With the advent of Web 2.0, there exist many online platforms that result in massive textual-
data production. With ever-increasing textual data at hand, it is of immense importance to …

Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

Text preprocessing is not only an essential step to prepare the corpus for modeling but also
a key area that directly affects the natural language processing (NLP) application results. For …

Abstract Open Information Extraction (IE) systems extract relational tuples from text, without
requiring a pre-specified vocabulary, by identifying relation phrases and associated …

Modern models of relation extraction for tasks like ACE are based on supervised learning of
relations from small hand-labeled corpora. We investigate an alternative paradigm that does …

We consider the problem of performing learning and inference in a large scale knowledge
base containing imperfect knowledge with incomplete coverage. We show that a soft …

We present YAGO, a light-weight and extensible ontology with high coverage and quality.
YAGO builds on entities and relations and currently contains more than 1 million entities and …

In response to the continuing research interest in computational semantic analysis, we have
proposed a new task for SemEval-2010: multi-way classification of mutually exclusive …

In the last few years, a number of NLP researchers have developed and participated in the
task of Recognizing Textual Entailment (RTE). This task encapsulates Natural Language …

Research into corpus-based semantics has focused on the development of ad hoc models
that treat single tasks, or sets of closely related tasks, as unrelated challenges to be tackled …