Comments%20on%20Guillaume%20Pitel:%20

1
Comments on Guillaume Pitel Using bilingual LSA
for FrameNet annotation of French text from
generic resources

• Gerd Fliedner
• Computational Linguistics
• Saarland University

2
Comments/Thoughts

• Useful approach, as it can potentially speed up
  and support annotation and thus making new
  FrameNets.
• Uses only few resources, therefore extendable to
  other language pairs (in principle).
• First experiments only.

3
Multilingual FrameNets

• Having FrameNet for as many languages as possible
  would be nice.
• There are numerous monolingual and cross-lingual
  applications.
• BUT Building a FrameNet is knowledge and
  labour intensive work, and thus expensive,
  funding may be a problem.

4
Bootstrapping Multilingual FNs
LSA

• (Re-) Use as much knowledge from existing
  FrameNets as possible.
• Ease the task of annotators by making useful
  suggestions.
• Use automatic methods for knowledge acquisition.

Swamp of Language
5
More than one strand of hair may be needed

• By the way Change_hair_configuration is not yet
  in FN.

6
FR.FrameNet

• In FR.FrameNet, several methods have been
  explored that could reduce time and costs of
  building new FrameNets.
• Tasks explored
• Lexical Unit (Frame Evoking Element) transfer
• Identify Frame Elements
• Disambiguating LU-Frame Assignment

7
Lexical Unit Transfer

• Can be seen as the task of finding and
  disambiguating translation pairs (links to
  Machine Translation, lexicography).
• Extract disambiguated translations from existing
  cluster-based dictionary.
• Some manual annotation required, but relatively
  fast and simple way of acquiring a solid core
  lexicon.

8
Manual Filtering

• Is frame information currently used for
  disambiguation?
• How is the manual annotation done? Sounds like
  rules of thumb. Guidelines?
• How is it evaluated?

9
Resources needed

• Lexical unit transfer
• English FrameNet ?
• Large coverage bi-lingual dictionary
  (source?target language, optimally
  sense-disambiguated) ?
• Corpus in target language ?
• (Some) manual annotation ?
• (Read ? OK, ? may be problem for small
  languages, ? may be problem for small projects)

10
Lexical Unit Transfer Other Possibilities

• Using human readable resources
• Use existing dictionaries
• Problem Disambiguation
• Using machine readable resources
• Use Euro WordNet or similar
• Problem again Disambiguation
• Use parallel corpora
• PadóLapata, AAAI-05

11
Identify Frame Elements

• Core idea The same semantic restrictions/preferen
  ces should apply to Frame Elements in source and
  target language.
• How can these semantic preferences be learned?
• First step Learn cross-lingual semantic
  similarity
• Second step Identify Frame Elements in one
  language and transfer.

12
Bilingual Infomap/Latent Semantic Analysis (LSA)

• Originally used for crosslingual information
  retrieval.
• Use bilingual, parallel core corpus.
• Parallel documents/paragraphs/ are put together
  and count as one text.
• Build vector space.
• Monolingual and cross-lingual similarities will
  fall out.

13
Identify and transfer Frame Elements

• Use Berkeley FrameNet corpus as training corpus
  (English) Frame Elements (content wordsPOS)
  from annotated examples are used as starting
  point.
• Use semantic space (generated by LSA) to find
  good (hopefully semantically related) translation
  candidates for words making up Frame Element.
• To identify French Frame Element Find closest
  vector.
• Several good examples, some less good ones.

14
Add Clustering

• Inspection of data shows Frame Elements may have
  semantically different fillers.
• Thus, clustering of LSA vectors seems promising.
• Identifying French Frame Elements Instead of
  finding closest vector, check whether word vector
  belong to one of the clusters.
• Problems Identify optimal number of clusters,
  sparse data,

15
Resources Needed

• Frame Element identification/transfer
• English FrameNet ?
• Parallel corpus source/target language ?
• Additional corpora in both languages ?
• Corpus in target language ?
• (Tagger in source/target language ?)
• (Not so little) manual annotation ?
• (Read ? OK, ? may be problem for small
  languages, ? may be problem for small projects)

16
Use information from WordNet?

• For French
• Use (Euro) WordNet alternatively/in addition
• Use Euro WordNet links (translations)
• Use WordNet to expand queries
• Use similarity measures such as JiangConrath 97.
• For other languages that do not have WordNet ???

17
Syntax

• Certain Frame Elements are semantically totally
  heterogeneous, but syntactically (relatively)
  easy to identify
• For example Statement.Message (engl. say that
  X, fr. dire que X)
• Problem Semantic transfer can be learned using
  LSA, syntactic transfer (thatque) cannot.
• Could (partially) parsed parallel corpora be used
  to learn syntactic transfer? Can syntactic and
  semantic Frame Element identification be
  combined? Alternatively Can syntactic Frame
  Elements be recognised and left to annotators
  altogether?

18
Frame Element Preferences

• Knowing more about Frame Elements (explicitly)
  would be very helpful.
• Automatic Frame/Frame Element assignment.
• Manual annotation/guidelines.
• Transfer to other languages.
• Encoding preferences as links within FrameNet
• Encoding preferences as links with external
  resources (WordNet? SUMO/MILO?), cf. work by
  Aljoscha Burchardt
• Cf. yesterdays talk by Michael Ellsworth

19
Conclusions

• (Some) more research required.
• Optimising the annotation process probably very
  important, e.g.
• Use several cycles (start with more certain
  cases, re-train with the additional data, )
• Integrate different strategies, e.g. syntax and
  semantics.
• Which decisions can be made automatically? Can
  suggestions be made? How good are they? Recall
  vs. precision optimisations