Named Entity Recognition

1
Named Entity Recognition

• Sobha Lalitha Devi
• AU-KBC Research Centre
• Chennai

2
Named Entity(NE) Recognition

• What is NE and What is not an NE
• How to identify NE
• Tagset and Annotation Guidelines
• Methods Used in developing NER

3
Why do NER?

• Key part of Information Extraction system
• Robust handling of proper names essential for
  many applications such as Summarization, IR,
  Anaphora,.........
• Pre-processing for different classification
  levels
• Information filtering
• Information linking

4
What is NER ?

• NER involves identification of proper names in
  texts, and classification into a set of
  predefined categories of interest.
• Three universally accepted categories
• Person, location and organisation
• Other common tasks recognition of date/time
  expressions, measures (percent, money, weight
  etc), email addresses etc.
• Other domain-specific entities names of Drugs,
  Genes, medical conditions, names of ships,
  bibliographic references etc.

5
NER Definition

• Named entity recognition (NER) (also known as
  entity identification (EI) and entity extraction)
  is the task that locate and classify atomic
  elements in text into predefined categories such
  as the names of persons, organizations,
  locations, expressions of times, quantities,
  monetary values, percentages, etc.
• John sold 5 companies in 2002.
• ltENAMEX TYPE"PERSON"gtJohnlt/ENAMEXgt sold ltNUMEX
  TYPE"QUANTITY"gt5lt/NUMEXgt companies in ltTIMEX
  TYPE"DATE"gt2002lt/TIMEXgt.

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What is not NER?

• NER is not event recognition.
• NER does not create templates,
• NER does not perform co-reference or entity
  linking,
• though these processes are often implemented
  alongside NER as part of a larger IE system.
• NER is not just matching text strings with
  pre-defined lists of names.
• It recognises entities which are being used as
  entities in a given context.
• NER is not an easy task!

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Named Entity and Philosophy of Language

• Proper Names are defined by
• Descriptivist's theory of Names
• Frege, Russell, Ludwig , Wittgenstein and John
  Searle
• Causal theory of Reference
• Saul Kripke

8

• Descriptivist's theory of Names
• Proper names either are synonymous with
  descriptions, or have their reference determined
  by virtue of the name's being associated with a
  description or cluster of descriptions that an
  object uniquely satisfies.
• Causal theory of Reference
• Proper names refer to an object by virtue of a
  causal connection
• with the object as mediated through communities
  of speakers. That is , proper names, in contrast
  to descriptions, are rigid designators.
• Rigid designators A proper name refers to the
  named object in every possible world in which the
  object exists.
• Descriptions designate a proper name as
  different objects in different possible worlds.

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Proper Names and Definite Descriptions

• A meaning of a Sentences involving Proper names
  could be substituted by a contextually
  appropriate description for a name.
• eg Otto von Bismarck can be known or described
  as the first Chancellor of the German Empire
• Kripke argues that definite descriptions cannot
  be rigid designators . Because definite
  descriptions cannot be same/similar in all
  possible worlds
• More on Kripkes Proper name in Naming and
  Necessity 1980

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What is Named Entity

• Named Entities are
• A Noun Phrase
• Rigid Designators It designates/denotes the
  same thing in all possible worlds in which the
  same thing exists and does not designate anything
  else in those possible worlds in which that same
  thing does not exist

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EXAMPLES for Named Entity and not a Named entity

• Hotel Taj Hotel
• Flower Rose Flower
• Beach Kovalam Beach
• Airport Indira Gandhi International airport
• The School Good Shepherd School
• Prime Minister Mr. Manmohan Singh

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Some problems in indentifying NE

• Variation of NEs.
• Manmohan Singh, Manmohan, Dr. Manmohan Singh
• Ambiguity of NE types
• 1945 (date vs. time)
• Washington (location vs. person)
• May (person vs. month)
• Tata (person vs. organization)

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Ambiguity Examples

• Person vs Location
• Sir C. P Ramaswamy was the Divan of Travancore
  (Per)
• Sir C.P Ramaswamy Road is in Chennai (Loc)
• Person vs Organization
• Anil Ambani opened Reliance Fresh (Per)
• Reliance Fresh is under Anil Amabani Group Ltd
  (Org)

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More complex problems in NER

• Issues of style, structure, domain, genre etc.
• Punctuation, spelling, spacing, formatting, .all
  have an impact
• Dept. of Computing and Information Science
• Manchester Metropolitan University
• Manchester
• United Kingdom
• gt Tell me more about Leonardo
• gt Da Vinci

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Problems in NE Task Definition

• Category definitions are intuitively quite clear,
  but there are many grey areas.
• Many of these grey area are caused by metonymy.
• Person vs. Artefact
• Organisation vs. Location
• Company vs. Artefact
• Location vs. Organisation

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Tagset for Named Entity

• ACE tagset is Hierarchical
• ACE-Automatic Content Extraction
• The tagset
• CLIA-is Hierarchical -Similar to ACE
• Developed for two domains
• Tourism and Health

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• Manmade
• Religious Places
• Roads/Highways
• Museum
• Theme parks/Parks/Gardens
• Monuments
• Facilities
• Hospitals
• Institutes
• Library
• Hotel/Restaurants/Lodges
• Plant/Factories
• Police Station/Fire Services
• Public Comfort Stations
• Airports
• Ports
• Bus-Stations
• Locomotives
• Artifacts

• TAGSET
• ENAMEX
• Person
• Individual
• Family name
• Title
• Group
• Organization
• Government
• Public/private company
• Religious
• Non-government
• Political Party
• Para military
• Charitable
• Association
• GPE (Geo-political Social Entity)
• Media
• Location

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Tagset Continued
Tagset Counts First Level Tags -3 Second Level
-43 Third Level 40 Total -
86

• NUMEX
• Distance
• Money
• Quantity
• Count
• TIMEX
• Time
• Date
• Day
• Period

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How to Annotate

• 1.ENAMEX
• 1.1 Person
• 1.1.1 Individual
• These refer to names of each individual person,
  also includes names of fictional characters found
  in stories/novels etc.
• Tag Structure
• ltENAMEX TYPE PERSON SUBTYPE_1
  INDIVIDUALgt abc lt/ENAMEXgt
• Examples
• English
• ltENAMEX TYPE PERSON SUBTYPE_1
  INDIVIDUALgtAbdul Kalamlt/ENAMEXgt

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Annotation continued

• 1.1.1.1 Family name
• In general we find that a person name consists
  of a family name. Whenever an instance of
  individual name occurs with family name, then
  that part of the name, which refers to family
  name, must be tagged specifically with subtag
  FAMILYNAME as shown below.
• Tag Structure
• ltENAMEX TYPE PERSON SUBTYPE_1 INDIVIDUAL
  SUBTYPE_2 FAMILYNAMEgt abc lt/ENAMEXgt
• Examples
• English
• ltENAMEX TYPEPERSON SUBTYPE_1INDIVIDUALgt
  Lalu PrasadltENAMEX TYPE PERSON SUBTYPE_1
  INDIVIDUAL SUBTYPE_2 FAMILYNAMEgtYadavlt/ENAMEX
  gtlt/ENAMEXgt

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NE Types

The Named entity hierarchy is divided into three
major classes Entity Name, Time and Numerical
expressions.
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Entity Types
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Entity Name Types

• Persons are entities limited to humans. A
  person may be a single individual or a group.
  Individual refer to names of each individual
  person. Group refers to set of individual
• Location entities are limited to geographical
  entities such as geographical areas like names of
  countries, cities, continents and landmasses,
  bodies of water, and geological formations.
• Organization entities are limited to
  corporations, agencies, and other groups of
  people defined by an established organizational
  structure

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Examples for Entity Name Types

• En SitaPERSON is working at HCLORGANIZATION
  , which is in Chennai LOCATION
• Ta Seetha PERSON chennaiyilrukkira LOCATION
  HCLlil ORGANIZATION
• En Sita Chennai
  HCL
• velaiseikirAl.
• Working
• Ml Seetha PERSON chennaiyillula LOCATION
  HCLlil ORGANIZATION
• En Sita Chennai
  HCL
• jolicheyyunnu.
• Working
• Hi Seetha PERSON HCL ORGANIZATION main kaam
  kar raha hai, jo
• En Sita HCL
  work is
  which
• chennai LOCATION main hain.
• Chennai in

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Entity Name Types
Facilities are limited to buildings and other
permanent man-made structures and real estate
improvements like hospitals, airport, colleges,
libraries etc. En Appolo Hospital FACILITY is
in Chennai LOCATION Ta Appallo
maruthuvamanAiFACILITY ChennaiyilLOCATION
irukkirathu Ml Appolo AsupathriFACILITY
chennaiyilLOCATION aaN Hi Appolo
aspathaalFACILITY chennaiLOCATION mein
haim.
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Entity Name Types
A locomotive entity is a physical device
primarily designed to move an object from one
location to another, by carrying, pulling, or
pushing the transported object. En Ananthapuri
ExpressLOCOMOTIVE departs from Chennai
LOCATION at 7.30pm Time. Hi Ananthapuri
express LOCOMOTIVE Chennai LOCATION se rAth
7.30 TIME ko ravana hoga Ml Ananthapuri
eksprass LOCOMOTIVE chennaiyilninn LOCATION
raathri 7.30 maNikk TIME puRappetum. Ta
Ananthapuri viraivu rayil LOCOMOTIVE
chennaiyilirunthu LOCATION iRavu 7.30 maNikku
TIME puRappatukirathu
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Entity Name Types
Artifact entities are objects or things, produced
or shaped by human craft, such as tools,
weapons/ammunition, art paintings, clothes,
ornaments, medicines En Vinayaga Statue
ARTIFACT is looking beautiful Ta Vinayakarin
Silai ARTIFACT pArpatharkku alakAkAkairukkirathu
Ml ganapathi vigrahamARTIFACT baMgiyaayi
irikkunnu. Hi Vinayaka moorthi ARTIFACT achi
lagh rahi haim.
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Entity Name Types
Entertainment entities denote activities, which
are diverting and hold human attention or
interest, giving pleasure, happiness, amusement
especially performance of some kind such as
dance, music, sports, events. En Flower
Exhibition ENTERTAINMENT is held at
HyderabadLOCATION Ta Malar kankAtchi
ENTERTAINMENT hyderabaadilLOCATION
Nadaiperukirathu Ml pushpa pradarshanam
ENTERTAINMENT hyderabaadil LOCATION
natakkunnu Hi phool pradarshnii ENTERTAINMENT
hyderabad LOCATION meN Ayojith kiyaa jAthA hai
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Entity Name Types
Materials refer to the names of food items,
cuisines, chemicals and cosmetics En
HoneyMATERIALS is good for face Ta
ThEnMATERIALS mukaththiRku nallathu Ml Madhu
MATERIALS mukaththinu nallathAN Hi Shahad
MATERIALS chehare ke liye achcha hai.
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Entity Name Types
ORGANISMS These are the names of different
animal species including birds, reptiles,
viruses, bacteria and names of herbs, medicinal
plants, shrubs, trees, fruits, flowers etc. En
Peacock ORGANISM is the national bird of
India LOCATION Ta Mayil ORGANISM
InthiyAvin LOCATION thEciyappaRavai Akum. Ml
Mayil ORGANISM indyayute LOCATION
raashtrapakshi AN. Hi Mor ORGANISM
bhaarath LOCATION kaa raashtrIya pakshi hai.
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Entity Name Types
Disease Names of disease, symptoms, diagonisis
and treatment are comes under this type. En
Smoking Causes Cancer DISEASE Ta
PukaippithithalAl puRRuNoi DISEASE
varukiRathu Ml pukavali aRbhudham DISEASE
uNtAkkunnu Hi dhumrapan kaansar DISEASE ka
kaaraN banaatha hai.
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Numerical Expressions
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Numerical Expressions

• Distance refers to the distance measures such as
  kilometers, Centimeters, meters, acres, feet etc.
• Example 10 cm., twenty feet, 15 hectares
• Money specifies the different currency value such
  as rupee, euro, Dinar, dollar etc.
• Example Rs. 1000, 250 Euro, 160
• Count denotes the number (or counts) of Items/
  articles/things etc.
• Example 5 subjects, 12 students, 20 books
• Quantity measurements like liters, tons, grams,
  volts etc. are comes under this category.
• Example 20 litres, 22 kg, 50g, 100 volts

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Time Expressions
35
Temporal Expressions

• Temporal expressions are the entities refers to
  time, date, year, month and day
• Time These refer to expressions of time,
  includes different forms
• of expressing time. This also includes Hours,
  minutes and seconds.
• Example
• 5o clock in the morning
• 9.30 a.m.
• Evening 6.30 p.m.
• Date This refers to expressions of Date such as
  13/12/2001 etc in
• different forms. This also includes month, date
  and year
• Example
• August 15 1947
• 1956
• September 11

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Temporal Expressions

• Day These are expressions, which convey days in
  a year. Also it can include
• days occurring weekly /fortnightly/ monthly
  /quarterly/ biennial etc.
• Example
• Sunday
• Tomorrow
• Today
• Yesterday
• Special Day refers to special days in a year
• Example
• Gandhi Jayanthi
• Rama Navami

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Temporal Expressions

• Period refers to expressions, which express
  duration of time or
• time periods or time intervals.
• Example
• 17 th century
• 10 minutes
• 10 a.m. to 12 p.m.
• One year

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Methodologies

• Methods
• Rule Based
• Machine Learning
• Hidden Markov Model (HMM)
• Naïve Bayes Classifier
• Maximum Entropy Markov Model (MEMM)
• Conditional random Fields (CRF)
• 4) Hybrid Approach

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Challenges of NER in Indian Languages

• Following are the major challenges encountering
  in Indian Languages.
• Agglutination
• Ambiguity
• Between Proper and common nouns
• Between named entities
• Lack of Capitalization

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Challenges of NER in Indian Languages
Agglutination In Dravidian languages, words
consist of a lexical root to which one or more
affixes are attached. Example in Tamil 1) Ta
Ramanaiththavira (otherthan
Raman) 2) Ta Cevvaiyandru (On
Tuesday) 3) Ta Inthiyavilllula (In
India) 4) Ta KannanaippaRRikkondu
(hold onto Kannan)
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Challenges of NER in Indian Languages
Example in Malayalam 1) Ml hemayiluNtaayirunna
(that which Hema have) 2) Ml
Chennaiyilethunna (reach in Chennai) 3)
Ml arabikatalinaBimukhamaayi
(towards the arabian sea) 4) Ml
kaaSiyilekkozhukunna ( flowing
towards kaaSi)
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Challenges of NER in Indian Languages

• Ambiguity
• Comparatively Indian languages suffer more due to
  the ambiguity that exists between common proper
  nouns and between named entities itself. In some
  cases same word can refer to different named
  entity types. Those instances can recognized by
  contextual information.
• Examples
• Hi Akash - Person name and Sky
• Hi Sooraj - Person name and Sun
• Hi Chaanth Moon and Silver
• Hi Aam Mango and Common
• Ml Roopa Person name and Rupee
• Ml Madhu Person name and Honey
• Ml Mala Person name and Garland

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Challenges of NER in Indian Languages

• Ta Thinkal - Day and Month
• Ta Malar - Person name and Flower
• Ta Chevvai - Day and planet
• Ta Shakthi Person name and Power
• Ta MAlai Evening and Garland
• Ta Ml Velli Silver, Planet, Day

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Challenges of NER in Indian Languages
Spell Variation Due to the different writing
styles same entity is represented in various word
forms. In Tamil, sanskirit letters such as ja,
sha, sri Ha are replaced by sa,ciri,
ka Example Roja can be written as
Rosa Srimathi - cirimathi Raja -
rasa ShajahAn - sajakAn
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Challenges of NER in Indian Languages

• Lack of Capitalization
• In English and some other European languages
  capitalization is considered as the important
  feature to identify proper noun.
• It plays a major role in NE identification.
• Unlike English capitalization concept is not
  found in Indian languages.

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Nested Entities
Nested Entities Refers to the named entities
which occurs within another named entities. Also
called as embedded entities. Ta Mathurai
LOCATION MeenAtchi AmmanPERSON
KoyilRELPLACE En Mathurai
Meenatchi Amman Temple Ml
Nittoor PERSON Srinivasa rao PERSON En
Nitoor Srinivasa rao Hi
Rajeev PERSON MArg ROAD En Rajeev
Road
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Approaches in Named Entity Resolution

• Dictionary Look-up
• Rule based ( Using lexical, contextual and
  morphological information)
• Maximum entropy theory based
• Hidden Markov Model
• Conditional Random Fields
• Hybrid methods (Statistical Linguistics)

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Dictionary (Gazetteers) Look-up Approach

• Uses Dictionaries for identifying NERs (
  Gazetteers)
• Gazetteer contains NEs from all domains
• Advantage
• Very simple approach
• Gives very high precision

49
Disadvantages of Dictionary Approach

• Preparation of exhaustive dictionary is a tedious
  and expensive process.
• The dictionary should cover the different
  spellings of the same place.

50
Rule Based Approach

• Rule Based System
• Needs more rules to tag all kinds of NE
• Advantages
• Rich and expressive rules
• Good results
• Disadvantages
• Requires huge experience and grammatical
  knowledge
• Experts to craft rules are expensive
• Highly domain specific ( not portable to a new
  domain)

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General difficulties

• Italy's business world was rocked by the
  announcement last Thursday that Mr. Verdi would
  leave his job as vice-president of Music Masters
  of Milan, Inc. to become operations director of
  Arthur Andersen".
• Capitalization useless for first word
• S not part of name "Italy"
• Date is "last Thursday" not "Thursday"
• Milan is location, not organization
• Arthur Andersen is organization, not person

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Rules success and failure

• Title Capitalized_Word Title Person_Name
• Correct Mr. Jones
• Incorrect Mrs. Field's Cookies (corporation)
• Month_name number_less_than_32 Date
• Correct February 28
• Incorrect Long March 3 (a Chinese Rocket)
• From Date to Date Date
• Correct from August 3 to August 9
• Incorrect I moved my trip from April to June
  (two
• separate dates)

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Statistical based approach

• Need to identify features
• Feature selection has to be correct for all types
  of NE
• Development of Tagged Corpus
• The Corpus should contain all types of tags in
  appropriate number
• Domain based corpus has to be generated.

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Automated approaches

• Address drawbacks of hand-coded system
• Automated training
• Human-annotated (with desired output
• standards) training data
• Annotation requires less effort and expertise
• than hand-coding rules
• Annotation accuracy
• Two annotators for checking, third annotator to
• resolve disputes

55
Literature Survey

• Named Entity Recognition was one of the tasks
  defined in Message Understanding
  Conference(MUC) 6.
• A survey on Named Entity Recognition was done by
  David Nadeau (2007).
• 3) Techniques used include
• - rule based technique by Krupka (1998)
• - using maximum entropy by Borthwick (1998)
• - using Hidden Markov Model by Bikel (1997)
• - bootstrapping approach using concept based
  seeds (Niu et al., 2003)
• - hybrid approaches such as rule based tagging
  for certain entities such as date, time,
  percentage and maximum entropy based approach for
  entities like location and organization (Rohini
  et al.,2000)
• 4) The Stanford NER software (Finkel et al.,
  2005), uses linear chain CRFs in their
• NER engine. Here they identify three classes
  of NERs viz., Person, Organization
• and Location.

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References

• Arulmozhi, P. and Sobha, L. (2006). HMM-based
  Part of Speech Tagger for Relatively Free
• Word Order Language. Advances in Natural Language
  Processing, Research in Computing Science
  Journal, Mexico Volume18, pp. 37-48.
• Bikel, D. M. Miller, S. Schwartz, R. Weischedel,
  R. (1997). Nymble A high-performance learning
  name-finder. In Fifth Conference on Applied
  Natural Language Processing. pp. 194201.
• Borthwick, A. Sterling, J. Agichtein, E. and
  Grishman, R. (1998). Description of the MENE
  named Entity System. In Seventh Machine
  Understanding Conference (MUC-7).
• Chen, W. Zhang, Y. and Isahara, H. (2006).
  Chinese Named Entity Recognition with Conditional
  Random Fields. In Fifth SIGHAN Workshop on
  Chinese Language Processing, Sydney. pp.118-121.
• Ekbal, A. Bandyopadhyay, S. (2009). A Conditional
  Random Field Approach for Named Entity
  Recognition in Bengali and Hindi. Linguistic
  Issues in Language Technology, 2(1). pp.1-44.

57
References

• Finkel, J. N. Grenager, T. and Manning, C.
  (2005). Incorporating Non-local Information into
  Information Extraction Systems by Gibbs Sampling.
  In 43nd Annual Meeting of the Association for
  Computational Linguistics (ACL 2005). pp.
  363-370.
• Finkel, J. Dingare, S. Nguyen, H. Nissim, M.
  Sinclair, G. and Manning, C. (2004). Exploiting
  Context for Biomedical Entity Recognition from
  Syntax to the Web. In Joint Workshop on Natural
  Language Processing in Biomedicine and its
  Applications, (NLPBA), Geneva, Switzerland.
• Gali, K. Surana, H. Vaidya, A. Shishtla, P.
  Sharma, D. M. (2008). Aggregating Machine
  Learning and Rule Based Heuristics for Named
  Entity Recognition. In Workshop on NER for South
  and South East Asian Languages, IJCNLP-08,
  Hyderabad, India.
• Kumar, K. N. Santosh, G. S. K. Varma, V. (2011).
  A Language-Independent Approach to Identify the
  Named Entities in under-resourced languages and
  Clustering Multilingual Documents. In
  International Conference on Multilingual and
  Multimodal Information Access Evaluation,
  University of Amsterdam, Netherlands.
• Lafferty, J. McCallum, A. Pereira, F. (2001).
  Conditional Random Fields for segmenting and
  labeling sequence data. In ICML-01, pp. 282-289.
• Loinaz, I.A. Uriarte, O. A. Ramos, N. E. Castro,
  M. I. F. D (2006). Lessons from the Development
  of Named Entity Recognizer for Basque. Natural
  Language Processing, 36. pp. 25 37.
• McCallum, A. and Li, W. (2003). Early Results for
  Named Entity Recognition with Conditional Random
  Fields, Feature Induction and Web-Enhanced
  Lexicons. In Seventh Conference on Natural
  Language Learning (CoNLL).

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References

• Nadeau, David and Sekine, S. (2007) A survey of
  named entity recognition and classification.
  Linguisticae Investigationes 30(1). pp.326.
• Niu, C. Li, W. Ding, J. Srihari, R. K. (2003).
  Bootstrapping for Named Entity Tagging using
  Concept-based Seeds. In HLT-NAACL03, Companion
  Volume, Edmonton, AT. pp.73-75.
• Pandian, S. Lakshmana, Geetha, T. V. and Krishna.
  (2007). Named Entity Recognition in Tamil using
  Context-cues and the E-M algorithm. In the
  Proceedings of the 3rd Indian International
  Conference on Artificial Intelligence, Pune,
  India. pp. 1951 -1958.
• Sasidhar, B., Yohan, P.M., Babu, V.A., Govarhan,
  A.(2011). A Survey on Named Entity Recognition in
  Indian Languages with particular reference to
  Telugu. J. International Journal of Computer
  Science Issues, Volume. 8, pp. 1694-0814 .
• Sobha, L., Vijay Sundar Ram. R. (2006). "Noun
  Phrase Chunker for Tamil", In Proceedings of
  Symposium on Modeling and Shallow Parsing of
  Indian Languages, Indian Institute of Technology,
  Mumbai, pp 194-198.
• Srihari, R.K. Niu, C. Yu, L. (2000). A Hybrid
  Approach for Named Entity Recognition in Indian
  Languages. In 6th Applied Natural Language
  Conference, pp. 247-254
• Gupta, S. and Bhattacharyya, P. (2010). Think
  globally, apply locally using distributional
  characteristics for Hindi named entity
  identification. In 2010 Named Entities Workshop,
  Association for Computational Linguistics
  Stroudsburg, PA, USA
• Vijayakrishna, R. and Sobha, L. (2008). Domain
  focused Named Entity for Tamil using Conditional
  Random Fields. In IJNLP-08 workshop on NER for
  South and South East Asian Languages, Hyderabad,
  India. pp. 59-66

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Literature Survey

• Indian Languages
• 5) Named Entity recognition for Hindi, Bengali,
  Oriya, Telugu and Urdu (some of the major Indian
  languages) were addressed as a shared task in the
  NERSSEAL workshop of IJCNLP. The tagset used here
  consisted of 12 tags.
• 6) Vijayakrishna Sobha (2008) worked on Domain
  focused Tamil Named Entity Recognizer for Tourism
  domain using CRF. It handles nested tagging of
  named entities with a hierarchical tag set
  containing 106 tags. They considered root of
  words, POS, combined word and POS, Dictionary of
  named entities as features to build the system.
• 7) Pandian et al (2007) have built a Tamil NER
  system using contextual cues and E-M algorithm.
• 8) The NER system (Gali et al., 2008) build for
  NERSSEAL-2008 shared task which combines the
  machine learning techniques with language
  specific heuristics. The system has been tested
  on five languages such as Telugu, Hindi, Bengali,
  Urdu and Oriya using CRF followed by post
  processing which involves some heuristics.

60
Thank you