Clear Journal September2016

CLEAR September 2016 1

CLEAR September 2016 2

Editorial…………………… 4 News & Updates……….5 CLEAR December 2016 Invitation…………………23

CLEAR Journal (Computational Linguistics in Engineering And Research) M. Tech Computational Linguistics, Dept. of Computer Science and Engineering, Govt. Engineering College, Sreekrishnapuram, Palakkad678633 www.simplegroups.in simplequest.in@gmail.com Chief Editor Dr. Ajeesh Ramanujan Assistant Professor Dept. of Computer Science and Engineering Govt. Engineering College, Sreekrishnapuram, Palakkad678633 Editors Deepthi Kavya V J Reshma M M Swedha B Layout and Cover page Design Rahul M

Last word…………………24

Named Entity Recognition in Malayalam Using Neural Network………………………06 Anju Balachandran, Anju R C, Faseela M Next Generation World Wide Web ………...........12 Deepthi Do the Roles can be really labelled? ............................15 Gayathri E S Deep Learning and Big Data Analytics........................................17 Swedha B Block Chain Technology and BitCoin……………………………20 Athira M A

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Dear & Readers!

Greetings!

This edition of CLEAR is an issue consisting of paper and articles covering the major researches done in the area of Natural Language Processing, Semantic Web, Deep learning and Big Data Analytics and Block Chain Technology.

In our last edition we concentrated mainly on different research aspects for insurance analysis as an application of data mining, speech language conversion to sign language and also feature articles on word sense disambiguation, a survey on data mining tools and subgroup detection and summarization in ideological discussion while this issue contributes a paper work done at IIITM-K and some articles from Semantic web, Deep learning & Big Data Analytics and Block Chain technology.

The good response from our readers reflect the wider acceptability and growing awareness of Language processing among the technical community, which is definitely gratifying for the entire CLEAR team.

On this positive note, I proudly present this Issue of CLEAR to the readers, for critical evaluation and feedback.

Best Regards, Dr. Ajeesh Ramanujan (Chief Editor)

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INDUSTRIAL TRAINING AT IIITMK-TRIVANDRUM

Virtual Resource Centre for Language Computing (VRC-LC) department of Indian Institute of Information Technology and Management-Kerala (IIITM-K) had organized a short course cum industrial training on Natural Language Processing exclusively for the M.Tech students of GEC, Sreekrishnapuram.

The course was mainly related to Natural language Processing and they also covered topics which include NLP and Big Data, NLP with Hadoop, Programming with R, Automatic Speech Recognition, Tokenization, POS tagging, Morph Analyzer, Named Entity Recognition- Malayalam, Machine Learning techniques, Data Engineering: An Introduction to Topic Modeling, its Applications and Implementation using MALLET and Block chain technology.

It was a 5 days programme (from 25th July to 29th July 2016). The course was handled by eminent faculties and research fellows. The demonstration of the installations and the related works in the labs helps us to know much about our area. The participants were also privileged to associate with various projects, like Named Entity Recognition using Neural Network, Semantic Role Labelling using CRF, Emotion Recognition from Speech, Text categorisation using WEKA and Word sense disambiguation using CRF which was later demonstrated and evaluated by the faculties of VRC-LC on the final day, and the head of VRC-LC, Dr. Elizabeth Sherly addressed the participants and distributed certificates. The course was very interesting and we got a lot of learning experience from this training.

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ADIEU TO FOURTH BATCH OF M TECH COMPUTATIONAL LINGUISTICS

A small farewell was arranged by us for our outgoing seniors. The function was actually a get-together of our seniors and us. It was an informal function, full of joy and happiness. From morning to evening we entertained our seniors with lots of games and interactive sessions. We presented our seniors a momento as a token of love. Our seniors also shared their memories in the college, and gave their feedback on the course and the institution. They had suggestions on how to further improve the system here and they also gave valuable directions to their junior batch regarding various aspects of the course and project works.

The forenoon function was concluded and a lush lunch was served. The party was hilarious and filled with excitement. We had a strong bond with our seniors. They were actually our inspiration. Thus the fourth batch of Computational Linguistics bid good bye to the college with a bunch of good memories, with a promise to stay connected.

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PLACEMENT 2014-2016 BATCH

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Sini G M got placed at Ernst & Young, Trivandrum as Data Analyst.

Archana S M got placed at ZyudlyLabs, Bangalore as Data Scientist.

Athira M got placed at mStack, Infopark, Kochi as Jr. Data Scientist.

Naima Vahab got placed at Molecular Connections pvt ltd, Bangalore as Data Scientist.  Anoop R got placed at Ma Foi Analytics and Business Services, Bangalore as Research Analysts.  Sreetha S got placed at Ma Foi Analytics and Business Services, Bangalore as Research Analysts.  Revathy P got placed at mStack, Infopark, Kochi as Jr. Data Scientist.

UGC NET -2016  Anoop R of M Tech Computational Linguistics, 2014-2016 Batch qualified UGC NET- 2016.

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Radhika K T of M Tech Computational Linguistics, 2011-2013 Batch, placed in Govt. Polytechnic, Thirurangadi Malappuram as Lecturer (CSE) by KPSC.

Simple Groups Congratulates all for their achievement and wish them a bright future!!

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PUBLICATION 

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“Word Sense Disambiguation Using Decision Tree Method”, Rekha Thankappan , Kala M T, Dibin Joseph published paper in International Journal Of Innovative Research In Management, Engineering And Technology (IJIRMET) Vol. 1, Issue 5, June 2016.

“Speaker Diarization using Deep learning and HMM models”, Archana S M, Kavitha Raju and Raseek C published paper in International Academic Conference on Engineering and Technology(IACET-2016) and participated in conference ICSSP-2016,September 8th 2016.

“Malayalam Text Summarization: An extractive Approach”, Krishnaprasad P, Sooryanarayanan A, Ajeesh Ramanujan participated in conference ICNGIS, RIT kottayam.

“Word Sense Disambiguation Using Context Clustering”, Pelja Paul N published paper in IACET-2016 at Trivandrum, Organized by Techown, on July 2016.

“Domain Specific Automatic Ontology Population From Unstructured Documents”, Sreetha S, Kavitha Raju and Raseek C published paper in International Journal Of Innovative Research In Management, Engineering And Technology (IJIRMET), Vol. 1, Issue 5, June 2016.

“Multi Class Text Classification using LSTM”, Bhavya K published paper in International Academic Conference on Engineering and Technology (IACET2016).

“Domain Specific Ontology Based Question Answering System”, Sini G M, Kavitha Raju and Binu R published paper in International Journal Of Innovative Research In Management, Engineering And Technology (IJIRMET), Vol.1, Issue 5, June 2016.

“A New Way of Topic Modeling Using MALLET for Current Job Trends”, Bhavya K published paper in International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE).

Simple Groups congratulates all the authors for their achievement and wish them a bright future!!

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Named Entity Recognition in Malayalam Using Neural Network Anju R C 1, Anju Balachandran 2, Faseela M 3 M Tech Computational linguistics Government Engineering College, Sreekrishnapuram anju.malu50000@gmail.com1 , anjubaala06@gmail.com2,mailforfasi@gmail.com

ABSTRACT: Named Entity Recognition is an important application area of Natural Language Processing. Named Entity Recognition deals with recognizing Named Entities(NE) such as person, organization, location in text documents. NER can be performed using Rule Based approaches, machine learning approaches and hybrid approaches. This paper proposes a method for Named Entity Recognition of Malayalam language using Neural Network.

I.

INTRODUCTION

The Named Entity Recognition(NER) is the identification of named entities (nouns) and classifying them into the names of person, location, organization etc. A named entity recognition (NER) System is a significant tool in natural language processing(NLP) research since it allows identification of proper nouns in open domain. The level of ambiguity in NER makes it difficult to attain human performance.

The term named entity is not strict but has to be explained in the context where it is used. Entity names form the main context of a document. NER is a very important step towards more intelligent Information extraction and management. NER performs what is known as surface parsing, delimiting sequences of tokens the answer important

questions such as “What”, “Where” and “How” in a sentence.

Malayalam belongs to Dravidian family of languages and is one of the four major languages of this family. It is one of the 22 scheduled languages of India with official language status in state of Kerala. It is spoken by 35.9 million people. Malayalam is a morphologically rich agglutinative language and relatively of free order. Also Malayalam has a productive morphology that allows the creation of complex words which are often highly ambiguous. As now we have no information regarding Malayalam NER work and no tag set been identified so far.

In this paper we used Neural Network to identify Named Entities in Malayalam domain. Neural Networks are powerful techniques to solve many real world problems. They have the ability to learn from experience in order to improve their

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performance and to adapt themselves to changes in the environments. In addition to that they are able to deal with incomplete information or noisy data and can be very effective especially in the situations where it is not possible to define the rules or steps that lead to the solution of the problem.

II.

Input to the tokenizer is a document in Malayalam. During tokenization process each sentence of the document is taken and split into words or co-occurrance patterns.

WEKA TOOL

Named Entity Recognition in Malayalam can be built using Weka tool. Weka is a collection of Machine Learning Algorithms for data mining tasks. This algorithm can be either directly applied to a data set or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well suited for machine learning schemes. Weka is open source software issued under the GNU General Public License.

III.

MALAYALAM NAMED ENTITY RECOGNITION

The Malayalam Named Entity Recognition is implemented by following paradigm approach. The data used in the NER should go through some preprocessing steps. The Figure 1 shows the steps involved.

TOKENIZATION Tokenization is the process of breaking a stream of texts into words, phrases, symbols, or other meaningful elements called tokens. The list of tokens becomes input for further processing such as parsing or text mining.

Fig1: Steps in NER

POS TAGGING The tokenized text is passed through the POS Tagger, in which assigns proper POS tag to each word in its context of appearance in the sentence. In natural language, there are a small number parts of speech those are noun, adjective, adverb, verb, Conjunction, Preposition etc.

Malayalam is a morphological rich language so that POS Tagging of Malayalam language will be difficult than English Language.

In the paper [4] describes a method for supervised training of a POS Tagger using a newly developed algorithm for Malayalam.

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In this they identified the ambiguities in Malayalam lexical items, and developed tag set appropriate for Malayalam.

Fig.2: Example for Malayalam POS Tagging

PREDICTION To identify the named entities in the POS tagged data set is given as the input to the Weka. In Weka a multilayer perceptron is used to predict the named entity. A multilayer perceptron is a feed forward artificial neural network model. Malayalam tagged data set is used for train the perceptron. After the training the Weka will be able to predict the proper named entities.

As the number of hidden layers in the perceptron increases accuracy of the prediction also increases accordingly. The output of Weka is shown below.

IV. CONCLUSION The Proposed work helps in identifying Named entity by Neural Networks. It is well suited for highly morphologically and inflectionally rich languages like Malayalam. This system is tested with enough dataset and gets almost 65% accuracy obtained.

REFERENCES [1] Jisha P Jayan, Rajeev R R, Elizabeth Sherly, A hybrid statistical approach for Named Entity Recognition for Malayalam Language., Proceedings of International Joint Conference on Natural Language Processing in Japan 2013.

[2] Bindu M S, Suman Mary Idicula publish a paper Named Entity Indentifier for Malayalam Using Linguistic Principles Employing Statistical methods., in International Journal of Computer Science Issues in September 2011.

[3] Shruthi S, Jiljo, Pranav P V, A study On Named Entity Recognition For Malayalam Language Using TnT Tagger & Maximum Entropy Markov Model. International Journal of Applied Engineering Research in 2016.

[4]D Muhammed Noorul Mubarak , Sareesh Madhu, S A Shanavas , A New Approach To Parts Of Speech Tagging in Malayalam. Publish on International Journal of Science and Technology in 2015.

Fig.3: Output

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Next Generation World Wide Web Deepthi M Tech Computational Linguistics Government Engineering College, Sreekrishnapuram deepthisuthan@gmail.com

The World Wide Web that most of us can’t seem to live without is only 25 years old, yet in a quarter of a century a lot of changes has come about. Now popularly known as the Web, World Wide Web was almost called The Information Mesh or The Information Mine. Paving the way for the Information Age, it is the primary tool that billions of people use to interact over the Internet. From the early stages which very few people could use or understand, the Web is now a part of almost every aspect of our lives and is much more than what we see and access through Web Browsers. From Google to Amazon to Facebook and, most recently, Pokemon Go, the transformations are revolutionary. But even at this pace, there are requirements that the current Web cannot fulfill. This is where Semantic Web comes in. The inventor of the World Wide Web, Tim Berners-Lee defines Semantic Web as not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation. In the paper "The Semantic Web" published in Scientific American by Tim Berners-Lee, James Hendler, and Ora Lassila in 2001, it says that the Semantic Web is a source to retrieve information from the web and access the data through

Semantic Web Agents or Semantic Web Services.

Tim Berners-Lee originally expressed the vision of the Semantic Web as follows: "I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers.

A "Semantic Web", which makes this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines.

The "intelligent agents" people have touted for ages will finally materialize." The idea is to extend the hyperlinked human readable webpages of today by inserting metadata into them. This machine readable metadata describes the pages and how they are related to each other enabling automated agents to read and understand them. The applications of such a technology would be enormous. An example of a semantic web agent is Apple's Siri. This intelligent agent understands natural speech and allows asking questions.

It pulls data from the user’s calendar, location and search results to schedule meetings, book tickets or to find the best route home. While Siri's abilities don’t fully encompass all the ideas behind the semantic web, the agent loosely matches the

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intelligent agents proposed by Tim BernersLee. Let’s look into a feature of the Web that we are all familiar with, Searching. Most of us search for content on the Web using Search Engines, like Google, Yahoo, Bing or any other. But how accurate is the retrieved contents of our search? How often do we get what we need without having to reformulate our search keywords? Search has changed dramatically over the past few years and the Semantic Technology is the heart of it all.

People expect the search engine to understand natural language, and today a search for "Will it rain today?" will give you the chances of rain in your area as the rain progresses. As more and more natural speech finds its way into search queries, the focus of the search has to shift from keywords to the intent behind the words. People expect the search engine to understand the intent behind their words, and the search engine algorithms are rising to the challenge. According to Search Engine Journal, semantic search (also known as “entity search”) “uses machine intelligence to determine the intended meaning of words so searches become more relevant.”

Most search engines today has realized the possibilities of Web Content that is meaningful to computers.

Google’s Knowledge Graph and Hummingbird has Semantic Technology running in its background. The Hummingbird, introduced in 2013, takes into account both the synonyms and the context of search queries.

Google has always been using synonyms but with Hummingbird the whole query is taken

into account ensuring that the search carries the intent behind the query. Hummingbird places greater emphasis on page content making search results more relevant and pertinent, ensuring that Google deliver users to the most appropriate page of a website, rather than to a home page or top level page.

The underlying concept is that the pages matching the meaning do better than the pages matching the keywords. The infamous PageRank algorithm is now one of the 200 major ingredients that go into the Hummingbird recipe. Some of the semantic search engines that are an alternative to Google are Kngine, Hakia, DuckDuckGo, Evri, Powerset and Truevert.

Semantic Web is still a vision. There is a long way to go. Converting the existing Web into Semantic Web is a true chicken-and-egg problem. Adding metadata to all existing web pages is quite a difficult task. It’s a big web out there. Until there is sufficient amount of semantic data available, there is little incentive to produce the software to process it, and without software for semantic data, where is the incentive to produce the data? Fortunately, more and more developers are taking up this challenge and Diffbot is one of the results.

“We want to build the world’s largest database of structured knowledge”, says the chief executive of Diffbot, Mike Tung. And so far they seem to be going right in that direction. Diffbot has been able to beat Google’s Knowledge Graph to feed the need for structured data. Diffbot has been able to get past the objects and facts figures of Knowledge Graph and is constantly adding

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new objects and facts without any human oversight. The Semantic Web, when it does become a reality presents with different order of problems. Facebook’s new Graph Search, equipped with semantic search technology, helps users to find the connections they want more easily. For example, a user or an advertiser can find a connection X who live in Y. These deeper data levels has also increased the concerns over privacy, preventing them from “liking” certain things. This invasion of privacy and reduced anonymity are causes of doubt. In a Semantic Web, information about your identity, interests and habits are trivial to discover. A web that exposes such information about everyone has its drawbacks. One of them being that there will always be someone ready to abuse that information and make some quick money.

Traditional security standards are no longer an option here. In order to secure the Semantic Web some security measures needs to be enforced, more importantly they needs to be considered right from the beginning of expansion of the Semantic Web. There are still more hurdles to bypass, but looking back at everything that’s happened so far, it’s unlikely that they stay a hindrance for long.

for the Semantic Web,” WIRED. [Available Online]:

https://www.wired.com/insights/2014/02/ search-today-beyond-optimizingsemantic-web/.

[Accessed: 18-Sep-2016].

[3] “Diffbot Bests Google’s Knowledge Graph to Feed the Need for Structured Data.” [Available Online]. http://www.forbes.com/sites/anthonykosner/20 15/06/04/diffbot-bests-googles-knowledgegraph-to-feed-the-need-for-structureddata/#305c4c9f570d.

[Accessed: 19-Sep-2016].

[4] Kashyap, Vipul and Shklar, Leon,. “Real World Semantic Web Applications.” John Wiley and Sons(2002).

[5] Knowledge Graph,” Wikipedia, the free encyclopedia. [Accessed: 16-Sep-2016].

REFERENCES [1] Berners-Lee, Hendler, J. and Lassila, O, [Available Online].

.

http://sciam.com/article.cfm?articleID=000481 44-10D2-1C70-84A9809EC588EF21.

[2] A. K. Recker, V. S. W. K. Recker, and V. S. Works, “Search Today and Beyond: Optimizing

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Do the Roles can really be labeled?

Gayathri E S M Tech Computational Linguistics Government Engineering College, Sreekrishnapuram gayathryes @gmail.com

Roles, once we say it what comes to our mind is the characters and people we know about and its fact that they can be labeled.

And they exist because they are labeled.

Now why there is a doubt in role labelling, because it is the semantic roles that are talking about. So, what is this semantic role??

Semantic role is the actual role a participant plays in some real or imagined situation, apart from the linguistic encoding of those situations. And the Semantic role labeling, sometimes also called shallow semantic parsing, it is a task in natural language processing consisting of the detection of the semantic arguments associated with the predicate or verb of a sentence and their classification into their specific roles. For example, given a sentence like "Mary sold the book to John", the task would be to recognize the verb "to sell" as representing the predicate, "Mary" as representing the seller (agent), "the book" as representing the goods (theme), and "John" as representing the recipient. This is an important step towards making sense of the meaning of a sentence. A semantic representation of this sort is at a higher-level

of abstraction than a syntax tree. For instance, the sentence "The book was sold by Mary to John" has a different syntactic form, but the same semantic roles. Since it is a NLP task there are variety of ways including different NLP tools and machine learning methods used for doing the same. During the visit to IIITMK an incomplete work on semantic role labeling was done. And it was done using CRF tool. The tool is a simple, customizable, and open source implementation of Conditional Random Fields (CRFs) for segmenting/ labeling sequential data. It is designed for generic purpose and will be applied to a variety of NLP tasks, such as Named Entity Recognition, Information Extraction and Text Chunking. Conditional random fields (CRFs) are a class of statistical modelling method often applied in pattern recognition and machine learning, where they are used for structured prediction.

Whereas an ordinary classifier predicts a label for a single sample without regard to "neighbouring" samples, a CRF can take context into account; e.g., the linear chain CRF popular in natural language processing predicts sequences of labels for sequences of input samples.

A training data including different sentences their lexical types, morphological

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information’s are constructed and the system was trained with the same. By checking accuracy on each time the training data is improved so that all the kind of semantic roles are included and labeled. The efficiency of the system depends on how strong the training data is.

‘Broca’s’ area processes both language and music at same time

When you read a book and listen to music, the brain doesn’t keep these two tasks nicely separated. A new study shows there is an area in the brain which is busy with both at the same time: Broca’s area. This area has been long associated with language. A research team led by Richard Kunert of the Max Planck Institute for Psycholinguistics in Nijmegen discovered that a brain region under the left, which has been associated with language since the 19th century, can do more than just process language. Using an MRI scanner, the researchers discovered that this area, known as Broca’s Area, can process language and music at the same time. They also found that the two tasks can influence each other: when participants are given a particularly difficult combination of tones, it is harder for them to process the structure of a sentence.

For more details visit: http://medicalxpress.com/news/2015-11-broca-area-languagemusic.html#nRlv

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Deep Learning and Big Data Analytics Swedha B M.Tech Computational Linguistics Government Engineering College, Sreekrishnapuram swedhathrisala93@gmail.com

Hi, one of the trending areas of discussion of now a day is about Big Data analytics. And when I started thinking about its relation with others and I came across so many areas. The one that I was interested to read was about Deep Learning and Big Data Analytics. Deep Learning and Big Data Analytics are two focus of data science. The concept of Big Data has become popular as many organizations is collecting large amount of massive information. This information can be domain-specific information or non-domain specific information. Actually we analyse the data for decision making or predicting future technology. The main benefit of using the deep learning is that as we are dealing with massive amount of unsupervised data, and there will be also largely unlabled or uncategorized data. Deep learning solutions have yielded outstanding results in different machine learning applications, including speech recognition, computer vision and natural language processing. The deep learning can be thus be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive data, semantic indexing, data tagging, fast information retrieval. So, through this article we can

address each problem and analyse how these can be solved by the deep learning strategies.

The first problem is semantic indexing, simply the task involves relating the set of documents and the terms that contained in documents, which done by producing set of concepts. As we know that there are massive amount of data and for its efficient storage they require semantic indexing rather than being stored as data bit strings. The semantic indexing presents the data in more efficient manner. Deep learning can be used to generate high-level abstract data representation which is used for semantic indexing. When we go in-depth study of semantic indexing using deep learning we can see that vector representation is used as it provides faster searching and information retrieval Representation of data plays important role in indexing because it will result in easier information retrieval. Thus, the high-level abstract data representations need to be meaningful and demonstrate relational and semantic association in order to actually confer a good semantic understanding and comprehension of the input. So, vector representations of complex high-level data abstractions for indexing the data make semantic indexing feasible. And

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extending this to document level is also possible. Google have introduced a tool called “word2vec� for automated extraction of semantic representations from Big Data. It takes corpus as input and produces word vectors and this allows doing semantic indexing of data. In short we can say that the semantic indexing is a retrieval method used for retrieving large collection of stored data by the introduction of deep learning concept the problem of retrieving becomes feasible.

The other problem is in doing discriminative tasks and semantic tagging in Big Data Analytics, but this can be easily done using deep learning. Discriminative analysis in Big Data Analytics is actually a primary purpose of data analysis and it is performed in order to make the searching feasible. Also, in the recent years there is an increase in the usage of internet and lot of images storage, and massive amount of informations come from social networks, global positioning satellites, image sharing systems, medical imaging systems, military surveillance, and security systems. To deal with large scale image data collections, one approach to consider is to automate the process of tagging images and extracting semantic information from the images.

Deep Learning presents new frontiers towards constructing complicated representations for image and video data as relatively high levels of abstractions, which can then be used for image annotation and tagging that is useful for image indexing and retrieval. In the context of Big Data Analytics, here Deep Learning would aid in the discriminative task of semantic tagging of data.

Now we have been discussing the benefits of using deep learning for big data analytics.

There are areas in which much exploration is required such as learning with streaming data, dealing with high dimensional data, scalability of models, and distributed computing. One of the challenging aspects in Big Data Analytics is dealing with streaming and fast moving input data. Such data analysis is useful in monitoring tasks, such as fraud detection. It is important to adapt Deep Learning to handle streaming data including incremental feature learning and extraction, denoising autoencoders, and deep belief networks as there is a need for algorithms that can deal with large amounts of continuous input data.

The paper written by Zhou et al. describes how a Deep Learning algorithm can be used for incremental feature learning on very large datasets, employing denoising autoencoders. Denoising autoencoders are a variant of autoencoders which extract features from corrupted input, where the extracted features are robust to noisy data and good for classification purposes. The deep learning algorithms uses hidden layers to contribute to the extraction of features.

Initially they have same number of nodes which is equal to the samples. Incrementally, some of the samples may not compact with the objective function so, we need to collect that nodes and use them for adding new nodes to hidden layer. Subsequently, incoming new data samples are used to jointly retrain all the features. This incremental feature learning and mapping can improve the discriminative or generative objective functionÍž However, monotonically

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adding features can lead to having a lot of redundant features and overfitting of data.

But the usage of deep learning algorithm makes it possible to adapt to new incoming stream of an online large scale data.

to do analysis but there are some areas in which much more exploration is required. I think this article helps you to know about deep

The paper by Calandra introduces adaptive deep belief networks which demonstrate how Deep Learning can be generalized to learn from online non-stationary and streaming data. Their study exploits the generative property of deep belief networks to mimic the samples from the original data, where these samples and the new observed samples are used to learn the new deep belief network which has adapted to the newly observed data. However, a downside of an adaptive deep belief network is the requirement for constant memory consumption. So, this area require much more exploration as there are many applications in which real time informations have to be used for its proper function. Next we have the high dimensional data to be handled. Currently, Convolutional neural networks are another method which scales up effectively on high-dimensional data. The same is required for scalability of models and distributed computing.

REFERENCE

Deep learning, the upcoming technique is used in Big Data Analytics was discussed here. It has got its own advantages in order

learning and makes a way you to explore in this area. Good Day.

[1]https://journalofbigdata.springeropen.c om/articles/10.1186/s40537-014-0007-7 [Available Online: Accessed on 2nd September 2016] [2]https://en.wikipedia.org/wiki/Deep_lear ning [ Available Online: Accessed on 2nd September 2016]

Google Knowledge Graph: The Knowledge Graph is a knowledge base used by Google to enhance its search engine's search results with semantic-search information gathered from a wide variety of sources.

Knowledge Graph display was added to Google's search engine in 2012, starting in the United States, having been announced on May 16, 2012. It provides structured and detailed information about the topic in addition to a list of links to other sites. The goal is that users would be able to use this information to resolve their query without having to navigate to other sites and assemble the information themselves.

The short summary provided in the knowledge graph is often used as a spoken answer in Google Now searches. According to some news websites, the implementation of Google's Knowledge Graph has played a role in the page view decline of various language versions of Wikipedia.

For more details visit: “Knowledge Graph,�

Wikipedia, the free encyclopedia. 16-Sep-2016.

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BLOCK CHAIN TECHNOLOGY AND BITCOIN Athira M A M Tech Computational Linguistics, Sreekrishnapuram athiraswathika@gmail.com

Hi, I came to know about Block chain technology and Bitcoin when we had a course at IIITM-K, Trivandrum. It was a fabulous class and I wish to transfer this new information to all who read this article.

First of all, we must know what is Block Chain technology and then about Bitcoin.

A block chain originally, block chain is a distributed database that maintains a continuously-growing list of records called blocks secured from tampering and revision. Each block contains a timestamp and a link to a previous block. The block chain is a technology that underlies bitcoinconceived in 2008 and first implemented in 2009-where it serves as the public ledger for all transactions. In the bitcoin case, every compatible client is able to connect to the network, send new transactions to it, verify transactions, and take part in the competition to create new blocks. The competition creating new blocks is known as mining. The bitcoin design has been the inspiration for other applications.

Bitcoin is a digital currency system that enables participants to create and transfer bitcoins using a block chain which are the rails upon which the bitcoins are created and transferred.

A bitcoin is a digital descriptor of a unit of value and its ownership, one bitcoin is currently worth about $255 and this represents the market forces of supply, demand and speculation. Unlike other currencies such as gold or platinum bitcoins have no other utility value they are an entry in a block chain ledger. New transactions can be created to change the ownership of the bitcoins by forming new entries in the ledger or block chain. A bitcoin can be subdivided by 8 orders of magnitude when it is then called a Satoshi named after the Bitcoin inventor or more likely his pseudonym (1 Satoshi = 0.00000001 bitcoin), most people really don’t know who he/she/they are.

The Bitcoin rails are a decentralised ledger called the block chain that is freely available in the cloud but what you can’t do is easily change it. A new block is created every 10 minutes or so and contains all the transactions submitted by users since the last block was created. Each block has to be agreed by the community before it is added to the previous blocks, i.e. the block chain.

So approximately every 10 minutes the block chain increases in size by one block. There are currently about 351,200 blocks in the chain and each block today has about 700 transactions. The block chain effectively contains a record of every transaction from

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the start which is called the genesis block (of 50 bitcoins).

Next we need to know how the bitcoin works. First we need to install the Bitcoin wallet on your computer or mobile phone, it will generate your first Bitcoin address and you can create more whenever you need one.

You can disclose your addresses to your friends so that they can pay you or vice versa. Also a user can check their Bitcoin balance. As we know block chain is a shared public ledger on which the entire Bitcoin network relies. All confirmed transactions are included in the block chain. This way, Bitcoin wallets can calculate their spendable balance and new transactions can be verified to be spending bitcoins that are actually owned by the spender. The integrity and the chronological order of the block chain are enforced with cryptography.

In Bitcoin technology, transaction is a transfer of value between Bitcoin wallets that gets included in the block chain. Bitcoin wallets keep a secret piece of data called a private key or seed, which is used to sign transactions, providing a mathematical proof that they have come from the owner of the wallet. The signature also prevents the transaction from being altered by anybody once it has been issued. All transactions are broadcast between users and usually begin to be confirmed by the network in the following 10 minutes, through a process called mining.

Mining in this technology is a distributed consensus system that is used to confirm waiting transactions by including them in the block chain. It enforces a chronological order in the block chain, protects the neutrality of the network, and

allows different computers to agree on the state of the system. To be confirmed, transactions must be packed in a block that fits very strict cryptographic rules that will be verified by the network. These rules prevent previous blocks from being modified because doing so would invalidate all following blocks. Mining also creates the equivalent of a competitive lottery that prevents any individual from easily adding new blocks consecutively in the block chain.

This way, no individuals can control what is included in the block chain or replace parts of the block chain to roll back their own spends.

The Benefits of block chain technology as specified by Forbes are: As a public ledger system, block chain records and validate each and every transaction made, which makes it secure and reliable. All the transactions made are authorized by miners, which makes the transactions immutable and prevent it from the threat of hacking. Block chain technology discards the need of any third-party or central authority for peer-topeer transactions. Decentralization of the technology. Banks and other financial institutions have also been active in investing (time and/or money) in this space too. This was a new topic for me and I think this helps you to know much more about the Block Chain technology.

REFERENCES [1]

https://letstalkpayments.com/an-overviewof-blockchain-technology/

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[Available Online: Accessed on 5th September

Redrawing the language of brain

2016]

[2]

http://www.protectdigitalassets.com/?p=23

[Available Online: Accessed on 5

th

September

2016] [3]

https://bitcoin.org/en/how-it-works

[Available Online: Accessed on 5th September 2016]

For 140 years, scientists’ understanding of language comprehension in the brain came from individuals with stroke. Based on language impairments caused by stroke, scientists believed a single area of the brain- a hotdog shaped section in the temporal lobe of the left hemisphere called Wernicke’s region- was the center of language comprehension. Wernicke’s was thought to be responsible for understanding the meaning of single words and sentences, two separate and critical functions.

White matter fiber architecture of the brain.

Credit: Human Connectome Project.

Knowing where language comprehension is located offers a more precise target for future therapies that could potentially protect or restore language function.

For more details visit: http://medicalxpress.com/news/2015-06redrawing-language-brain.html#nRlv

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M.Tech Computational Linguistics Dept. of Computer Science and Engg, Govt. Engg. College, Sreekrishnapuram Palakkad www.simplegroups.in simplequest.in@gmail.com

SIMPLE Groups Students Innovations in Morphology Phonology and Language Engineering

Article Invitation for CLEAR- December-2016 We are inviting thought-provoking articles, interesting dialogues and healthy debates on multifaceted aspects of Computational Linguistics, for the forthcoming issue of CLEAR (Computational Linguistics in Engineering And Research) Journal, publishing on December 2016. The suggested areas of discussion are:

The articles may be sent to the Editor on or before 10th December, 2016 through the email simplequest.in@gmail.com.

For more details visit: www.simplegroups.in Editor,

Representative,

CLEAR Journal

SIMPLE Groups

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Hello world, Nowadays, information is available in different forms and it’s huge. Works have been going on for their extraction and different techniques are being developed for analysing the human behaviour pattern we need, the so called Data mining technologies. In order to make the data mining activity feasible and efficient we are researching in different areas. One among them is Big Data Analytics problems solved using Deep Learning methods. Semantic web is another area which has got wide interest among researchers. Also Semantic role labelling and Named Entity Recognition, as we know both have their own importance in Natural Language Processing.

This issue of CLEAR focuses on the researches done in the field of Data mining, Semantic web, Natural Language Processing and Block chain technology. The articles have been written up in the hopes of giving an insight into this fast emerging field. CLEAR always welcomes ideas that are refreshing yet purposive and is thankful to everyone that helps realise this target.

Simple group welcomes more aspirants in this area.

Wish you all the best!!!

Swedha

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M.Tech Computational Linguistics 2014-2016 Batch

Anoop R

Archana S M

Athira M

Bhavya K

Dhanaraj V

Krishnaprasad P

Naima Vahab

Neethu K V

Pelja Paul N

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Rekha Thankappan

Reshma R

Revathy P

Seena I T

Sini G M

Soorya K

Sooryanarayanan

Sreetha S

Suneera C M

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CLEAR September 2016 27