Ben Zhou

We define five classes of temporal commonsense, and use crowdsourcing to develop a new dataset, MCTACO, that serves as a test set for this task. Expand

We introduce ZOE, a zero-shot entity typing approach that requires no annotated data and can flexibly identify newly defined types. Expand

We present CogCompTime, a system that has these two important functionalities. Expand

We propose a new annotation paradigm for NLP that helps to close systematic gaps in the test data. Expand

We present our library COGCOMPNLP which simplifies the process of design and development of NLP applications by providing modules to address different challenges: we provide a corpus-reader module that supports popular corpora in the NLP community, a module for various low-level data-structures and operations (such as search over text). Expand

We propose a new annotation paradigm for NLP that helps to close systematic gaps in the test data. Expand

This work proposes a novel sequence modeling approach that exploits explicit and implicit mentions of temporal common sense, extracted from a large corpus, to build TACOLM, a temporal commonsense language model. Expand

We propose a new model, JEANS , which jointly represents multilingual KGs and text corpora in a shared embedding scheme, and seeks to improve entity alignment with incidental supervision signals from text. Expand