With the ever increasing size of the web, relevant information extraction on the Internet with
a query formed by a few keywords has become a big challenge. Query Expansion (QE) …

This paper presents an overview of Apertium, a free and open-source rule-based machine
translation platform. Translation in Apertium happens through a pipeline of modular tools …

The problem of event extraction requires detecting the event trigger and extracting its
corresponding arguments. Existing work in event argument extraction typically relies heavily …

State-of-the-art named entity recognition systems rely heavily on hand-crafted features and
domain-specific knowledge in order to learn effectively from the small, supervised training …

Brain activity in numerous perisylvian brain regions is modulated by the expectedness of
linguistic stimuli. We leverage recent advances in computational parsing models to test what …

We propose a deep learning method for eventdriven stock market prediction. First, events
are extracted from news text, and represented as dense vectors, trained using a novel …

Memory is fleeting. New material rapidly obliterates previous material. How, then, can the
brain deal successfully with the continual deluge of linguistic input? We argue that, to deal …

Neural networks have shown promising results for relation extraction. State-of-the-art
models cast the task as an end-to-end problem, solved incrementally using a local classifier …

Most current dependency parsers presuppose that input words have been morphologically
disambiguated using a part-of-speech tagger before parsing begins. We present a …

This paper investigates the problem of cross-lingual dependency parsing, aiming at
inducing dependency parsers for low-resource languages while using only training data …