Enhanced answer selection in CQA using multi-dimensional features combination

Best-Answer Selection Using a Machine Learning Tool at NTCIR8 CQA Pilot Task

Using Syntactic Features in Answer Reranking

This paper describes a baseline question answering system for Swedish. The system includes modules to carry out the question analysis, hypothesis generation, and reranking of answers. It was trained and evaluated on questions from a data set inspired by the Swedish television quiz show Kvitt eller Dubbelt. We used the Swedish Wikipedia as knowledge source and we show that paragraph retrieval fr...

An Enhanced Convolutional Neural Network Model for Answer Selection

Answer selection is an important task in question answering (QA) from the Web. To address the intrinsic difficulty in encoding sentences with semantic meanings, we introduce a general framework, i.e., Lexical Semantic Feature based Skip Convolution Neural Network (LSF-SCNN), with several optimization strategies. The intuitive idea is that the granular representations with more semantic features...

Multi-dimensional model order selection

Multi-dimensional model order selection (MOS) techniques achieve an improved accuracy, reliability, and robustness, since they consider all dimensions jointly during the estimation of parameters. Additionally, from fundamental identifiability results of multi-dimensional decompositions, it is known that the number of main components can be larger when compared to matrix-based decompositions. In...

Modeling negotiation using multi-focused answer sets

Answer Set Programming (ASP) is a form of declarative programming based on the stable model semantics [5]. The idea of ASP is to represent a computational problem by a set of (propositional) rules P such that particular minimal models of P , the stable models, correspond to the solutions of the original problem. ASP has proven successful as an elegant and convenient vehicle for commonsense reas...