Transliteration based Gazetteer Preparation for Named Entity Recognition in Hindi

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Transliteration based Gazetteer Preparation for
Named Entity Recognition in Hindi
Sujan Kumar Saha, Sudeshna Sarkar, Pabitra
Mitra Department of Computer Science
Engineering Indian Institute of Technology
Kharagpur India
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Contents

• Introduction
• NER approaches
• Gazetteer and application in NER
• Gazetteer Indian language scenario
• Transliteration our approach gazetteer
  preparation
• MaxEnt based Hindi NER system
• Use of prepared gazetteers in MaxEnt system

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What is NER

• Named Entity Recognition (NER) Locate and
  Classify the Names in Text
• Example
• Jawaharlal Nehru was the first prime minister of
  India
• Per-beg Per-end
  Title Loc
• Importance
• Information Extraction, Question-Answering
• Can help Summarization, ASR and MT
• Intelligent document access
• etc

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NER Approaches

• 1. Linguistic Approach
• Rule based Models
• Requires lot of experience and grammatical
  knowledge
• Example Ralph Grishman (1995), Wakao et al.
  (1996)
• 2. Machine Learning based Approach
• ML based Models HMM, MaxEnt, CRF
• Requires huge amount of annotated data
• Example Borthwick (1999), McCallum et al.
  (2003)
• 3. Hybrid Approach
• Several ML and Rule-based systems are combined
• Example Srihari et al.(2000), ..

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Gazetteer

• Name Lists of specific type very helpful in
  identification of NEs
• Example
• Names of cities, countries etc.
• First name list, surnames
• List of towns in India
• Used in many NER systems
• Rule-based approaches and ML approaches
  gazetteers used in both

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Gazetteers in NER Systems Example

• Grishman (1995) (rule-based)
• Names of all countries, major cities, companies,
  common first names etc.
• Wakao et al. (1996) (rule-based)
• Names of locations, organizations, first names,
  human titles etc.
• Borthwick (1999) (ML-based)
• First names (1,245), corporate names (10,300),
  colleges and universities (1,225), corporate
  suffixes (244), date and time (51) etc.
• Srihari et al. (2000) (ML-based)
• Male names (3,000), female names (5,000), family
  names (14,000), location names (2,50,000) with
  category.

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Gazetteer Advantage Disadvantage

• Advantage
• Simple
• Fast
• Language Independent
• Easy to retarget
• Disadvantage
• Impossible to enumerate all names
• Collection maintenance is difficult
• Cant deal name variants
• Cant resolve ambiguity

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Gazetteer Indian Language Scenario

• Resource Poor Language
• No reasonable size publicly available list in
  Indian languages
• Web resources use of Indian languages in web is
  very little compared to English
• Relevant lists are available in English
• Need transliteration English to Indian language
  (Hindi)

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Transliteration

• Direct transliteration (English Hindi) is not
  easy
• Vocabulary size 52 vs 26
• Compound characters in Hindi
• Availability of bilingual corpora
• Many possible outcomes for a input string
• Example Bengali English transliteration system
• Joint source channel model
• Accuracy
• 69.3 word agreement ratio (Bengali English)
• 67.9 word agreement ratio (English
  Bengali)

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Our Approach

• Transliteration through an intermediate alphabet
• English string (E) is transliterated to the
  intermediate string (S1)
• Also Hindi string (H) is transliterated to the
  intermediate string (S2)
• If S1 equivalent to S2
• E and H are transliteration of each other
• Intermediate alphabet - size 34 preserves
  phonetic properties

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Transliteration - English to Intermediate

• Define the phonetic map-table
• Maps English n-gram into intermediate character
• Example few entities of map-table

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Procedure - Transliteration

• Source English, Output - Intermediate
• Scan the source string (S) from left to right
• Extract the first n-gram (G) from the string (n
  3)
• Find if it is in the map-table
• If yes, insert its corresponding intermediate
  state entity into target string T Remove the
  n-gram from S, S S G Go to step 2
• Else set n n 1 Go to step 3.

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Transliteration Hindi to Intermediate

• Convert the Hindi strings to itrans
• itrans is a representation of Indian language
  alphabets in terms of ASCII
• The map-table is available at aczoom.com/itrans
• Define a map-table for itrans to Intermediate
  transliteration
• Follow similar procedure

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Transliteration Example
Debasis Debashis Debasish
??????? ???????
intermediate
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Gazetteer Lists

• Prepared lists with size and example
• Common Location List 70 jila, nagar, road
• Location List 13019 Kolkata, New York
• Month name, Days of week 40 ravivar, March
• First Name List 9722 Suman, Kunal
• Middle Name List 35 Kumar, Chandra
• Surname List 1700 Kundu, Chopra, Gates
• Organization list 950 Bhajapa, Microsoft

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Gazetteers and Transliteration

• Collected English name lists are transliterated
  into Intermediate alphabet
• These transliterated lists are used as gazetteers
  in Hindi NER development
• Hindi strings are transliterated into
  Intermediate alphabet and searched in the
  gazetteers
• Gazetteer based NER system (If wi is in list of
  category Cj then wi is an NE)
• Accuracy Person 43.1, Location 61.44,
  Organization 48.14

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MaxEnt Based Hindi NER

• Computes P(t/h)
• t outcome, h history, gi feature, ai
  weight of gi

• An open-nlp MaxEnt tool (www.maxent.sourceforge.ne
  t) is used to compute the class-conditional
  probabilities (P(t/h))
• Beam Search Algorithm to find the most probable
  tag sequence

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Training Data

• Collected from Dainik Jagaran
• 4 classes Person, Location, Organization, Date
• Each class contains 4 subclasses Begin,
  Continue, End, Unique
• Example New/LocBeg Delhi/LocEnd, Atal/PerBeg
  Bihari/PerCon Vajpayee/PerEnd, Microsoft/OrgUniq
• Corpus size 2,43,341 words with 16482 NEs
• 6298 Person, 4696 Location, 3652 Organization and
  1845 Date Entities

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Hindi NER Features

• Static Word Feature
• Dynamic NE Tag of previous words
• Contains Digit
• Made up of 4 Digits
• Numerical Words
• Word Suffix fixed length or binary
• Word Prefix
• POS information full or coarse grained,
  binary POS features (nominal-PSP)

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Gazetteer Features

• Direct matching based decision is not taken
  Causes ambiguity
• Kharagpur Location vs IIT Kharagpur
  Organization
• Rabindranath Tagore Person vs Rabindranath
  Tagore Lane Location
• Purba may be name of person or building
• Gazetteers are used as features of MaxEnt
  Binary features
• If wi is in location list,
  location-gazetter-feature(wi) 1 else
  location-gazetter-feature(wi) 0

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Accuracy for Different Features

• The Accuracies of MaxEnt based NER system for
  different feature sets All the values are
  f-values
• Without using gazetteer information

• The highest accuracy obtained is 75.89 f-value.

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Accuracy for Different Gazetteers

• The improvement of accuracies after adding
  gazetteer features
• Individual gazetteers are capable of increasing
  the accuracy of the particular class

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Accuracy MaxEnt System Gazetteer
The transliterated gazetteers are able to
increase accuracy The highest system accuracy is
81.12 f-value
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References

• Andrew Borthwick. 1999. A Maximum Entropy
  Approach to Named Entity Recognition. Ph.D.
  thesis, Computer Science Department, New York
  University.
• Daniel M. Bikel, Scott Miller, Richard Schwartz,
  and Ralph Weischedel. 1997. Nymble A high
  Performance Learning Name-finder. In Proceedings
  of the Fifth Conference on Applied Natural
  Language Processing, pages 194-201.
• Ekbal Asif, S. Naskar, S. Bandyopadhyay. 2006.
  A Modified Joint Source Channel Model for
  Transliteration. In Proceedings of the COLING/ACL
  2006, Australia, 191-198.
• Grishman Ralph. 1995. Where's the syntax? The New
  York University MUC-6 System. In Proceedings of
  the Sixth Message Understanding Conference.
• R Srihari, C Niu, W Li. 2000. A Hybrid Approach
  for Named Entity and Sub-Type Tagging. In
  Proceedings of the sixth conference on Applied
  natural language processing.
• Wakao T., R. Gaizauskas and Y. Wilks. 1996.
  Evaluation of an Algorithm for the Recognition
  and Classification of Proper Names. In
  Proceedings of COLING-96.

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Thank You