1. Natural Language Processing
A COMPUTATIONAL APPROACH TO POLITENESS with application to social factors
(Mizil, Jurafsky, Leskovec, Potts)
By:
Sakaar Khurana
Department of Computer Science and Engineering,
Indian Institute of Technology, Kanpur

2. 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)

3. Politeness Data Requests in online communities
Wikipedia community of editors
Stack-exchange community.

4. 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

5. Data Distribution Requests have average of 0 (interesting)
Standard deviation – 0.7
Binary perception – 1st and 4th quartile have maximum binary consensus among annotators

6. Politeness Markers
Requests exhibiting politeness markers are extracted using regular expression matching on dependency parse by Stanford dependency parser with specialized lexicons

8. 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.

9. 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.

10. Relation to social factors
Relation to social outcome:
Politeness and Power:

11. 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.