  Scientific writing is difficult. It is even harder for those for whom English
is a second language (ESL learners). Scholars around the world spend a
significant amount of time and resources proofreading their work before
submitting it for review or publication.
  In this paper we present a novel machine learning based application for
proper word choice task. Proper word choice is a generalization the lexical
substitution (LS) and grammatical error correction (GEC) tasks. We demonstrate
and evaluate the usefulness of applying bidirectional Long Short Term Memory
(LSTM) tagger, for this task. While state-of-the-art grammatical error
correction uses error-specific classifiers and machine translation methods, we
demonstrate an unsupervised method that is based solely on a high quality text
corpus and does not require manually annotated data. We use a bidirectional
Recurrent Neural Network (RNN) with LSTM for learning the proper word choice
based on a word's sentential context. We demonstrate and evaluate our
application on both a domain-specific (scientific), writing task and a
general-purpose writing task. We show that our domain-specific and
general-purpose models outperform state-of-the-art general context learning. As
an additional contribution of this research, we also share our code,
pre-trained models, and a new ESL learner test set with the research community.
