Recent advances in deep learning have improved the performance of many Natural
Language Processing (NLP) tasks such as translation, question-answering, and text …

The disambiguation of a syntactically ambiguous sentence in favor of a less preferred parse
can lead to slower reading at the disambiguation point. This phenomenon, referred to as a …

Frame Semantics includes context as a central aspect of the theory. Frames themselves can
be regarded as a representation of the immediate context against which meaning is to be …

Cross‐task generalization is a significant outcome that defines mastery in natural language
understanding. Humans show a remarkable aptitude for this, and can solve many different …

In computational neurolinguistics, it has been demonstrated that hierarchical models such
as recurrent neural network grammars (RNNGs), which jointly generate word sequences …

Background Data accuracy is essential for scientific research and policy development. The
National Violent Death Reporting System (NVDRS) data is widely used for discovering the …

A cognitive agent performing in the real world needs to learn relevant concepts about its
environment (eg, objects, color, and shapes) and react accordingly. In addition to learning …

Event extraction plays a crucial role in natural language processing (NLP), facilitating the
transformation of unstructured text into structured representations. This conversion …

Natural language understanding (NLU) has made massive progress driven by large
benchmarks, but benchmarks often leave a long tail of infrequent phenomena …

Abstract Deep Neural Network (DNN) models are challenging to interpret because of their
highly complex and non-linear nature. This lack of interpretability (1) inhibits adoption within …