The Current State of FrameNet

CLFNGJune 26, 2006

Fillmore

The Starting Challenge

• A standard dictionary often does not give us– the background to the meanings of words

• for specialist vocabulary nobody doubts the need of such background

• frame semantics proposes that ALL (content) words, including the ones that everybody knows, are best understood in terms of their background frames

– a description of a word’s combinatory affordances (valences)• this requires a description of the elements of each frame – the things that can be relevantly talked about within phrases structurally related to the word being described – and a classification of relevant syntactic forms and functions

– examples illustrating its common uses• and these should be determined from examples from a large corpus

The Starting Solution

• Describe a situation type (frame) that holds for one or more words in the language.– [Revenge]

• Find the frame elements for this frame. – [Avenger, Offender, Injury,Injured_Party, Punishment]

• Choose words that belong to the frame. – [avenge, retaliate]

• Create valence descriptions as sets of triples {frame elements, grammatical function, phrase type} for each word.– one possibility for avenge:

• {{Av,Subj,NP}, {Inj,Obj,NP}, {Pun, Dep, PPing[by]}}

Do this by:• Having trained humans annotate “good” example sentences by assigning FE labels to the syntactic dependents of words being described.

• Having automatic processes assign syntactic form and function properties to the labeled phrases.

• Having automatic processes derive the valences from the annotations.

• Noticing whether the number of syntactic phrases found with the head word matches the number of frame elements known to belong to the frame, and figure out what to make of discrepancies when you find them.

The Product

• Frame Descriptions• Lexical Entries specifying

– frame membership– valences– access to examples

• Annotated Sentences• Map of Frame-to-Frame Relations

Immediately Emerging Issues

• For our lexicographic purposes we wanted to select only simple clear examples. – First, that’s not possible.– Second, we need to be able to annotate full texts.

• There are cases where the major frame in a construction was evoked by a syntactic dependent, not a syntactic head– support verbs or prepositions with dependent frame-bearing nouns

• say a prayer, swear an oath, make a promise– transparent nouns “selected by” their complements (unitizers,

etc.)• stick of gum, kernel of rice,

• There are cases of mismatches between the number of syntactic constituents we find in a sentence and the number of frame elements that we might expect to see represented. For example:– null representation of some FEs (for which we have stories)– multiple or discontinuous representations of some FEs

More Recently Emerging Issues

• Not all FE are of equal status. We proposed distinguishing – Core, – Peripheral and – Extrathematic FEs,

if only because we wanted to say SOMETHING about all phrases that were in syntactically appropriate positions relative to each target LU.– We bombed their village yesterday in retaliation.

• The set of frames is not a flat list. There are frame-to-frame relations, of various kinds, importantly– inheritance– composition– perspective

Issues that don’t go away

• Public Relations– Some of our lexicography and NLP friends object that

since we work one frame at a time,rather than one word at a time, it’ll take forever before we can provide the research community with any serious kind of systematic polysemy analysis – hence we’re useless for WSD.

• Speed– We’ll always be slow, but a current grant providing

collaboration with Adam Killgarriff and the use of his WordSketch tool for FrameNet “vanguarding” shows promise. (Vanguarding is what you do before you turn material over to the annotators: selecting correct senses of polysemous words, sorting and selecting samples that can together show the variety of syntactic patterns found with a given word, choosing the most relevant collocations, etc.. Collin will talk about this.)

Continuing Ambitions

1. A speeding up of the whole process (the WordSketch connection)

2. Collaborative alignment with WordNet and SUMO

3. Full sample FN analysis of some portions of the ANC

4. Some open-source way of enhancing the growth of the FrameNet lexicon

5. Success in funding 1-4.

Current FN Visitors

• Carlos Subirats

• Jan Scheffsczyk

• Thomas Schmidt

• Kyoko Hirose

• Fran Valverde