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Full-Text Articles in Physical Sciences and Mathematics
From Question Context To Answer Credibility: Modeling Semantic Structures For Question Answering Using Statistical Methods, Protima Banerjee, Hyoil Han
From Question Context To Answer Credibility: Modeling Semantic Structures For Question Answering Using Statistical Methods, Protima Banerjee, Hyoil Han
Hyoil Han
Within a Question Answering (QA) framework, Question Context plays a vital role. We define Question Context to be background knowledge that can be used to represent the user’s information need more completely than the terms in the query alone. This paper proposes a novel approach that uses statistical language modeling techniques to develop a semantic Question Context which we then incorporate into the Information Retrieval (IR) stage of QA. Our approach proposes an Aspect-Based Relevance Language Model as basis of the Question Context Model. This model proposes that the sparse vocabulary of a query can be supplemented with semantic information …
A Computationally Efficient System For High-Performance Multi-Document Summarization, Sean Sovine, Hyoil Han
A Computationally Efficient System For High-Performance Multi-Document Summarization, Sean Sovine, Hyoil Han
Hyoil Han
We propose and develop a simple and efficient algorithm for generating extractive multi-document summaries and show that this algorithm exhibits state-of-the-art or near state-of-the-art performance on two Document Understanding Conference datasets and two Text Analysis Conference datasets. Our results show that algorithms using simple features and computationally efficient methods are competitive with much more complex methods for multi-document summarization (MDS). Given these findings, we believe that our summarization algorithm can be used as a baseline in future MDS evaluations. Further, evidence shows that our system is near the upper limit of performance for extractive MDS.
Language Modeling Approaches To Information Retrieval, Protima Banerjee, Hyoil Han
Language Modeling Approaches To Information Retrieval, Protima Banerjee, Hyoil Han
Hyoil Han
This article surveys recent research in the area of language modeling (sometimes called statistical language modeling) approaches to information retrieval. Language modeling is a formal probabilistic retrieval framework with roots in speech recognition and natural language processing. The underlying assumption of language modeling is that human language generation is a random process; the goal is to model that process via a generative statistical model. In this article, we discuss current research in the application of language modeling to information retrieval, the role of semantics in the language modeling framework, cluster-based language models, use of language modeling for XML retrieval and …