About

A half-day tutorial, held at CIKM 2017 on November 10th.

Presenters

Niket Tandon Aparna Varde Gerard de Melo

Audience:

  • Beginners are introduced to commonsense knowledge mining.
  • Developers can learn applications of commonsense.
  • Researchers can understand the state-of-the-art and open problems.



Part 1: Acquiring commonsense knowledge [slides]

Introduction to commonsense

We introduce and compare commonsense against encyclopedic knowledge. We enumerate the types of commonsense and existing knowledge bases for each type of commonsense.

Representation learning

We discuss unstructured, structured, and continuous representations of commonsense knowledge.

Commonsense fact acquisition

We discuss the wide spectrum of models, including curated, distantly supervised and unsupervised models for mining commonsense from text and multimodal resources.

Evaluation of commonsense knowledge

In addition to individual facts, we discuss models to mine entailments for commonsense reasoning.

Part 2: Detecting and correcting odd collocations in text [slides]

Collocations and odd collocations

We introduce collocated expressions and odd collocations that are collocation errors where expressions are found that are not typically used in correct communication, e.g., mighty tea instead of strong tea.

Broad overview of techniques to fix collocation errors

We discuss interesting tasks where collocation error correction is useful and common techniques to fix these .

Linguistic classification

We discuss current research in the area of classifying collocation errors which leads to their correction, mainly from a linguistic classification perspective.

Detection and correction

We discuss frequency-based, semantic similarity, ranking and ensemble learning based techniques.

Part 3: Applications and open issues [slides]

Smart Cities

We describe the primary directions in smart cities research and highlight how commonsense has been useful currently, and envision potential application of commonsense in smart cities.