Unsupervised identification of synonymous query intent templates for attribute intents

ABSTRACT Among all web search queries there is an important subset of queries containing entity mentions. In these queries, it is observed that users are most interested in requesting some attribute of an entity, such as "Obama age" for the intent of age, which we refer to as the attribute intent. In this work we address the problem of identifying synonymous query intent templates for the attribute intent. For example, "how old is [Person]" and "[Person]'s age" are both synonymous templates for the age intent. Successful identification of the synonymous query intent templates not only can improve the performance of all existing query annotation approaches, but also could benefit applications such as instant answers and intent-based query suggestion. In this work we propose a clustering framework with multiple kernel functions to identify synonymous query intent templates for a set of canonical templates jointly. Furthermore, signals from multiple sources of information are integrated into a kernel function between templates, where the weights of these signals are tuned in an unsupervised manner. We have conducted extensive experiments across multiple domains in FreeBase, and results demonstrate the effectiveness of our clustering framework for finding synonymous query intent templates for attribute intents

Medicaid risk adjustment model with diagnosis and pharmacy-based adjusters: Does it work?

National health expenditures will continue to grow faster than nominal gross domestic product (GDP) in the early 21st century (Heffler et al., 2002; Heffler et al., 2005). Increased Medicaid costs have spurred research to find reliable cost-saving methodologies (Kronick et al., 1996). The Medicaid administrations of some states have chosen risk adjustment as a methodology for savings (Tollen et al., 1998; Weiner et al., 1998), since it can reduce the financial burden of health care providers and distribute medical resources more efficiently. This dissertation presents a risk-adjustment model based on two types of health condition adjusters: diagnosis-based HCC adjusters and pharmacy-based RxRisk adjusters. HCC adjusters were developed from different diagnostic categories from inpatient, outpatient and long-term care data. RxRisk adjusters included diseases inferable from prescription drug usage. The underlying assumption is that using both types of health condition adjusters, rather than relying on either diagnosis-based adjusters or pharmacy-based adjusters alone can help increase predictive power and lower Medicaid\u27s risk of reimbursing inflated medical costs for its beneficiaries. The population in this study consisted of all disabled and aged Florida adults who were eligible for Florida\u27s Medicaid program in state fiscal year (SFY) 2002-03 and state fiscal year 2003-04. The population was broken down into two subpopulations: disabled Medicaid beneficiaries aged 64 and under and beneficiaries aged 65 or over.The proposed regression model includes diagnostic and pharmacy-based adjusters, and this dissertation compares the proposed model with models based solely on pharmacy- or diagnosis-based adjusters.The results presented in this dissertation demonstrate the proposed model has higher predictive power than the diagnosis-based HCC model and the pharmacy-based RxRisk model for the overall population and the subpopulations in this study. Risk-adjustment models using diagnostic and prescription drug information have higher predictive power and decrease the possibility of inappropriate gaming of the Medicaid capitation payment system

An investigation into SOP based contract dispute adjudication process

Security of Payment Act is introduced in Singapore in the year 2004 till present. During this period of time, Singapore is experiencing slowdown in the growth of the construction industry. There have been various problems in the construction industry; the most common issue is the cash flow problem between the Sub-Contractor and the Main Contractor. Therefore, the Security of Payment Act aims to facilitate the progress payment by resolving the dispute cases through the adjudication procedures. The objective is to minimise the time delay to process the proposed payment claims between the two concerned parties. The implementing of the Security of Payment Act has shown positive feedbacks and results. Due to the fact that the SOP Act is introduced only recently, many Claimants and Respondents have minimum knowledge about it. There are various documentation formatting requirement and stringent timeline adhered closely to in the SOP Act. Without the proper guidelines and sufficient knowledge, it has resulted in high level of non-compliances of the SOP Act. This has led to the ineffective use of this legislation to facilitate the claiming of the progress payment by the Claimants and Respondents. The objective of the FYP is to formulate a checklist by identifying four problems of concern which are the improper formatting of payment claims, payment response, Notice of Intention for Adjudication Application and Adjudication Response. These four issues are the rising problems in the submission of important documents. Hence, four case studies will be analysed to identify and understand the common mistakes made by the Claimants and Respondents. These problems will be used as a reminder to help in the process of formulating a comprehensive and useful checklist. After all, the checklist aims to assist the Claimant and Respondent to understand the checklist in a simplified manner and thus reduce the occurrence of defective submission of the important documents.Bachelor of Engineering (Civil

A systematic study of multi-level query understanding

Search and information retrieval technologies have significantly transformed the way people seek information and acquire knowledge from the internet. To further improve the search accuracy and usability of the current-generation search engines, one of the most important research challenges is for a search engine to accurately understand a user’s intent or information need underlying the query. This thesis presents a systematic study of query understanding. In this thesis I have proposed a conceptual framework where there are different levels of query understanding. And these levels of query understanding have natural logical dependency. After that, I will present my studies on addressing important research questions in this framework. First, as a major type of query alteration, I addressed the query spelling correction problem by modeling all major types of spelling errors with a generalized Hidden Markov Model. Second, query segmentation is the most important type of query linguistic signals. I proposed a probabilistic model to identify the query segmentations using clickthrough data. Third, synonym finding is an important challenge for semantic annotation of queries. I proposed a compact clustering framework to mine entity attribute synonyms for a set of inputs jointly with multiple information sources. And finally, in the dynamic query understanding, I introduced the horizontal skipping bias which is unique to the query auto- completion process (QAC). I then proposed a novel two-dimensional click model for modeling the QAC process with emphasis on such behavior

Medicaid risk adjustment model with diagnosis and pharmacy-based adjusters: Does it work?

National health expenditures will continue to grow faster than nominal gross domestic product (GDP) in the early 21st century (Heffler et al., 2002; Heffler et al., 2005). Increased Medicaid costs have spurred research to find reliable cost-saving methodologies (Kronick et al., 1996). The Medicaid administrations of some states have chosen risk adjustment as a methodology for savings (Tollen et al., 1998; Weiner et al., 1998), since it can reduce the financial burden of health care providers and distribute medical resources more efficiently. This dissertation presents a risk-adjustment model based on two types of health condition adjusters: diagnosis-based HCC adjusters and pharmacy-based RxRisk adjusters. HCC adjusters were developed from different diagnostic categories from inpatient, outpatient and long-term care data. RxRisk adjusters included diseases inferable from prescription drug usage. The underlying assumption is that using both types of health condition adjusters, rather than relying on either diagnosis-based adjusters or pharmacy-based adjusters alone can help increase predictive power and lower Medicaid\u27s risk of reimbursing inflated medical costs for its beneficiaries. The population in this study consisted of all disabled and aged Florida adults who were eligible for Florida\u27s Medicaid program in state fiscal year (SFY) 2002-03 and state fiscal year 2003-04. The population was broken down into two subpopulations: disabled Medicaid beneficiaries aged 64 and under and beneficiaries aged 65 or over.The proposed regression model includes diagnostic and pharmacy-based adjusters, and this dissertation compares the proposed model with models based solely on pharmacy- or diagnosis-based adjusters.The results presented in this dissertation demonstrate the proposed model has higher predictive power than the diagnosis-based HCC model and the pharmacy-based RxRisk model for the overall population and the subpopulations in this study. Risk-adjustment models using diagnostic and prescription drug information have higher predictive power and decrease the possibility of inappropriate gaming of the Medicaid capitation payment system

Accurate Sequence Alignment using Distributed Filtering on GPU Clusters

Advent of next generation gene sequencing machines has led to computationally intensive alignment problems that can take many hours on a modern computer. Considering the fast increasing rate of introduction of new short sequences that are sequenced, the large number of existing sequences and inaccuracies in the sequencing machines, short sequence alignment has become a major challenge in High Performance Computing. In practice gaps as well as mismatches are found in genomic sequences, resulting in an edit distance problem. In this paper we describe the design of a distributed filter, based on shifted masks, to quickly reduce the number of potential matches in the presence of gaps and mismatches. Furthermore, we present a hybrid dynamic programming method, optimized for GPGPU targets, to process the filter outputs and find the accurate number of insertions, deletions and mismatches. Finally we present results from experiments performed on an NCSA cluster of 128 GPU units using the Hadoop framework.unpublishednot peer reviewe