Collecting Common Sense from text and People

In order to display human-like intelligence, advanced computational systems should have access to the vast network of generic facts about the world that humans possess and that is known as commonsense knowledge (books have pages, grocery has a price, ...). Developers of AI applications have long been aware of this, and, for decades, they have invested in the laborious and expensive manual creation of commonsense knowledge repositories. An automated, high-throughput and low-noise method for commonsense collection still remains as the holy grail of AI. Two relatively recent developments in computer science and computational linguistics that may provide an answer to the commonsense collection problem are text mining from large amounts of data, something that has become possible with the massive availability of text on the Web, and human computation, which is a workaround technique implemented by outsourcing the 'hard' sub-steps of a problem to people. Text mining has been very successful in extracting huge amounts of commonsense knowledge from data, but the extracted knowledge tends to be extremely noisy. Human computation is also a challenging problem because people can provide unreliable data and may lack motivation to solve problems on behalf of researchers and engineers. A clever, and recently popularized, technique to motivate people to contribute to such projects it to pose the problems as entertaining games and let people solve those problems while they play a game. This technique, commonly known as games-with-a-purpose approach, has proved a very powerful way of recruiting laypeople on the Web. The focus of this thesis is to study methods to collect common sense from people via human computation and from text via text mining, and explore the opportunities in bringing these two types of methods together. The first contribution of my study is the introduction of a novel text miner trained on a set of known commonsense facts. The text miner is called BagPack and it is based on a vector-space representation of concept pairs, that also captures the relation between the pairs. BagPack harvests a large number of facts from Web-based corpora and these facts constitute a -- possibly noisy -- set of candidate facts. The second contribution of the thesis is Concept Game, a game with a purpose which is a simple slot-machine game that presents the candidate facts -- that are mined by BagPack -- to the players. Players are asked to recognize the meaningful facts and discard the meaningless facts in order to score points. Thus, as a result, laypeople verify the candidate set and we obtain a refined, high-quality dataset of commonsense facts. The evaluation of both systems suggests that text mining and human computation can work very efficiently in tandem. BagPack acts as an almost-endless source of candidate facts which are likely to be true, and Concept Game taps laypeople to verify these candidates. Using Web-based text as a source of commonsense knowledge has several advantages with respect to a purely human-computation system which relies on people as the source of information. Most importantly, we can tap domains that people do not talk about when they are directly asked. Also, relying on people just as a source of verification makes it possible to design fast-paced games with a low cognitive burden. The third issue that I addressed in this thesis is the subjective and stereotypical knowledge which constitutes an important part of our commonsense repository. Regardless of whether one would like to keep such knowledge in an AI system, being able to identify the subjectivity and detect the stereotypical knowledge is an important problem. As a case study, I focused on stereotypical gender expectations about actions. For this purpose, I created a gold standard of actions (e.g., pay bill, become nurse) rated by human judges on whether they are masculine or feminine actions. After that, I extracted, combined, and evaluated two different types of data to predict the gold standard. The first type of data depends on the metadata provided by social media (in particular, the genders of users in a microblogging site like Twitter) and the second one depends on Web-corpus-based pronoun/name gender heuristics. The metadata about the Twitter users helps us to identify which actions are mentioned more frequently by which gender. The Web-corpus-based score helps us to identify which gender is more frequently reported to be carrying out a given action. The evaluation of both methods suggests that 1) it is possible to predict the human gold standard with considerable success, 2) the two methods capture different aspects of stereotypical knowledge, and 3) they work best when combined together.