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Ranking Community Answers by Modeling QuestionAnswer Relationships via Analogical Reasoning
"... The method of finding highquality answers has a significant impact on users ’ satisfaction in a community question answering system. However, due to the lexical gap between questions and answers as well as spam typically contained in usergenerated content, filtering and ranking answers is very cha ..."
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The method of finding highquality answers has a significant impact on users ’ satisfaction in a community question answering system. However, due to the lexical gap between questions and answers as well as spam typically contained in usergenerated content, filtering and ranking answers is very challenging. Existing solutions mainly focus on generating redundant features, or finding textual clues using machine learning techniques; none of them ever consider questions and their answers as relational data but instead model them as independent information. Meanwhile, they only consider the answers of the current question, and ignore any previous knowledge that would be helpful to bridge the lexical and semantic gap. We assume that answers are connected to their questions with various types of links, i.e. positive links indicating highquality answers, negative links indicating incorrect answers or usergenerated spam, and propose an analogical reasoningbased approach which measures the analogy between the new questionanswer linkages and those of some previous relevant knowledge which contains only positive links; the candidate answer which has the most analogous link to the supporting set is assumed to be the best answer. We conducted our experiments based on 29.8 million Yahoo!Answer questionanswer threads and showed the effectiveness of our proposed approach.
Small sets of interacting proteins suggest latent linkage mechanisms through analogical reasoning
, 2007
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Testing a Bayesian Measure of Representativeness Using a Large Image Database
"... How do people determine which elements of a set are most representative of that set? We extend an existing Bayesian measure of representativeness, which indicates the representativeness of a sample from a distribution, to define a measure of the representativeness of an item to a set. We show that t ..."
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How do people determine which elements of a set are most representative of that set? We extend an existing Bayesian measure of representativeness, which indicates the representativeness of a sample from a distribution, to define a measure of the representativeness of an item to a set. We show that this measure is formally related to a machine learning method known as Bayesian Sets. Building on this connection, we derive an analytic expression for the representativeness of objects described by a sparse vector of binary features. We then apply this measure to a large database of images, using it to determine which images are the most representative members of different sets. Comparing the resulting predictions to human judgments of representativeness provides a test of this measure with naturalistic stimuli, and illustrates how databases that are more commonly used in computer vision and machine learning can be used to evaluate psychological theories. 1
RANKING RELATIONS USING ANALOGIES IN BIOLOGICAL AND INFORMATION NETWORKS
, 2009
"... Analogical reasoning depends fundamentally on the ability to learn and generalize about relations between objects. We develop an approach to relational learning which, given a set of pairs of objects S ={A (1) : B (1),A (2): B (2),...,A (N) : B (N)}, measures how well other pairs A: B fit in with th ..."
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Analogical reasoning depends fundamentally on the ability to learn and generalize about relations between objects. We develop an approach to relational learning which, given a set of pairs of objects S ={A (1) : B (1),A (2): B (2),...,A (N) : B (N)}, measures how well other pairs A: B fit in with the set S. Our work addresses the following question: is the relation between objects A and B analogous to those relations found in S? Such questions are particularly relevant in information retrieval, where an investigator might want to search for analogous pairs of objects that match the query set of interest. There are many ways in which objects can be related, making the task of measuring analogies very challenging. Our approach combines a similarity measure on function spaces with Bayesian analysis to produce a ranking. It requires data containing features of the objects of interest and a link matrix specifying which relationships exist; no further attributes of such relationships are necessary. We illustrate the potential of our method on text analysis and information networks. An application on discovering functional interactions
Generative Inferences Based on a Discriminative Bayesian Model of Relation Learning
"... Bayesian Analogy with Relational Transformations (BART) is a discriminative model that can learn comparative relations from nonrelational inputs (Lu, Chen & Holyoak, 2012). Here we show that BART can be extended to solve inference problems that require generation (rather than classification) of ..."
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Bayesian Analogy with Relational Transformations (BART) is a discriminative model that can learn comparative relations from nonrelational inputs (Lu, Chen & Holyoak, 2012). Here we show that BART can be extended to solve inference problems that require generation (rather than classification) of relation instances. BART can use its generative capacity to perform hypothetical reasoning, enabling it to make quasideductive transitive inferences (e.g., “If A is larger than B, and B is larger than C, is A larger than C?”). The extended model can also generate humanlike instantiations of a learned relation (e.g., answering the question, “What is an animal that is smaller than a dog?”). These modeling results suggest that discriminative models, which take a primarily bottomup approach to relation learning, are potentially capable of using their learned representations to make generative inferences.
Departments of Psychology and Statistics
"... A deep problem in cognitive science is to explain the acquisition of abstract semantic relations, such as antonymy and synonymy. Are such relations necessarily part of an innate representational endowment provided to humans? Or, is it possible for a learning system to acquire abstract relations from ..."
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A deep problem in cognitive science is to explain the acquisition of abstract semantic relations, such as antonymy and synonymy. Are such relations necessarily part of an innate representational endowment provided to humans? Or, is it possible for a learning system to acquire abstract relations from nonrelational inputs of realistic complexity (avoiding handcoding)? We present a series of computational experiments using Bayesian methods in an effort to learn and generalize abstract semantic relations, using as inputs pairs of specific concepts represented by feature vectors created by