About
Quantification is a supervised learning task in which we must predict, for each class of interest, the percentage of data items that belong to the class. It also goes under the name of “supervised prevalence estimation”, and has a number of applications in market research, epidemiology, the social sciences, and political science, among others. Quantification differs from classification, since in classification we are interested in predicting the class of each unlabelled item, while in quantification we are only interested in predicting the fractions of unlabelled items that belong to each class. While quantification may be solved by classifying each unlabelled item and counting how many items have been attributed the class, this method has been shown to be suboptimal. Research in quantification has to do with devising new supervised algorithms for quantification, in devising appropriate measures and protocols for evaluating quantification accuracy, each of these for different types of quantification (binary, single-label multi-class, multi-label multi-class, ordinal).
This page describes the work done at QCRI on Quantification.
Related publications
- Giovanni Da San Martino, Wei Gao and Fabrizio Sebastiani. Ordinal Text Quantification.
The 38th Annual International ACM SIGIR Conference on Research and
Development in Information Retrieval (SIGIR 2016), July 2016, Pisa,
Italy. [ source
code and data ]
- Giovanni Da San Martino, Wei Gao and Fabrizio Sebastiani. QCRI at SemEval-2016 Task 4:
Probabilistic Methods for Binary and Ordinal Quantification. The
10th International Workshop on Semantic Evaluation (SemEval 2016), June
2016, San Diego, California, USA. [ source code and data ]
- Wei Gao and Fabrizio Sebastiani. From Classification to Quantification in
Tweet Sentiment Analysis. Social Network Analysis and Mining
(SNAM), Volume:6, Issue:1, Article 19, 2016, Springer. (DOI) [ source
code | data
]
- Wei Gao and Fabrizio Sebastiani. Tweet Sentiment: From Classification
to Quantification. The 2015 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining (ASONAM 2015), August
2015, Paris, France. (Best Paper
Runner-Up) [ data ]
Further Material
- Slides of a course on "Text Quantification", that Fabrizio Sebastiani gave at the 2015 Russian Summer School on Information Retrieval (RussIR 2015), St. Petersburg, Russia.