In this paper, we present a survey on the application of recurrent neural networks to the task
of statistical language modeling. Although it has been shown that these models obtain good …

Two problems arise when using distant supervision for relation extraction. First, in this
method, an already existing knowledge base is heuristically aligned to texts, and the …

We develop a learning principle and an efficient algorithm for batch learning from logged
bandit feedback. This learning setting is ubiquitous in online systems (eg, ad placement …

Word representations have proven useful for many NLP tasks, eg, Brown clusters as
features in dependency parsing (Koo et al., 2008). In this paper, we investigate the use of …

Recently, many researchers have concentrated on using neural networks to learn features
for Distant Supervised Relation Extraction (DSRE). These approaches generally use a …

Recently, sentiment analysis has become a focus domain in artificial intelligence owing to
the massive text reviews of modern networks. The fast increase of the domain has led to the …

Distributed representations of words have been shown to capture lexical semantics, based
on their effectiveness in word similarity and analogical relation tasks. But, these tasks only …

We present a language agnostic, unsupervised method for inducing morphological
transformations between words. The method relies on certain regularities manifest in …

Word embeddings have been found useful for many NLP tasks, including part-of-speech
tagging, named entity recognition, and parsing. Adding multilingual context when learning …

Short texts have become the prevalent format of information on the Internet. Inferring the
topics of this type of messages becomes a critical and challenging task for many …