Post coming soon. Inspired by Dr. Amy J Ko and her work: https://medium.com/bits-and-behavior/studying-programming-language-learning-a-3-year-recap-bda469e5be04
I was helping a friend of mine figure out how Wikipedia works, and I made this self-learning tutorial. It compares stuff you can do with the source editing and the visual editing mode, and when you'd want to prefer the former over the latter (usually when you want to reuse one of the popular templates,... Continue Reading →
Liling Tan of Saarland University compiled a list of open source NLP tools for anyone to get started with. Thanks Liling! We don't know each other, but your list is awesome! Here's the compiled list of NLP tools. Here are the NLP tools slides that Liling presented at FOSS Asia in 2017.
Update: I got an ICWSM Reviewer Award this year -- Thanks ICWSM!I'm using this post as a running list of checkpoints for reviewers of computational social science and computer science conferences. But everything can be generalized to whichever field you belong to. Here's a short and sweet list of points to write your next peer... Continue Reading →
It has long been known that human affect is context-driven, and that labeled datasets should account for these factors in generating predictive models of affect. This motivates our Shared Task, which is organized in collaboration with researchers at Megagon Labs and is built upon the HappyDB dataset, comprising human accounts of `happy moments'. I'll add... Continue Reading →
Announcing the SciSumm corpus. The purpose behind the release of this corpus is to highlight the challenges and relevance of the scientific summarization problem, support research in automatic scientific document summarization and provide evaluation resources to push the current state of the art.
The WKWSCI Sentiment Lexicon by Christopher S. G. Khoo, Sathik Basha Johnkhan and Jin-Cheon Na is based on the 6of12dict lexicon, and currently covers adjectives, adverbs and verbs. The words were manually coded with a value on a 7-point sentiment strength scale. The effectiveness of the four sentiment lexicons for sentiment categorization at the document-level and sentence-level was evaluated using an Amazon product review dataset.... Continue Reading →