End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification
Abstract: We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoderdecoder that generates paths in the type hierarchy and can be trained end-to-end. We show that our model performs better on fine-grained entity classification than…
AI & Machine Learning Research with Meaningful Impact
I am curious about the foundations of computational intelligence, specifically methodologies that bridge the areas of connectionist and symbolic learning applied to real-world AI and NLP applications. I am passioned about enabling an open, interdisciplinary research & innovation culture with…
LinkedHealthAnswers: Towards Linked Data-driven Question Answering for the Health Care Domain
Abstract: This paper presents Linked Health Answers, a natural language question answering systems that utilizes health data drawn from the Linked Data Cloud. The contributions of this paper are three-fold: Firstly, we review existing state-of-the-art NLP platforms and components, with a special…
USI Answers: Natural Language Question Answering Over (Semi-) Structured Industry Data
Abstract: The paper reports on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data…
Connecting Question Answering and Conversational Agents: Contextualizing German Questions for Interactive Question Answering Systems
Abstract: Research results in the field of Question Answering (QA) have shown that the classification of natural language questions significantly contributes to the accuracy of the generated answers. In this paper we present an approach which extends the prevalent question…
