Language choice models for microplanning and readability

This paper describes the construction of language choice models for the microplanning of discourse relations in a Natural Language Generation system that attempts to generate appropriate texts for users with varying levels of literacy. The models consist of constraint satisfaction problem graphs that have been derived from the results of a corpus analysis. The corpus that the models are based on was written for good readers. We adapted the models for poor readers by allowing certain constraints to be tightened, based on psycholinguistic evidence. We describe how the design of microplanner is evolving. We discuss the compromises involved in generating more readable textual output and implications of our design for NLG architectures. Finally we describe plans for future work

Believe It or Not: Adding Belief Annotations to Databases

We propose a database model that allows users to annotate data with belief statements. Our motivation comes from scientific database applications where a community of users is working together to assemble, revise, and curate a shared data repository. As the community accumulates knowledge and the database content evolves over time, it may contain conflicting information and members can disagree on the information it should store. For example, Alice may believe that a tuple should be in the database, whereas Bob disagrees. He may also insert the reason why he thinks Alice believes the tuple should be in the database, and explain what he thinks the correct tuple should be instead. We propose a formal model for Belief Databases that interprets users' annotations as belief statements. These annotations can refer both to the base data and to other annotations. We give a formal semantics based on a fragment of multi-agent epistemic logic and define a query language over belief databases. We then prove a key technical result, stating that every belief database can be encoded as a canonical Kripke structure. We use this structure to describe a relational representation of belief databases, and give an algorithm for translating queries over the belief database into standard relational queries. Finally, we report early experimental results with our prototype implementation on synthetic data.Comment: 17 pages, 10 figure

A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. With the merit of integrating contextual information and cross-documental knowledge, the DRNews model creates news vectors that describe both the semantic information and potential linkages among news events through an attributed news network. Two stock market prediction tasks, namely the short-term stock movement prediction and stock crises early warning, are implemented in the framework of the attention-based Long Short Term-Memory (LSTM) network. It is suggested that DRNews substantially enhances the results of both tasks comparing with five baselines of news embedding models. Further, the attention mechanism suggests that short-term stock trend and stock market crises both receive influences from daily news with the former demonstrates more critical responses on the information related to the stock market {\em per se}, whilst the latter draws more concerns on the banking sector and economic policies.Comment: 25 page