We present Contextual Discourse Vectors (CDV), a distributed document representation for efficient answer retrieval from long healthcare documents. Our approach is based on structured query tuples of entities and aspects from free text and medical taxonomies. Our model leverages a dual encoder architecture with hierarchical LSTM layers and multi-task training to encode the position of clinical entities and aspects alongside the document discourse. We use our continuous representations to resolve queries with short latency using approximate nearest neighbor search on sentence level. We apply the CDV model for retrieving coherent answer passages from nine English public health resources from the Web, addressing both patients and medical professionals. Because there is no end-to-end training data available for all application scenarios, we train our model with self-supervised data from Wikipedia. We show that our generalized model significantly outperforms several state-of-the-art baselines for healthcare passage ranking and is able to adapt to heterogeneous domains without additional fine-tuning.

When searching for information, a human reader first glances over a document, spots relevant sections, and then focuses on a few sentences for resolving her intention. However, the high variance of document structure complicates the identification of the salient topic of a given section at a glance. To tackle this challenge, we present SECTOR, a model to support machine reading systems by segmenting documents into coherent sections and assigning topic labels to each section. Our deep neural network architecture learns a latent topic embedding over the course of a document. This can be leveraged to classify local topics from plain text and segment a document at topic shifts. In addition, we contribute WikiSection, a publicly available data set with 242k labeled sections in English and German from two distinct domains: diseases and cities. From our extensive evaluation of 20 architectures, we report a highest score of 71.6% F1 for the segmentation and classification of 30 topics from the English city domain, scored by our SECTOR long short-term memory model with Bloom filter embeddings and bidirectional segmentation. This is a significant improvement of 29.5 points F1 over state-of-the-art CNN classifiers with baseline segmentation.

With the convergence of data warehousing, online analytical processing and the Semantic Web, analytical tasks are no longer only designed and executed by experts. Instead, various users expect to query keyword search engines with analytical intentions. One efficient approach to answer these tasks is to leverage the factual information stored in large-scale text databases. These systems enable analysts to access unstructured text sources from the Web with structured query languages. The challenge of mapping keyword queries to structured queries has been approached in various forms. However, these systems are not able to detect the underlying intent of a task. Thus, they cannot infer the user's expectations towards specificity and form of the results. Moreover, a large fraction of queries for retrieving analytical results is rare. As a result, services for intent-aware task recognition perform poorly or are not even triggered on these long-tail queries. We report from a study over 102,360 query and click patterns from a factual search engine. Our analysis reveals six common analytical tasks: explore, relate, resolve, list, compare and answer. To distinguish among these, we study the effects of syntactical structures in the query, methods for interactive entity detection and query segmentation techniques. We evaluate these features on language models and Naive Bayes classifiers. From our evaluation we report a combined F1 score of 90% for the prediction of task intent from keyword queries.