  Novel contexts may often arise in complex querying scenarios such as in
evidence-based medicine (EBM) involving biomedical literature, that may not
explicitly refer to entities or canonical concept forms occurring in any fact-
or rule-based knowledge source such as an ontology like the UMLS. Moreover,
hidden associations between candidate concepts meaningful in the current
context, may not exist within a single document, but within the collection, via
alternate lexical forms. Therefore, inspired by the recent success of
sequence-to-sequence neural models in delivering the state-of-the-art in a wide
range of NLP tasks, we develop a novel sequence-to-set framework with neural
attention for learning document representations that can effect term transfer
within the corpus, for semantically tagging a large collection of documents. We
demonstrate that our proposed method can be effective in both a supervised
multi-label classification setup for text categorization, as well as in a
unique unsupervised setting with no human-annotated document labels that uses
no external knowledge resources and only corpus-derived term statistics to
drive the training. Further, we show that semi-supervised training using our
architecture on large amounts of unlabeled data can augment performance on the
text categorization task when limited labeled data is available. Our approach
to generate document encodings employing our sequence-to-set models for
inference of semantic tags, gives to the best of our knowledge, the
state-of-the-art for both, the unsupervised query expansion task for the TREC
CDS 2016 challenge dataset when evaluated on an Okapi BM25--based document
retrieval system; and also over the MLTM baseline (Soleimani et al, 2016), for
both supervised and semi-supervised multi-label prediction tasks on the
del.icio.us and Ohsumed datasets. We will make our code and data publicly
available.
