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Novel contexts may often arise in complex querying scenarios such as inevidence-based medicine (EBM) involving biomedical literature, that may notexplicitly 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 currentcontext, may not exist within a single document, but within the collection, viaalternate lexical forms. Therefore, inspired by the recent success ofsequence-to-sequence neural models in delivering the state-of-the-art in a widerange of NLP tasks, we develop a novel sequence-to-set framework with neuralattention for learning document representations that can effect term transferwithin the corpus, for semantically tagging a large collection of documents. Wedemonstrate that our proposed method can be effective in both a supervisedmulti-label classification setup for text categorization, as well as in aunique unsupervised setting with no human-annotated document labels that usesno external knowledge resources and only corpus-derived term statistics todrive the training. Further, we show that semi-supervised training using ourarchitecture on large amounts of unlabeled data can augment performance on thetext categorization task when limited labeled data is available. Our approachto generate document encodings employing our sequence-to-set models forinference of semantic tags, gives to the best of our knowledge, thestate-of-the-art for both, the unsupervised query expansion task for the TRECCDS 2016 challenge dataset when evaluated on an Okapi BM25--based documentretrieval system; and also over the MLTM baseline (Soleimani et al, 2016), forboth supervised and semi-supervised multi-label prediction tasks on thedel.icio.us and Ohsumed datasets. We will make our code and data publiclyavailable.

Sequence-to-Set Semantic Tagging: End-to-End Multi-label Prediction  using Neural Attention for Complex Query Reformulation and Automated Text  Categorization