RECOGNISING NOMINALISATIONS

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RECOGNISING NOMINALISATIONS

• Supervisors Dr. Alex Lascarides
• Dr. Mirella Lapata
• (Andrew) Yuk On KONG
• University of Edinburgh

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DEFINITION

• Nominalisation refers to the process of forming
  a noun from some other word-class. (e.g.
  redness)
• or (in classical transformational grammar
  especially) the derivation of a noun phrase from
  an underlying clause (e.g. Her answering of the
  letter.from She answered the letter).
• The term is also used in the classification of
  relative clauses (e.g. What concerns me is her
  attitude). (Crystal 1997)

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• Nominalisations (1st definition) from verbs only
  are considered here, e.g. "statement" from
  "state".
• Problem WORD--noun? from a verb or not?
• Nominalsations derived from verbs are very
  productive in English and are usually created by
  means of suffixation (i.e., suffixes that form
  nouns are attached to verb bases).

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EXCLUSIONS

• Nominals, e.g. the poor, the wounded
• Nominalisation NOT From Verb, e.g. redness
• -ing form, e.g. the making of the movie
• Antidisestablish-ment-arian-ism

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REGULAR?

• Nominalise nominalisation
• Interpret interpretation
• Interrupt interruption
• Associate association
• delete deletion
•  
• break breakage
• leak leakage

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• Confine confinement
• Refine refinement
• (but
• define definition)
•  
• submit submission
• admit admission (but also admittance)
• remit remission remittance remit

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VERBNOUN

• Debate Debate (not debation) debater
• Pay pay
• Love love
• Boss boss
• Stand stand
• purchase purchase
• Lie lie (tell a lie)
• (cf lie down)

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VERBNOUN (except stress)

• transfer transfer
• transport transport
• import import
• rebel rebel (rebellion)

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1 VERB, gt1 NOUNS

• Collect collection collector
• Interpret interpretation interpreter
• Cover cover coverage
• Conduct conduction conductor
• Depend dependant/dependent dependence
  dependency

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SEMANTICS

• Conduct conduction (conduct electricity/heat
  )
• Conduct conduct (behave/organise)

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WHEN TO USE WHICH SUFFIX

• -tion/-sion
• er/or
• Debate debater
• Talk talker
• Collect collector
• Conduct conductor

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IRREGULAR NOMINALISATION

• Choose choice
• Succeed successsuccessionsuccessor
• Decide decision
• Sell sale

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PSEUDO-NOMINALISATION

• mote?? Motion
• (noun a very small piece of dust)
• Depart Departure Department???
• Apart apartment????

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WHY BOTHER?

• The identification of nominalisations and their
  associated verbs (e.g. "statement" and "state").
  important for a number of NLP tasks
• machine translation
• information retrieval
• automatic learning of machine-readable
  dictionaries
• grammar induction

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HOW ?

• nominalisation is a productive morphological
  phenomenon
• list all acceptable nominalised forms?
• New words?

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techniques NOT focusing on nominalisations

• build rules
• machine-learning approaches to induce
  morphological structures using large corpora
• knowledge-free induction of inflectional
  morphologies (Schone and Jurafsky 2001).

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SCHONE AND JURAFSKY (2001)

• Schone and Jurafsky (2001) have performed work
  for acquiring cognates and morphological
  variants. 
• Induced semanticsLatent Semantic Analysis (LSA)
• Induced orthographic info
• Induced syntactic info
• Transitive information
• Affix frequencies

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GOAL OF THIS STUDY

• The principal goal of this project is to develop
  a system which can recognise nominalisations,
  together with the verbs from which they are
  derived.

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EXPERIMENT 1 (baseline)

• identify nouns using the tags in the corpus
• identify potential nominalisations from the list
  of nouns with a list of nominalisation suffixes
• find the corresponding potential verb for each by
  identifying the verb (from among verbs as tagged)
  that shares with it the greatest number of
  letters in sequence
• accept a pair of nominalisation and verb if the
  letter matched gt 50 and discard any other

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EXPERIMENT 2

• using decision tree to build a model
• possible features include
• -letter similarity between verbs and nouns
• -suffix frequency
• -verb frequency
• -verb semantics
• -subject of noun
• -subject of verb

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EVALUATION

• experiments will be based on the BNC corpus.
• The obtained nominalisations will be evaluated
  against the CELEX morphological lexicon and
  manually annotated data.
• Precision, recall and F-score

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BRITISH NATIONAL CORPUS

• Over 100 million words
• Corpus of modern English
• Both spoken (10) and written (90)
• Each word is automatically tagged by the CLAWS
  stochastic POS tagger
• 65 different tags
• encoded using SGML to represent POS tags and a
  variety of other structural properties of texts
  (e.g. headings, paragraphs, lists, etc.)

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• ltitemgt
• lts n086gt
• ltw NN1-VVGgtShopping ltw PRPgtincluding ltw
  NN1gtcollection ltw PRFgtof
• ltw NN2gtprescriptions
• lt/itemgt
• ltitemgt
• lts n087gt
• ltw VVGgtDaysitting ltw CJCgtand ltw VVGgtnightsitting
• lt/itemgt

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CELEX

• English, Dutch and German
• Annotated by human using lemmata from two
  dictionaries of English
• 52,446 lemmata and 160,594 wordforms
• orthographic, phonological, morphological,
  syntactic and frequency information
• morphological structure, e.g. ((celebrate),(ion))

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MILESTONES

• 6/2002 Experiment 1baseline
• 7/2002 Experiment 2
• 8/2002 Write-up
• 9/2002 Finalise report