Acquisition of Semantic Classes for Adjectives from Distributional Evidence

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Acquisition of Semantic Classes for Adjectives from Distributional Evidence. Gemma Boleda Universitat Pompeu Fabra Barcelona. general picture. automatic classification of adjectives Catalan according to broad semantic characteristics clustering syntactic evidence. motivation. - PowerPoint PPT Presentation

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Acquisition of Semantic Classes for Adjectives from

Distributional Evidence

Gemma BoledaUniversitat Pompeu Fabra

Barcelona

general picture

• automatic classification of adjectives– Catalan

• according to broad semantic characteristics• clustering

– syntactic evidence

motivation

• Lexical Acquisition– infer properties of words– lexical bottleneck

• both symbolic and statistical approaches

• adjectives– determining NP reference

• the French general– establishing properties of entities

• this maimai is round and sweet

motivation

• initial motivation: POS-tagging– 55% remaining ambiguity involves adjectives

general francès: ‘French general’ or ‘general French’?• observations

– general tendencies in syntactic behaviour of adjectives– ... which correspond to broad semantic properties

• generalisation: best at semantic level– low-level tasks (POS-tagging)– initial schema for lexical semantic representation

approach

• no general, well established semantic classification– have to build and test ours!

• clustering: unsupervised technique– groups objects according to feature distribution– does not depend on pre-classification– provides insight into the nature of the data

• shallow approach to syntax: n-grams– limited syntactic distribution– local relationship to arguments=> test feasibility

rodó ‘round’ 0.4 0.4 0.2

dolç ‘sweet’ 0.5 0.4 0.1

francès ‘French’ 0.1 0.6 0.3

italià ‘Italian’ 0.05 0.5 0.45

outline

• adjective syntax and semantic classification• methodology• experiment 1• experiment 2• partial conclusions• outlook: rest of the thesis

Boleda, Badia, Batlle (2004)

outline

• adjective syntax and semantic classification• methodology• experiment 1• experiment 2• partial conclusions• outlook: rest of the thesis

adjective syntax

• default function: noun modifier (92%)– right of the noun (default position: 72%)– some to the left (‘epithets’: 28%)

• predicative uses unfrequent (7%), but significant

two-way classification

• number of arguments– unary: pilota vermella ‘red ball’– binary: professor gelós de la Maria ‘teacher jealous of

Maria’• ontological kind (Ontological Semantics)

– basic: vermell ‘red’– object: malaltia pulmonar ‘pulmonary disease’ (=>

lung)– event: propietat constitutiva ‘constitutive property’ (=>

constitutes)

Ontological Semantics

• coverage (ordinary cases)• machine tractability• explicit model of world: ontology

– vermell => attribute::colour::red(x)– pulmonar => related-to::lung(x)– constitutiu => event::benef::constitute(x)

• however: no commitment to particular framework

rationale

• observation: syntactic preferences correspond to semantic properties

• hypothesis: we can use syntactic features to infer semantic classes

outline

• adjective syntax and semantic classification• methodology• experiment 1• experiment 2• conclusions and future work

data and procedure

• 2283 adjectives>50 times in 16 million word Catalan corpus

• lemma and morphological info

• cluster the whole set– perform different tasks on different subsets

• tuning subset: choose features• Gold Standard: evaluation and analysis

features and feature selection• features:

– empirically chosen from blind distribution– double bigram, simplified POS-representation

ella diu que la pilota vermella és seva

she says that the ball red is hers

-3ey -2dd -1cn +1ve

• tuning subset: 100 adjectives– choose features (distribution)

Fig. A: Feature selection

analysis

• Gold Standard– 80 adjectives– annotated by 3 human judges, acceptable

agreement (92 and 84%, .72 and .74 kappa)

outline

• adjective syntax and semantic classification• methodology• experiment 1• experiment 2• partial conclusions• outlook: rest of the thesis

experiment 1: unary / binary

• final evaluation:10 features, raw percentage– clustering algorithm: k-means (cosine)

• predictions:– binary adjectives cooccur with prepositions

more frequently than unary ones– unary adjectives are more flexible

unary / binary: results

• agreement with Gold Standard: – 97%, kappa = 0.87 – comparable to humans

• features:

cl high low0 (un) -1cn +1prep

1 (bin) +1prep (-1cn)Fig. B: Clusters vs. unary/binary

unary (yellow)

binary (red)

outline

• adjective syntax and semantic classification• methodology• experiment 1• experiment 2• partial conclusions• outlook: rest of the thesis

experiment 2: basic / object / event

• final evaluation: 32 features, normalisation– clustering algorithm: k-means (cosine)

• predictions:– basic adjectives are flexible, work as epithets,

occur in predicative contexts, appear further from the noun

– object adjectives appear rigidly after the noun– event adjectives tend to occur in predicative

positions and do not act as epithets

basic / object / event: results

• agreement with Gold Standard: – 73%, kappa = 0.56– lower than humans

• features:

cl high low

0 (obj) -1cn -1ve1 (ev) +1prep

2 (bas) -1co +1ajFig C: Clusters vs. basic/event/object

object (yellow)

event (orange)

basic (red)

basic/object/event: error analysis

• something has gone wrong!– characterisation of event adjectives

Fig C: Clusters vs. basic/event/object

Fig D: Clusters vs. unary/binary

binary!unary event adjectivesbasic adjectives with an object reading (polysemy)

binary event adjectives

outline

• adjective syntax and semantic classification• methodology• experiment 1• experiment 2• partial conclusions• outlook: rest of the thesis

partial conclusions

• overall, results seem to back up:– use of syntax-semantics interface for adjectives– linguistic predictions as to relevant features and

differences across classes– shallow approach

• unary / binary: piece of cake– few binary adjectives, but worth spotting

(denote relationships)

partial conclusions

• basic / object / event: need reworking– object adjectives seem to be the most robust

class– variation in basic adjectives (default class),

polysemy– event adjectives: seem to behave much like

basic adjectives with respect to features chosen => redefine class?

outline

• adjective syntax and semantic classification• methodology• experiment 1• experiment 2• partial conclusions• outlook: rest of the thesis

outlook: rest of the thesis

• rethink classification• redefine features in light of results• integrate polysemy judgments into the

experiment and analysis• perform experiments with other corpora

classification

• what to do with event adjectives? cp.:– constitutiu ‘constitutive’ (“active”)– legible ‘readable’ (“passive”)– reproductor ‘reproducing’ (“active,

habituality”)• yet another parameter: gradability

– important for adjectives– should be easy to induce

better blind distribution or self-defined features?

empirical accurate sparseness objective

blind X X ?

self X?(depends on method)

X

• n-grams: sparseness, selection

• other features?– account for different levels of description

polysemy

• crucial aspect, explains much of results• difficult to integrate!

– meaningless kappa values• alternatives?

– clearer definition of polysemy within task– specific tests– other resources: dictionary?

other resources

• CUCWeb (208 million word)http://www.catedratelefonica.upf.es

• test whether “more data is better data” (Mercer and Church 1993: 18-19)– advantages and challenges of Web corpora

• current results: for verb subcategorisation experiment, results 12 points lower than using smaller, balanced, controled corpus

Acquisition of Semantic Classes for Adjectives from

Distributional Evidence

Gemma BoledaUniversitat Pompeu Fabra

Barcelona

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