1. RECOGNISING NOMINALISATIONS
Supervisors: Dr. Alex Lascarides
Dr. Mirella Lapata
(Andrew) Yuk On KONG
University of Edinburgh

2. DEFINITION
“Nominalisation refers to the process of forming a noun from some other word-class. (e.g. red+ness)
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)

3. 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).

4. 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

5. REGULAR? Nominalise nominalisation Interpret interpretation
Interrupt interruption
Associate association
delete deletion
break breakage
leak leakage

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

7. VERB=NOUN Debate Debate (not debation); debater Pay pay Love love
Boss boss
Stand stand
purchase purchase
Lie lie (“tell a lie”)
(cf lie down)

8. VERB=NOUN (except stress)
transfer transfer
transport transport
import import
rebel rebel; (rebellion)

9. 1 VERB, >1 NOUNS Collect collection; collector
Interpret interpretation; interpreter
Cover cover; coverage
Conduct conduction; conductor;
Depend dependant/dependent; dependence; dependency

10. SEMANTICS Conduct conduction (conduct electricity/heat)
Conduct conduct (behave/organise)

11. WHEN TO USE WHICH SUFFIX
-tion/-sion
er/or
Debate debater
Talk talker
Collect collector
Conduct conductor

12. IRREGULAR NOMINALISATION
Choose choice
Succeed success;succession;successor
Decide decision
Sell sale

13. PSEUDO-NOMINALISATION
mote?? Motion
(noun; a very small piece of dust)
Depart Departure; Department???
Apart apartment????

14. 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

15. HOW ? nominalisation is a productive morphological phenomenon:
list all acceptable nominalised forms?
New words?

16. 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).

17. SCHONE AND JURAFSKY (2001)
Schone and Jurafsky (2001) have performed work for acquiring cognates and morphological variants. 
Induced semantics—Latent Semantic Analysis (LSA)
Induced orthographic info
Induced syntactic info
Transitive information
Affix frequencies

18. 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.

19. 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 > 50% and discard any other

20. 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

21. 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

22. 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.)

23. <item>
<s n=086>
<w NN1-VVG>Shopping <w PRP>including <w NN1>collection <w PRF>of
<w NN2>prescriptions
</item>
<s n=087>
<w VVG>Daysitting <w CJC>and <w VVG>nightsitting

24. 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))

25. MILESTONES 6/2002 Experiment 1—baseline 7/2002 Experiment 2
8/2002 Write-up
9/2002 Finalise report