Argument mining systems for student essays need to be able to reliably identify argument components independently of particular essay topics. Thus in addition to features that model argumentation through topic-independent linguistic indicators such as discourse markers, features that can abstract over lexical signals of particular essay topics might also be helpful to improve performance. Prior argument mining studies have focused on persuasive essays and proposed a variety of largely lexicalized features. Our current study examines the utility of such features, proposes new features to abstract over the domain topics of essays, and conducts evaluations using both 10-fold cross validation as well as cross-topic validation. Experimental results show that our proposed features significantly improve argument mining performance in both types of cross-fold evaluation settings. Feature ablation studies further shed light on relative feature utility.
The next time you pull out your smartphone and ask Siri or Google for advice, or chat with a bot online, take pride in knowing that some of the theoretical foundation for that technology was brought to life here in Canada. Indeed, as far back as the early 1980s, key organizations such as the Canadian Institute for Advanced Research embarked on groundbreaking work in neural networks and machine learning. Academic pioneers such as Geoffrey Hinton (now a professor emeritus at the University of Toronto and an advisor to Google, among others), the University of Montreal's Yoshua Bengio and the University of Alberta's Rich Sutton produced critical research that helped fuel Canada's rise to prominence as a global leader in artificial intelligence (AI). Stephen Piron, co-CEO of Dessa, praises the federal government's efforts at cutting immigration processing timelines for highly skilled foreign workers. Canada now houses three major AI clusters – in Toronto, Montreal and Edmonton – that form the backbone of the country's machine-learning ecosystem and support homegrown AI startups.
That's because, to paraphrase Amazon's Jeff Bezos, artificial intelligence (AI) is "not just in the first inning of a long baseball game, but at the stage where the very first batter comes up." Look around, and you will find AI everywhere--in self driving cars, Siri on your phone, online customer support, movie recommendations on Netflix, fraud detection for your credit cards, etc. To be sure, there's more to come. Featuring 30 lectures, MIT's course "introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence." It includes interactive demonstrations designed to "help students gain intuition about how artificial intelligence methods work under a variety of circumstances."
LinkedIn wants to become more useful to workers by adding personalized news feeds, helpful messaging "bots" and recommendations for online training courses, as the professional networking service strives to be more than just a tool for job-hunting. The new services will arrive just as LinkedIn itself gains a new boss -- Microsoft -- which is paying 26 billion to acquire the Silicon Valley company later this year. LinkedIn said the new features, which it showed off to reporters Thursday, were in the works before the Microsoft takeover was announced in June. But LinkedIn CEO Jeff Weiner said his company hopes to incorporate some of Microsoft's technology as it builds more things like conversational "chat bots," or software that can carry on limited conversations, answer questions and perform tasks like making reservations. Chat bots are a hot new feature in the consumer tech world, where companies like Facebook, Apple and Google are already racing to offer useful services based on artificial intelligence.