A COMPUTATIONAL APPROACH TO POLITENESS

WITH APPLICATION TO SOCIAL FACTORS

( M i z i l , J u r a f s k y, L e s k o v e c , P o t t s )

Natural Language Processing

By:Sakaar Khurana

Department of Computer Science and Engineering,Indian Institute of Technology, Kanpur

Abstract

• Computational framework for identifying linguistic aspects of politeness.

• Starting point: A corpus of requests annotated for politeness – evaluate various aspects of politeness theory

• Develop a computational framework for identifying and characterizing politeness marking in REQUESTS (because they involve speaker imposing on addressee – negative politeness – minimizing imposition)

Politeness Data

• Requests in online communities• Wikipedia community of editors• Stack-exchange community.

Annotating Data

• Data labelled using AMTs.• Context – Requests with 2 sentences.• Each annotator – 13 requests.• Each request – 5 annotators• Rate between very impolite to very

polite(slider was presented)• Z-score normalization on each annotator

Data Distribution

• Requests have average of 0 (interesting)• Standard deviation – 0.7• Binary perception – 1st and 4th quartile

have maximum binary consensus among annotators

Politeness Markers

• Requests exhibiting politeness markers are extracted using regular expression matching on dependency parse by Stanford dependency parser with specialized lexicons

Predicting Politeness

• Wikipedia – Training set• Stack exchange – Test set• BOW model – SVM with unigram feature

representation• Linguistically informed classifier (Ling.) –

SVM using features in previous table in addition to unigram features.

Results

• Ling. Model performed 3-4 % better.• Results are within 3% from human

performance

• Hence the theory inspired features are effective and generalize well to new domains.

Relation to social factors

• Relation to social outcome:

• Politeness and Power:

Other Work

• Other researches have identified politeness marking across • different text and media types(Herring)• Between social groups(Burke and

Kraut)• This paper had more data which allowed

a fuller survey of different strategies.