Tag: Dou

O6-N Knowledge Graph based Question Answering for Automatic Customer Service

Short text Matching plays an important role in natural language processing tasks such as information retrieval (IR), question answering (QA), and dialogue system. Traditional text matching methods rely on human-crafted rules and template. Though they are effective to deal with specific situations, but lack in the ability to handle unobserved cases. In natural language, it’s infeasible to exhaust every possible expression that conveys the same ideas. Recently, neural network-based models have good generalizability to unobserved cases but provide less interpretability. In this project, we aim to balance the two sides by introducing the concept of entity to QA. We propose an interpretable, deep learning-based short text matching model for customer service domain question answering, which consists of two major components: Word segmentation enhanced Named Entity Recognition (NER), and text semantic matching. By doing so, the proposed method explicitly models entities and types of entity as building blocks of interpretable QA.

O2-M Ontology-based Deep Learning with Explanation for Human Behavior Prediction

In the second year, we will focus on domain ontology-specific deep learning methods, integrating contemporary deep learning model designs with the existing SMASH healthcare ontology and corresponding data, to structure both model input and potentially output in order to develop meaningful semantic explanations that compare favorably with the state of the art.

O3-B Ontology-based Interpretable Deep Learning for Consumer Complaint Explanation and Analysis

In this project, we will design ontology-based interpretable deep models for consumer complaint explanation and analysis. The main idea of our algorithms is to consider domain knowledge in the design of deep learning models and utilize domain ontologies for explaining the deep learning models and results through casual modeling.