Jong 1998). While, superficially, the JA consonant clusters seem to be composed of two consecutive
consonants (CC), there is dialectological and phonetic evidence that shows that an additional vowel
(an epenthetic vowel) arises in between the consonants, resulting in sequences such as gv r, gv l, cv
r, etc. A recent study by Samah Salem tested the production of S+C consonant clusters by 20 highly
educated Levant Arabic ( from which JA dialect decends) speakers living in Canada. It showed that
participants did not

The Natural Language Processing ( Nlp )
INTRODUCTION Natural Language Processing (NLP): NLP is a process in which human makes
communication with machine easily. NLP is related to area of human and computer interaction.
There are many application developed last few years. The very helpful application is that a machine
take instruction by human voice and follow operation on it. NLP are trying to make computer more
reliable that are easier to use by people. So rather than learn a special language of computer
command, people will talk with computer in their own language. 1.1 MAJOR TASK IN NLP [b]:
1.1.1 Automatic Summarization [b]: It work as the summary of a chunk of text. Like, article in
political section in newspaper. 1.1.2: Machine translation [b]: Automatically translate language from
one human to another human. 1.1.4 Optical Character Recognition [b]: Optical Character
Recognition is a process mechanical or electronic conversion of scanned or photographic image of
typewritten or printed text into machine encode or computer readable text. 1.1.4 Speech recognition
[b]: In speech recognition take a sound clip of the person and determine the textual representation of
speech. 1.1.5 Speech segmentation [b]: In speech segmentation take a sound clip on the person and
separate into words. 1.1.6 Word segmentation [b]: Word segmentation takes the chunk of continuous
text and separate words. 1.2 APPLICATION OF NLP [b]: Classify text into categories Index and
search large text Question answering Speech

State of the Art in Multilingual Text Retrieval Accessing...
In this paper we surveyed the state of the art in multilingual text retrieval accessing parallel web
pages. Multilingual search engines typically consist of a crawler which traverses the web, retrieves
the required web page in the desire languages. It provides front end user interface, which can be
used for selecting language for query submission. The way the query is fired leads into two types of
search Cross–language information retrieval and Multi–language information retrieval. In cross–
language retrieval the user query is machine translated into multiple language queries automatically
as per user selection and then fired. In multi–language retrieval the user has to provide queries in
multiple languages to fetch the web documents in different languages. Also in some search engine
the web page is machine translated and forwarded to the user. NLP is still in the expansion phase
and has to make advances in it, research is going on all over world. Experiment was done with top
rated multilingual search engine to access parallel pages but could not find it. When billions of
parallel web documents are present on the web in different languages why not explore that? A
alternative to that can be searching through parallel pair finder.
Keywords: NLP,MT Machine Translation, CLIR Cross–language, information retrieval,
Multilingual.
1. Introduction
The total number of languages in the world is between 5,000 and 10,000. In a country like India
people speak about 57 different

Sentiment Analysis : The Language Processing
Sentiment Analysis Social opinion has been analysed using sentiment analysis (SA). This is
basically a natural language processing (NLP) application that uses computational linguistics and
text mining to identify text sentiments as positive, negative and neutral. This technique is known as
emotional polarity analysis which is related to text mining field, opinion mining and review mining.
In addition, to calculate sentiment score, the sentiment acquired from the text is compared to a
dictionary in order to determine the strength of that sentiment. Studies on sentiment analysis focus
on text written in English such as sentiment lexicons while applying this to other languages will
cause domain adaptation problem [12]. Traditional text classification is different from sentiment
classification. Traditional text classification refers to pre–defined class to determine a document's
category, and it gauges the theme of the text itself, while the main aim of the latter is to determine
the attitudes and opinion through mining and analysing user interest or other subjective information
[13]. Studies in sentiment analysis have found that pre–processing the data is the procedure of
cleaning and adapting. Data mining discovery is applied to identify patterns in data. Likewise, text
mining looks for patterns in text. Text mining can work and analyse the unstructured data such as
PDF files, emails and XML files [14]. Many researchers have argued that the use of classifiers in
tweets,

Questions On Natural Language Processing
Introduction
1.1 Inspiration and Outline
Today in the world of information technology everyone is using internet to share and access data.
But the data is in the form of natural language. And as we all know that all natural languages have
basic feature that it have ambiguity. It is something which can be understand in two or more ways,
and that depends on what situation it occurs. There are different types of ambiguities present in
natural languages like lexical ambiguity, structural or grammatical ambiguity, ambiguity of scope,
pragmatic ambiguity etc. Among all these ambiguities, lexical ambiguity is generally present in
natural languages. This ambiguity can be defined as the ambiguity which occurs because a word has
various meaning. So, to use information technology in best way we need to eliminate ambiguity
from the sentences with the help of tool called word sense disambiguation.
Word Sense Disambiguation (WSD) is a tool which computes the correct sence of ambiguious word
in context which it occur. It is a main challenge in Natural Language Processing(NLP) and it is
considered an AI– complete problem. For example consider the sentence–। and ;g ,d vke ckr gSA
vke Qyks dk jktk gSA Here, the ambiguous word is vke which can be interpreted as 'common' or as
'mango' based on the situation in which it is said.
The problem of recognition of definite sense of given word seems to be simple. As human being
simply identify the meaning of a word given on a given context. But

Natural Language Processing ( Nlp )
CHAPTER1: INTRODUCTION
Natural Language Processing (NLP) deals with actual text element processing. The text element is
transformed into machine format by NLP. Artificial Intelligence (AI) uses information provided by
the NLP and applies a lot of maths to determine whether something is positive or negative. Several
methods exist to determine an author's view on a topic from natural language textual information.
Some form of machine learning approach is employed and which has varying degree of
effectiveness. One of the types of natural language processing is opinion mining which deals with
tracking the mood of the people regarding a particular product or topic. This software provides
automatic extraction of opinions, emotions and sentiments in text and also tracks attitudes and
feelings on the web. People express their views by writing blog posts, comments, reviews and
tweets about all sorts of different topics. Tracking products and brands and then determining
whether they are viewed positively or negatively can be done using web. The opinion mining has
slightly different tasks and many names, e.g. sentiment analysis, opinion extraction, sentiment
mining, subjectivity analysis, affect analysis, emotion analysis, review mining, etc.
However, they all come under the umbrella of sentiment analysis or opinion mining. Sentiment
classification, feature based sentiment classification and opinion summarization are few main fields
of research predominate in sentiment analysis.
In

Research Methodology on Natural Language Processing
Research methodology on Natural Language Processing:
The main aim of this project is to research on the integration of "Natural Language Processing " and
information systems engineering to enhance query retrieval in natural language processing.
Defining a research methodology:
The definition of research methodology includes two parts:
– Research definition
– Methodology
Research:
It is defined as a thorough and organized query or investigation on a particular theme to revise or
determine the facts, theories, applications and so on.
Methodology:
It is a system of methods followed by a particular discipline. Thus, research methodology is the way
how a researcher performs research.
Uses of research methodology:
There are many ... Show more content on Helpwriting.net ...
[Gordhan Marshall, 1998]
Objective–1:
To study the concept of natural language processing and various features involved in it.
The above objective is fulfilled by initiating an exact design required for performing the research
process. The research design illustrates the basic understanding about the research (NLP) and
various features.
Research strategies:
Normally, research methodology includes two strategies for performing research.
– Qualitative research
– Quantitative research
Quantitative research:
Quantitative research methods are utilized to study the natural phenomena. So, it includes survey
methods, formal methods, and numerical methods.
Qualitative research:
Qualitative research methods are utilized to study the social and cultural phenomena. So it includes
observation, participant observation (field work), interview sessions, documents and texts and
finally the researcher's imitations and feedback. [Myers, 2009]

Qualitative research is a technique of promoting research that stresses the quality according to the
user's point of view and approaches. In depth interviews and focus groups are best examples of
qualitative research. [Laura Lake, 2009]
Objective–2:
To study various procedures involved in query evaluation of natural language processing
The above objective of the project is fulfilled by selecting a suitable strategy among all the available
strategies. In this research methodology, the researcher used

Greek Language Essay
A survey on various platforms for Sanskrit and Part–Of–Speech Tagging Methods
Sulabh Bhatt, Parmar Krunal
Department Of Computer Science, Gujarat University
Ahmedabad, India sulabhbhatt@gmail.com parmar.krunal005@gmail.com
Abstract – In this paper we present a Natural language processing for Sanskrit using Different
approaches. Sanskrit is a oldest and considered as the mother of all languages. The Sanskrit, the
world 's ancient language has got a wealthy grammar. The Sanskrit grammar text Ashtadhyayi is
written by Panini (an Indian Sage). Sanskrit is a well suitable language for providing an advanced
artificial intelligence for Computers. Part of Speech (POS) Tagging is the first step in the
development of any NLP Application. A POS Tagger (POST) is a piece of software that reads text in
some language and assigns parts of speech to each word (and other token), such as noun, verb,
adjective, etc.
Keywords – NLP for Sanskrit, Tagset, POS Tagging, HMM, CRF
I. INTRODUCTION
Natural language processing: Natural language is the embodiment of human cognition and human
intelligence. Natural language processing (NLP) is a cross disciplinary field in computer science and
linguistics where a computer can generate and understand human speech. It is the ability of a
computer program to understand human speech as it is spoken. NLP is a component of artificial
intelligence (AI).The development of NLP applications is challenging because computers
traditionally require humans to "speak"

The Importance Of Pragmatic Language Processing
as Goodman & Stuhlmuller (2013) claim that pragmatic language understanding is a social
cognition. Perhaps the complex systems involved in pragmatic language understanding are modules
that specifically process information about social context, and, it is likely that these interpretation.
Perhaps one might start by searching for data suggesting that semantic processing and pragmatic
processing involve different parts of the brain.
In a series of experiments, Rabagliati, Pylkkanen & Marcus (2013) tested linguistic ambiguity
resolution in children compared to adults. They found that children processed language differently
than adults and that children had trouble integrating contextual cues (Rabagliati et al., 2013, p.
1085). And they suggested that this may be because children's executive function abilities are not
fully developed (Rabagliati et al. p. 1085, 2013). This notion is in alignment with previous research
by Khanna and Boland (2010) who studied lexical ambiguity resolution in 7 to 10 year old's and in
adults. They found that those who had more fully developed executive function abilities also were
more sensitive to context in their lexical interpretation (Khanna & Boland, 2010).
These results are starting to paint a picture that the structures for analyzing and interpreting syntax
and semantics may be different than the structures that are involved in a pragmatic interpretation.
One might hypothesize that the modules for strict semantic understanding are in

Benefit Of Artificial Intelligence
Artificial Intelligence Today one of the most spectacular technologies in IT industry is "Artificial
Intelligence" that was invented in 1955 by John McCarthy who is also recognized as the father of AI
(Artificial Intelligence). As the name suggests that a technique which provides intelligence to
artificial (machine) is AI. Now let's elaborate what AI exactly is. Artificial Intelligence is such kind
of technology that makes a machine capable to act as a human as self–driving cars, robots, missile
guidance etc. Here intelligent agents are designed to perform according to the real–world
environment. For an instance, the voice recognition system takes the input of spoken words and
reacts according to it as Apple's Siri which listens to human voice and perform the task given to it.
So far, we have seen what Artificial Intelligence is. Now let's know about the benefits and risks
related to AI. Benefits of AI: Artificial Intelligence has made the technology so advanced that it's
benefits are taken in almost every field. Health: People who need some medical help can ask to the
computer that provides the proper solution to them, besides if there is some medical emergency and
the clinic or hospital is not nearby then the medical help can also be taken on phone by calling to a
hospital and tell about the problem, the nurse will put your problems in her system then the system
will provide the cure of the problem. Other than this AI is adopted in hospitals to store a large
amount

Text Analysis : Text Mining
Text mining sometimes known as text data mining often refers to the process of pulling out of
interesting and non–trivial patterns of knowledge form a semi or unstructured text document. Text
mining can also serve as an extension of data mining or of data finding from a structures database.
With text mining it can be the same as data mining but with a bit more complexity, because they
somewhat carry out the same processes and has the same purpose, however with text mining the
data is more unstructured rather that structured in the data files such as : (pdf, word, xml etc.). This
is so because most people store information in the form of text, it is believed that text mining can be
greater than data mining, during recent years there where a number of studies done which indicates
that 80% of business information is stored in text format.
Text mining is an interdisciplinary field which involved retrieving information, text analysis,
information extraction, clustering, categorization, visualizations, database technology, machine
learning and data mining.
Text mining can be of great help to different area where there are large amount of data/ information
such as accounting( monthly reports),human resource(employee reports),marketing(customer
feedback) just to name a few . With text mining we don't just use it as a means of filter but it can
also be used for prioritizing emails based on their level of important
Differentiate between text mining, text analytics and data

Introduction Of Natural Language Processing
INTRODUCTION TO NATURAL LANGUAGE PROCESSING
What is NLP?
Natural Language Processing can be defined as the use and ability of systems to process sentences
in a natural language such as English, instead of using a specialized artificial computer language
such as C, C++ etc. The systems used for NLP are a digital computer which is similarly resemble to
mainframe and personal computers. The digital computer systems in this fifth generation possesses
artificial intelligence technique and thus able to process natural languages. Natural language is a
more restricted subset of a human language; it cannot be thought of an actual language that
possesses ambiguities which computers could not sort out. Hence it is fair that "Human Languages
grant aberration that natural language cannot grant." In a larger view; Natural Language Processing
includes signal processing or speech recognition, context reference issues and semantic analysis and
processing. Typical applications for Natural Language Processing include the following:
a) A good human computer interface that can translate from a natural language into a computer
language and vice versa. A natural language system can act as an interface to a database system.
This technology is very useful for the blind people; that with the help of speech recognition interact
with computers.
b) A basic necessity is for a translation program that could convert from one human vocal language
to another. This saves down the time for translation.
C)

We Propose A Novel Framework For Tweet Segmentation
Abstract–Twitter has become one of the most important communication channels with its ability
providing the most up–to–date and newsworthy information. Considering wide use of twitter as the
source of information, reaching an interesting tweet for user among a bunch of tweets is
challenging. A huge amount of tweets sent per day by hundred millions of users, information
overload is inevitable. For extracting information in large volume of tweets, Named Entity
Recognition (NER), methods on formal texts. However, many applications in Information Retrieval
(IR) and Natural Language Processing (NLP) suffer severely from the noisy and short nature of
tweets. In this paper, we propose a novel framework for tweet segmentation in a batch mode, called
HybridSeg by splitting tweets into meaningful segments, the semantic or context information is well
preserved and easily extracted by the downstream applications. HybridSeg finds the optimal
segmentation of a tweet by maximizing the sum of the stickiness scores of its candidate segments.
The stickiness score considers the probability of a segment being a phrase in English (i.e., global
context) and the probability of a segment being a phrase within the batch of tweets (i.e., local
context). For the latter, we propose and evaluate two models to derive local context by considering
the linguistic features and term–dependency in a batch of tweets, respectively. HybridSeg is also
designed to iteratively learn from confident segments as pseudo

Social Values: What Is A Personal Value?
What is a personal value?
A personal value is an individual's absolute or relative and ethical value, the assumption of which
can be the basis for ethical action. A value system is a set of consistent values and measures. A
principle value is a foundation upon which other values and measures of integrity are based. Some
values are physiologically determined and are normally considered objective, such as a desire to
avoid physical pain or to seek pleasure. Other values are considered subjective, vary across
individuals and cultures, and are in many ways aligned with belief and belief systems. Types of
values include ethical/moral values, doctrinal/ideological (religious, political) values, social values,
and aesthetic values. It is debated whether some values that are not clearly physiologically
determined, such as altruism, are intrinsic, and whether some, such as acquisitiveness, should be ...
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The first are personal life value priorities – Determining candidates most important current values
(e.g., money, location, service to others, time with family), rank–ordering and deciding which will
trade off if faced with a contradiction (e.g., the job you want not being available in the location you
want). As said earlier, many people keep themselves in a state of continual agitation by refusing to
make focused value decisions.
The second are personal job–content objectives – Identifying what specific combination of skills or
competencies (e.g., intellectual, technical, interpersonal, physical, artistic, mathematical, etc.)
candidates want to develop and exercise in their future on–the–job activities. These objectives
become their criteria for judging the content of potential employee, if a potential opening involves
doing a lot of financial or technical analysis by their self with no opportunity for interacting with
others, some candidates will avoid that job even if it is a

Artificial Intelligent, Natural Language Processing
Abstract
We are living in a busy world. Everyone is trying to catchup with almost everything he can. As a
result we all need our own persona assistant to help us out everyday. But not everyone can afford it.
Intelligent Personal
Assistant is the answer for them. Artificial Intelligent is improving fast. And taking advantage of it
in our everyday life can make our life much easier. It can mimic basic things like a human
companion. In this paper we compared between currently popular Intelligent Personal Assistants.
What they can do and what they can not yet. Though they provide state of art features but there is
still many field to make improvement, like improved natural language processing so that we don't
have to use some predefined keyword to get answer.
Chapter 1
Introduction
With the breakthrough of speech recognition, natural language processing, semantic web and
machine learning we can safely say that the age of Intelligent Personal Assistant(IPA) is upon us. A
software agent that can do various task or service for a person is called Intelligent Personal
Assistant.
It depend on user input, location information and various online sources for information about
weather, traffic conditions, news etc. Currently there are many of such agent is available for
everyday use such as Google Now by
Google, Siri by Apple, Amazon Echo, Microsoft Cortana and Facebook's M.
The first fully functional IPA is Denise developed by NextOS formerly known as Guile3D. The
company was founded

Trends Of E-Commerce Trends
Four Trends That Will Dominate the E–Commerce Industry In 2018 Every year the e–commerce
industry is experiencing a stupendous 20% growth. Last year has been awesome for the e–
commerce industry. New technologies emerged at the end of 2016, flourished and evolved to bring
about a maturity in the entire online retail industry. Definitely, at the end of 2017, this evolved
technology and few emerging technologies in the e–commerce domain will be setting new trends for
2018. Build Your Online Store by Diving into the Happening E–commerce Trends of 2018 The
technology advances in 2018 have brought us where computers can easily augment human
understanding and behavior. Moreover, e–commerce businesses are trying hard to stand out today's
noisy competition by making their online ... Show more content on Helpwriting.net ...
Using AR and VR technology to develop an e–commerce website will involve customers deeply
offering a compulsive 'Immersive' experience. So hold on to your seats, tighten your seat belts and
enjoy the ride to v–commerce because the e–commerce industry will be changing forever.
Conclusion The primary aim of every business is to acquire and retain customers; e–commerce
business is no different. Each year technology evolves compelling us to take a note of it. Coping up
with these trends is beneficial for online businesses because early adoption of latest technology
provides a leading edge over the competition. Inversely neglecting new market and technology
trends will eventually result in a major setback. It is hard to predict the future concerning e–
commerce technology. However, we think these four trends will particularly make an impact in
2018. Regardless of what you are, selling online it is pretty clear that not a single e–commerce brand
can afford to rest on its laurels. Therefore, it's your take whether to keep up with these trends or

Wireless Voice Controlled (Natural Language Processing)
WIRELESS VOICE CONTROLLED (NATURAL LANGUAGE PROCESSING) HOME
AUTOMATION SYSTEM
ABSTRACT
Automation is an upcoming technology of 21st century. The important reason automation gaining its
popularity is reducing human effort, interaction and to reduce human errors. With the improvement
in latest technologies, smartphones have become an essential gadget to all. For 2016, the number of
Smartphone users is forecast to reach 2.1 billion. Another upcoming technology is the natural
language processing which uses human voice for commands. Combining all of these technologies,
the project presents a wireless voice controlled home automation system. Such a system will be
helpful for senior citizens and physically disabled persons who are in need ... Show more content on
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2. SYSTEM DESIGN
2.1. System Components
2.2. Figure 1: Architecture Diagram of the System
The Voice–operated Android and Arduino Home automation system uses an Android based
Bluetooth enabled phone for its application and the Arduino Uno as the microcontroller. The key
components of this system are:
Android Smartphone
Arduino UNO board
ESP8266 Wi–Fi module
Relays
Light bulbs / LED's
2.2.1. Android Based Phone
Android is a mobile operating system (OS) based on the Linux kernel and currently developed by
Google. With a user interface based on direct manipulation, the OS uses touch inputs that loosely
correspond to real–world actions, like swiping, tapping, pinching, and reverse pinching to
manipulate on–screen objects, and a virtual keyboard. We have used the Android platform because
of its huge market globally and it's easy to use user interface. Applications on the Android phones
extend the functionality of devices and are written primarily in the Java programming language
using the Android software development kit (SDK). The voice recognizer which is an in built
feature of Android phones is used to build an application which the user can operate to automate the
appliances in his house.
2.1.2 Arduino

Arduino is an open source prototyping platform easy to use on software as well as hardware. The
board can programmed to switch on/off electronic appliances using ARDUINO (IDE).
2.1.3 Wi–Fi Module
This allows the Arduino board to connect to android

Broca Language Processing
Language, the ability to speak and to express thoughts and feelings as well as the comprehension of
the words one may speak. Language plays a big role in daily function in everyday life. Looking at
the biological bases of behavior, which was learned the PSYC 1001 slides you can see that the way
the brain works in the human body and the way it plays a role in language production and
understanding. In the brain the language processing occurs mainly in the left hemisphere in the
brain. There are two areas in the brain that must do with the production and the comprehension of
language in the brain. These areas are known as Wernicke's area and the Broca's area. The
Wernicke's area in the brain is known for the comprehension of language and the ... Show more
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The Broca's area is associated with the outputs of language and how the production of language is
produced. Having to choose an area to lose in the brain will both have major setbacks to language
and the way we communicate with others. Looking how both the areas process language you can see
which one would be more beneficial than the other area. If one area would have to be gone from the
brain, it would be the Broca's area. Even though I am one to like talking a lot, it would be hard to
talk about anything without being able to understand what the other person is saying. Looking how
both areas function you can see that with losing the Broca's area you would be losing the production
of language. If I were to choose the Wernicke's area instead, I would be losing the ability to
understand language inputs. Losing the Broca's area would mean that I would lose the ability to
produce language, but would still can understand language because I wouldn't be losing the
Wernicke's area. Even with losing one area, the processing of language has to do with both, due to
the fact that the Wernicke's area and the Broca's area is interconnect with a bunch of nerve fibers
called the arcuate

Performance For Web Documents Mining Using Nlp And Latent...
A
THESIS
On
Performance for Web document mining using NLP and Latent Semantic Indexing with Singular
Value Decomposition
ABSTRACT
In this thesis we propose a description Web based document file can be say that Latent Semantic
Indexing is a application for information sentence and word based retrieval that promises to offer
better performance by incapacitating approximately limits that waves outdated term identical
methods. These word matching techniques have constantly relied on matching query terms with
document terms to retrieve the documents having terms matching the query terms. However, by use
of these traditional retrieval techniques, user's no need for adequately helped. While users want to
search through information based on conceptual content, natural languages have limited the
expression for such area of study. By Using Cholesky decomposition finds the lower triangular
matrix that satisfies . For instance, with two random variables the decomposition is done as worked.
Although, a determinant of the correlation matrix of the main variables does not have to be positive
and in that case other transformation methods can be applied. NLP (natural language processing)is
used for stemming, stop word and they show problem for polynomial series for the sentence . Due to
these natural language problems, individual words contained in user's queries, may not clearly
specify the intended user's concept that find the result in retrieval of some unrelated

How Does Bilingual Language Processing Work?
The ability to read a word is not as easy as it seems. In fact, it is a cognitively complex process that
it is not only requires that the reader should know its meaning only but also other linguistic aspects.
To name a little, reading a given word necessities understanding its meaning, pronunciation, form,
relationship to the world such as its sense or reference along with how it is morphologically
structured and syntactically functioned. So understanding such processes enable a monolingual
reader to properly read and use this given word. But when it comes to reading in two different
languages things behave slightly different. Bilinguals so often encounter words that are shared in
both of his/her language either phonologically or orthographically such as English–Spanish rich,
rico, and English–Dutch monster–monster. This way, cross–language similarities highly increase the
complexity level of processing and in turn pose important questions; how does bilingual language
processing work? Does it work the same way as L1? If so,
Prior answering such questions, these cross–linguistic similarities have motivated psycholinguists to
investigate what is known as cognates. Languages such as English, Dutch and Spanish are so–called
Indo–European languages sharing a considerable number of cognates. On the other hand, English,
Chinese, Arabic are unrelated languages. In other words, they did not come from a common ancestor
language as the case of the previously mentioned languages;

Language Processing Theories In Chapter 4 OfHow Languages...
LING325 Assignment 1
Patsy Lightbrown and Nina Spada (2013) explore various second language processing theories in
Chapter 4 of 'How Languages are Learned' through behaviourist, innatist, cognitive, and
sociocultural perspectives.
After briefly reviewing the behaviourist perspective which had an early influence in teaching where
students had been made to learn through memorisation and imitation, the chapter goes on to the
innatist perspective with Stephen Krashen's (1982) 'Monitor Model'. Krashen postulated five
hypotheses.
One of these is the acquisitional learning hypothesis which states that language is acquired by being
exposed to a selection of language without conscious attention, whereas language is learnt through
conscious attention to rule learning and form.
Another of Krashen's hypotheses is the monitor hypothesis, wherein second language users employ
the rules and patterns they have learnt when engaging in spontaneous conversation, allowing them
to make slight changes and refine what they have acquired. However, this only occurs when the
speaker has enough time, has learnt the relevant rules for application, and is concerned with
accuracy.
According to the natural order hypothesis, language rules that are the simplest to instruct are not
necessarily the first to be acquired.
In the comprehensible input hypothesis, acquisition takes place when the learner is exposed to
language that is comprehensible and holds i+1. The 'i' constitutes the level of language

Natural Language Processing And Machine Learning Techniques
The manuscript at hand presents a framework that implements Natural language processing (NLP)
and machine learning techniques to extract synthesis parameters of metal oxides from a large set of
published articles. The manuscript also presents insights into the key synthesis parameters using
machine learning algorithms. NLP technique is of broad and current interest in many research areas
and it is being extensively used to extract information on a large scale, which is otherwise not
feasible via manual exploration. In the field of chemistry/materials, NLP holds immense promise to
extract useful information the literature, such as extraction of materials properties, processes, and
various synthesis details. However, NLP has not been ... Show more content on Helpwriting.net ...
I believe the current manuscript holds the merit to be published in a high–quality journal, and my
recommendation is to accept the manuscript to be published in Chemistry of Materials. The authors
may want to address the following list of minor issues to further improve the manuscript: 1. Logistic
regression classifier is applied to distinguish paragraphs that are related to synthesis from other non–
synthesis related paragraphs. Applying logistic regression to classify the paragraphs is an elegant
way to determine the synthesis details, however, it might have errors in the classification (95%
accuracy in the current manuscript). A simpler way to classify would be to search for section titles
like 'Methods', 'Materials', 'Methods and materials', etc., and then classify the text in this section as
synthesis paragraphs. It will be worth considering this approach and comparing it with the logistic
regression approach. However, the former technique might not work if the search paper is a review
article. 2. From the description presented in the manuscript, it appears that the data is extracted from
the paragraphs. However, there are articles where the information is presented in the form of
tables/figures. A discussion on the scope of data extraction from tables/figures would be of
significant interest to the chemistry community as large amount of chemical/materials data is
published in this format. 3. It would be helpful to have an explanation of the

The And Temporal Information Extraction
Much of the information extraction community early focus on in tasks like named entity
recognition, co–reference and relation extraction, but recently due to the high demand of temporal
information in different NLP application, scholars are focusing on event, temporal and event and
temporal information extraction and the extraction of event and temporal information became a hot
research area. Different scholars involved in event and temporal information extraction researches.
Currently there are a lot of research conducted in event and temporal information extraction in
different domains and languages with different techniques, methods and tools. In this chapter, we
present some of the works conducted in the English language related to this thesis project. The first
work we discuss is called TIE, Temporal Information Extraction system extracts events from text by
inducing as much as temporal information possible. TIE makes global inference, enforcing
transitivity to bound the beginning and finishing time for each event. TIE introduces temporal
entropy as a method to evaluate the performance of temporal IE system. TIE system outperforms in
experiment in three optional approaches. The TIE system uses a probabilistic method to recognize
temporal rations. They use TimeBank data [25] to train the system. TIE processes, each natural
language sentence into two sequential phases. The first phase is responsible for extracting event and
identifying temporal expression and it use a

Language Development Of Language And The Processing Speed
Early language development predicts the amount of vocabulary knowledge as the child develops and
is a key factor that is linked with later academic achievement (Pungello et al., 2009; Weisleder &
Fernald, 2013). Also, background factors must be analyzed and assessed, in order to understand how
language growth differs from one child to the next. Exposure to speech is very important and helps
influence early development of language and the processing speed (Fernald, Marchman, & Wielder,
2013 as cited by Weisleder & Fernald, 2009). A study done by Kwon et al., (2013), found that play
has a significant effect on the language complexity for children's language use pertaining to the
structure of play or activity setting (free play), however the gender of the parent did not influence
the language growth for the child. Furthermore, children are able to identify familiar words when
speech is directed towards the child and not over heard, facilitated vocabulary learning at the age of
24 months (Weislder & Fernald, 2013). For example, over hearing adult conversation is not as
beneficial towards the child's vocabulary learning.
However, research rarely focuses on the parent's emotional intelligence and how it has an effect on a
child's language growth. Emotions are defined as, "internal events that coordinate many
psychological responses, cognitions, and conscious awareness" (Mayer et al., 1999 p.268). Yet,
emotional intelligence is the ability to perceive, assimilate, understand, and

Multi Label Semantic Relation Classification
Multi–label Semantic Relation Classification
Between Pair of Nominals
Kartik Dhiwar,
PG Scholar, Department of Computer Science and Engineering, SSGI, SSTC, Bhilai (CG), India
kartikdhiwar21@gmail.com Abhishek Kumar Dewangan
Professor, Department of Computer Science and Engineering, SSGI, SSTC, Bhilai (CG), India
abhishek.dew2006@gmail.com Abstract: Relation classification is a keynote in the field of Natu–ral
Language Processing (NLP) to mine information from text facing problems of over–reliance on the
standard of handcrafted features. Features annotated by specialists and lin–guistic data derived from
linguistic analysis modules is expen–sive and ends up with the difficulty of error propagation. Rela–
tion extraction plays a crucial role in extracting struc–tured data from unstructured sources like raw
text. One might want to seek out interactions between medicines to create medical information or
extract relationships among people to create a simply searchable knowledgebase. We propose a deep
Convolutional Neural Network model for the multi–label text relation classification task without
hand crafted features. This model outperforms the best existing model as per our knowledge without
depending much on manually engineered features with the small updates in the loss function
applied.
Index Terms – Relation Classification, Features, Label, Convolutional Neural Network, Information
Extraction.
.
1. INTRODUCTION
Natural Language Processing tasks are now applicable to

A Study On The Mapping Process Of Mapping The Coordinates...
ABSTRACT Named entity disambiguation is a very interesting problem having wide ranging
applications. It is the process of mapping the mentions of persons, organisations, events etc. in
textual documents to real world entities. The mapping process becomes tedious when these
mentions in the text are commonly used to describe more than one real world entities. It is then said
that the mention is ambiguous. If the mentions can be correctly mapped to the corresponding real
world entities, then it can lead to a more informative and intuitive web experience where the textual
documents can be linked to other knowledge bases which contain more information on various
entities described in the document. In this report, we have surveyed well known research papers in
the field of named entity disambiguation and have described the approaches suggested in them.
Successive sections of report aim to solve the shortcomings of the algorithms discussed in the
previous sections. INTRODUCTION The recent years have seen a huge increase in the number of
online documents. This has resulted in a huge amount of information being available at the click of a
mouse. But, at the same time, the retrieval of relevant information from this collection of
unstructured documents has emerged as a challenging task and is a topic of research. A major part of
retrieving information out of a document is finding out the words or phrases of significance in the
article like the persons, organization, location,

Human Differences Between Human And Artificial Intelligent...
A Turing test is the process of a human distinguishing the difference in answers when conversing
with both a Cleverbot and a human, however is unaware of the questions that have been answered
by the human and which have been answered by artificial intelligence. This test is also to find out if
Artificial intelligence responds similar enough that it is equivalent or indistinguishable. Example:
We carried out this investigation to see what would happen and to see if it is easy enough to
distinguish the difference between human and artificial intelligent conversations. A certain human
was picked to complete the Turing, as he was the one capable of asking the questions. The
importance of him asking the questions was because we needed a person to begin the conversation,
and also to make sure that the investigation was fair. Here are the results: Human conversing with
Human Questionnaire: – Human Questionnaire: Hey! How are you? – Human: I 'm good. How
about you? – Human Questionnaire: I 'm ok thank you. – Human: That's nice what's your name? –
Human Questionnaire: My name if Joffrey, what about you? – Human: My name is Robin. – Human
Questionnaire: That's a cool name, my sister is called Robin – Human: Do you like your sister? –
Human Questionnaire: No, she is loud and annoying – Human: How old is she? – Human
Questionnaire: She is 8. – Human: Yeah that's an annoying age. How old are you? Human
conversing with Artificial intelligence/Cleverbot: – Human Questionnaire: Hey! How are

Description Of Automatic Text Summarization
Automatic Text Summarization Bhavika S. Joshi Pooja N. Mane Atharva College of Engineering
Atharva College of Engineering Department of Computer Science Department of Computer Science
University of Mumbai University of Mumbai Mumbai, India Mumbai, India Email:
bhavika.joshi1994@gmail.com Email: poojamaner2611@gmail.com Kaveri S. Metkari Atharva
College of Engineering Department of Computer Science University of Mumbai Mumbai, India
Email: kaveri.metkari@gmail.com Abstract – Nowadays people want a quick, rapid and concise
review of any given text rather than going through the entire length of the content. In this growing
age of information technology witty huge amount of data being churned every second users for
reliable context that is short and concise over lengthy paragraphs of text. Realizing this need this
project is based on summarizing content in to a more concise yet informative context without losing
the moral premise or the objective for which the text was originally developed. We employ various
data mining algorithms to condense the entire text into a content rich summary document by
extracting the prominent and relevant information outlining the purpose of the entire text. I.

Case Study On Neural Machine Translation
Facebook partnered with Bing in 2011 for translation services, making it an early pioneer in the
move toward neural machine translation.
In December 2015, Facebook dropped Bing and began to outsource its translation services while
developing its own translation technologies.
Facebook's own neural machine translator went fully operational on August 3, 2017.
Overview
Hello and thank you for your question about Facebook neural machine translation. The short version
is that Facebook has been moving toward integrating their own translation technology since 2011,
and have recently (August 2017) gone fully operational with their own Neural Machine Translator.
Below you will find a deep dive of our findings.
METHODOLOGY
Our goal for this ... Show more content on Helpwriting.net ...
Facebook was an early pioneer of online translation when in 2011 it started using automated systems
to translate users' posts and comments in the News Feed. Facebook used Bing initially because they
didn't yet have their own technology.
In January 2015, Facebook open–sourced Torch (their deep learning library). Facebook explains
TORCH as an open source development environment for numerics, machine learning, and computer
vision. Many projects on machine learning and AI at FAIR (Facebook AI Research) use Torch.
Also in 2015, Facebook acquired Wit.ai, a startup using understanding of natural language in
text/voice to power user interfaces.
In December 2015, Facebook dropped Bing and started using its own translation technology. The
issue was that Bing was built to translate more properly written website text, not human slang.
Facebook stated that the disconnect between the languages people speak and the content they want
to connect to on the Internet is what made them want to create their own neural network–based
machine translation (MT) system.
Also in Dec 2015, Facebook opensourced it's hardware designs for anyone to explore through the
Open Compute Project. The server was called BigSur, which is described as servers with graphics
processing units (GPUs), which have become the chip of choice for deep learning.

Later, in June 2016, Facebook stated that Statistical

An Analysis Of Banking Chatbots In The Banking Industry
. This is possible when this algorithm ingests millions of data poits from existing information. An
Artificial Intelligent company in San Fransisco called Sentient technology, running a hedge fund,
has developed a machine learning algorithm that can get this done. Also, Numerai, which is another
hedge fund, is using Artificia iIntelligence to make trending decisions. Banking Chatbots: Machine
learning algorithm and natural languauge process are very important machineries to ensure a
conversational and personalized experience to customers across board.The AI chatbots have played
significant roles in advancing the course of the banking industry . One way the AI chatbot is
improving the banking sector is the way it helps user manage their ... Show more content on
Helpwriting.net ...
The hman staff will have more time to engage in more challenging and important tasks while
automation of tasks related to screening of curriculum vitae, among others will be handled by
Artificial Intelligence. New Employee Onboarding: Onboarding of new employees entails
introducing them to the culture, processes and polices of the company. The AI's Virtual Assistant can
provide answers to those areas of questions. The Issues of Artificia Intelligence The speed at which
Artificia Intelligence is growing and developing is very interesting and at the sam time , a source of
concern. The school of thought of some researchers, scientists, and developers with respect to this
pace of development is that Artificial Intelligence could grow to the extent that regulation or the
control of its activities may be very difficult to achieve. Humans now seem to be threatened that the
degree of intelligence intoduced into these machines may not be worth the while in the nearest
future. The Threat To Safety: It is believed in some quarters that self–improving Artificial Intelligent
Systems can evolve beyond the expectations of hmans. If this happens,it will be very difficult to
stop them from achieving their gial ,which could lead to unintended consequenses. Concern About
Human Privacy:

Web Intelligence And Its Usefulness
Abstract
In the world of Information Technology (IT), there are many areas and disciplinary of research
available and Web Intelligence (WI) is one of the new sub disciplinary of Artificial Intelligence (AI)
and Advanced IT. When AI and IT is implemented on web it defines WI. WI is used to develop web
– empowered system, Wisdom Web, Web Mining, web site automation, etc. In this paper, detail
discussion is done on Web Intelligence and its usefulness in developing intelligent web. Many
literatures are also discussed related to the Web Intelligence and at the end challenges and problems
faced during the research in the area is also mentioned. This paper will provide the pathway to the
researcher who want to perform research in the field of Web Intelligence.
Keywords – Natural Language Processing, Web Intelligence, Artificial Intelligence, Advanced
Information Technology
I. Introduction
In the era of Information Technology (IT) Web Intelligence (WI) represent new sub disciplinary for
scientific research and development that explores fundamental roles as well as practical impacts of
Intelligence. T. Y. Lin and Yan–Qing Zhang [2] have described Intelligence as" a specific set of
mind capabilities which allow the individual to use the acquired knowledge efficiently and to
behave appropriately in the presence of new tasks and living conditions". With the explosive growth
of internet, wireless network, web database and wireless mobile devices implies intelligence on web.
Y.Y. Yao,

Space Race Evolution
Even though the Earth has been around for a few billion years, it has been only 200,000 years when
the first homo sapiens, modern human species, came to existence. To put it into perspective, if the
arm length represents the timeline, the shoulder is the Big Bang, the beginning of the finger nail is
when the dinosaurs existed, and the human start to become a possibility right around the tip of the
finger. Even more astounding, industrialization started only in the 1800s, which estimated 200 years
to develop to the current society. The remarkable number is evidence to human extraordinary
intelligence and limitless capabilities to transform ambition into reality; however, any development
will reach to a point where the growth is less significant than it is used to be. This is referred as the
law of diminishing return. Therefore, the research for a more advanced technology, Artificial
Intelligence, or AI, has become the new Space Race of the 21st century. Many ... Show more content
on Helpwriting.net ...
If human hadn't eaten the food the same way the ancestor of over a half million years ago did, which
was to cook them first, it would have taken more than nine hours of eating a day to power the brain.
(15) Cooked foods are pre–digested, softer and easier to swallow, promoting complete digestion and
absorption of nutrients. The same idea applies with allowing AI to take over time consuming and
difficult task. The impact that AI brings far more than just direct consequences. With AI, people
work less and can spend more time exercising, maintaining a balance between physical and mental
health, which prevents stress and lowers the suicide or crime rate. Others can volunteer and help
people in need while some can spend more time with their relatives. The smartest one after all is still
humans. They design machines to do the hard work so they can have more time for

Language And Speech Processing Throughout Modern Humans Essay
The ability of speech and language processing has always been a defining factor in what makes
humans unique, especially from their closest living relative primates. This paper will analyze the
differences in modern human brain structure and the common chimpanzee brain structure (pan
troglodytes) in regards to the language and speech function. Language and speech processing in
modern humans will focus on two parts of the cerebral cortex: Broca's area and Wernicke's area,
which is responsible for generating speech and language and receiving speech and language
respectively. Broca's area will be analyzed by the two parts it is made up of: Brodmann area 44,
associated mainly with phonological tasks and Brodmann area 45, associated mainly with semantic
processing. Wernicke's area receives and interprets speech and language and is shown to be
connected to Broca's area by a neuronal tract known as the arcuate fasciculus. The structure of the
common chimpanzee brain is shown to have homologous structures to Broca's and Wernicke's area
in modern humans, but is significantly smaller, and is unable to perform the same developed
functions as modern humans in regards to language and speech, but is much more limited and
simplified. Speech and language are key components that distinguish modern humans from their
close relatives primates. In the modern human brain, located on the frontal lobe, is the motor cortex
or strip that regulates the facial and oral muscles. They include the tongue,

Language Processing And Memory Retrieval
In the past, cognitive studies on language processing and memory retrieval was mostly focused on
monolingual speakers. The idea of bilingualism and its effect on memory is relatively new, but it is
also considered as a rising topic in the field of psychology, linguistics, cognitive science, and second
language studies. In 1993, Javier, Barroso, and Muñoz conducted a research with a group of
Spanish–English bilingual speakers. They emphasized that language is a powerful retrieval tool and
a cue to the previous events and it serves to organize events in our memory. As the researchers asked
participants to describe an event in their personal histories in two different languages, they found out
that the participants' answers were slightly ... Show more content on Helpwriting.net ...
Episodic memory was tested by subject–performed tasks and verbal tests whereas semantic memory
was tested by word fluency tests. From this research, the researchers found that bilingualism had
positive effects in both episodic and semantic memory. Bilingual children were able to integrate and
organize information in two different languages and therefore they performed better in cognition
tests.
In 2006, Bialystok, Fergus, and Freedman proposed that bilingualism helps maintaining cognitive
functions and delaying the symptoms of dementia. According to the research conducted by
Bialystok et al. (2006), among the 184 samples with cognitive complaints from a Memory clinic,
more than half of the samples were considered bilinguals and they showed symptoms slower than
monolinguals by about four years. To find the result, the researchers examined the yearly records of
samples including their medical history, physical examination, and mental status evaluation for
about four years. And then the researchers gathered the data to analyze the speed of dementia
symptoms occurring to each samples. Then the samples were divided into bilinguals and
monolinguals to determine which group showed symptoms faster than the other. As a result, the
bilingual patients showed a delay of about four years in the onset symptoms of dementia compared
to monolinguals

Language Processing Disorder
Children and most certainly adults can be diagnosed with language processing disorder. The term
"Language disorder" is broad, and could be understood in two categories Language Processing
Disorder (LPD) and Auditory Processing Disorder APD. Although the two terms seems very closely
related, they are very different. A person that is diagnosed with LPD disorder may find themselves
having difficulty learning grammar, sentence structure, comprehending what is being read or said
(making sense of what being told) in a given language. "The disorder may involve the form of
language (phonology, syntax, and morphology), its content or meaning (semantics), or its use
(pragmatics), in any combination (American Speech–Language–Hearing Association 1993)". This
does not necessarily mean that the child or adult has a hearing loss. This could mean that their brain
does not process or interpret auditory information, properly ... Show more content on
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Especially, in young children. Early signs can be traced right in elementary school, language
disorders often exhibit reading and academic learning difficulties. Although a teacher, physiologist
etc. may assess a child with language disorders varies based on the age of the child. A diagnosed in
which reveals the severity of the disorder is also observed during "play" behaviors, interaction with
parents, siblings and peers provides information about the child's cognitive and social development.
There are also certain literacy skills that could but used as a formative assessment. Teachers should
monitor how student print alphabets and names, can the student recall the story or simply tell a
story, conversations with peers and other written samples of language. There are a lot of ways to
"see" the symptoms in the assessments of a child with language disorders. The results may indicate
specific areas of deficit, ascertain the possible causes of the impairment, and formulate specific
goals to remediate the

Automatic Summarization Of News Articles Using Textrank
AUTOMATIC SUMMARIZATION OF NEWS ARTICLES USING TEXTRANK
ABSTRACT:
With an increase in the amount of information consumed every day, time is a prime resource.
Keeping up with current events is an activity that is essential for everyone, but saving time is also
important. Our paper is mainly focused on the implementation of Natural Language Processing
techniques and algorithms to summarize news articles from public sources such that they can be
consumed in a short amount of time, keeping the user updated of global as well as local events. We
first provide an Introduction by stating problems faced, and an overview of NLP and Automatic
Summarization. We then survey different types of Summarization, and detail a solution using the
TextRank algorithm, along with our proposed implementation.
1. INTRODUCTION:
In today's world time is limited but the information available online is in excess. With the advent of
personal mobile computing devices, we are being presented with a barrage of information every
minute. This makes it increasingly important to consume as much information as possible in the
least amount of time, while eliminating irrelevant and redundant data. News is another domain
which falls prey to this information overload. With the emergence of lightning–fast news delivery
through social media services such as Twitter, Facebook well as various News Sources, It is a dire
need of the day to save time and grasp just enough information that is required about current

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