Resource-Bounded Information Extraction: Acquiring Missing Feature Values on Demand

Kanani, Pallika; McCallum, Andrew; Hu, Shaohan

doi:10.1007/978-3-642-13657-3_45

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…Elliasi-Rad [2] explored the problem of building an information extraction agent, but did not address the problem of acquiring specific missing pieces of information on demand. The problem of Resource-bounded Information Extraction (RBIE) was first introduced in our previous work [5], in which the main idea was to select a subset of the web documents to process by exploiting the network structure in the data. The example task in [5] is to find a missing year of publication in citation data.…”

Section: Resource-bounded Reasoningmentioning

confidence: 99%

“…The problem of Resource-bounded Information Extraction (RBIE) was first introduced in our previous work [5], in which the main idea was to select a subset of the web documents to process by exploiting the network structure in the data. The example task in [5] is to find a missing year of publication in citation data. All available queries are issued, and all the search results are downloaded, which are then filtered using a simple heuristic.…”

Section: Resource-bounded Reasoningmentioning

confidence: 99%

“…Resource-bounded Information Extraction (RBIE) is the process of searching for and extracting specific pieces of information from an external information source under a limited budget of resources, such as computation time, network bandwidth, and storage space [5]. The problem of missing information in databases is ubiquitous.…”

Section: Introductionmentioning

confidence: 99%

“…This process is more efficient than indiscriminate processing, and would use relatively smaller amounts of resources. The problem of Resource-bounded Information Extraction (RBIE) was first introduced in our previous work [5]. Queries are formed by combining existing, relevant information in the database with user defined keywords.…”

Section: Introductionmentioning

confidence: 99%

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Selecting actions for resource-bounded information extraction using reinforcement learning

Kanani

McCallum

2012

Proceedings of the Fifth ACM International Conference on Web Search and Data Mining

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Given a database with missing or uncertain content, our goal is to correct and fill the database by extracting specific information from a large corpus such as the Web, and to do so under resource limitations. We formulate the information gathering task as a series of choices among alternative, resource-consuming actions and use reinforcement learning to select the best action at each time step. We use temporal difference q-learning method to train the function that selects these actions, and compare it to an online, errordriven algorithm called SampleRank. We present a system that finds information such as email, job title and department affiliation for the faculty at our university, and show that the learning-based approach accomplishes this task efficiently under a limited action budget. Our evaluations show that we can obtain 92.4% of the final F1, by only using 14.3% of all possible actions.

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Section: Resource-bounded Reasoningmentioning

confidence: 99%

Section: Resource-bounded Reasoningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Selecting actions for resource-bounded information extraction using reinforcement learning

Kanani

McCallum

2012

Proceedings of the Fifth ACM International Conference on Web Search and Data Mining

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“…More recent work explicitly considers the combinatorial selection of features [1], and has investigated theoretical properties of such a selection [3]. Cost-sensitive feature acquisition has also been applied to information mining from the internet [9].…”

Section: Related Workmentioning

confidence: 99%

Goal-oriented sensor selection for intelligent phones

Koltunova

Hoey

Grzes

2011

Proceedings of the 2011 International Workshop on Situation Activity &Amp; Goal Awareness - SAGAware '11

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Context information on a smart phone can be used to tailor applications for specific situations (e.g. provide tailored routing advice based on location, gas prices and traffic). However, typical context-aware smart phone applications use very limited context information such as user identity, location and time. In the future, smart phones will need to decide from a wide range of sensors to gather information from in order to best accommodate user needs and preferences in a given context. In this paper, we present a model for sensor selection within decision-making processes, in which observational features are selected based on longer-term impact on the decisions made by the smart phone. This problem is more challenging than measuring information gain, because information gathering can be useful for decisions made at some point in the future. This paper formulates the problem as a partially observable Markov decision process (POMDP), and shows how the POMDP can integrate partially available sensor information with user goals and preferences, allowing a seamless fusion of uncertain sensor data with complex and long-term decision-making on the mobile device.

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