6600453
The nature of indexing: how humans and machines analyze messages and texts for retrieval - Part I: Research, and the nature of human indexing
Abstract Does human intellectual indexing have a continuing role to play in the face of increasingly sophisticated automatic indexing techniques? In this two-part essay, a computer scientist and long-time TREC participant (Perez-Carballo) and a practitioner and teacher of human cataloging and indexing (Anderson) pursue this question by reviewing the opinions and research of leading experts on both sides of this divide. We conclude that human analysis should be used on a much more selective basis, and we offer suggestions on how these two types of indexing might be allocated to best advantage. Part one of the essay critiques the comparative research, then explores the nature of human analysis of messages or texts and efforts to formulate rules to make human practice more rigorous and predictable. We find that research comparing human vs automatic approaches has done little to change strongly held beliefs, in large part because many associated variables have not been isolated or controlled. Part II focuses on current methods in automatic indexing, its gradual adoption by major indexing and abstracting services, and ways for allocating human and machine approaches. Overall, we conclude that both approaches to indexing have been found to be effective by researchers and searchers, each with particular advantages and disadvantages. However automatic indexing has the over-arching advantage of decreasing cost, as human indexing becomes ever more expensive.
14337981
The nature of indexing: how humans and machines analyze messages and texts for retrieval - Part II: Machine indexing, and the allocation of human versus machine effort
Abstract Does human intellectual indexing have a continuing role to play in the face of increasingly sophisticated automatic indexing techniques? In this two-part essay, a computer scientist and long-time TREC participant (Perez-Carballo) and a practitioner and teacher of human cataloging and indexing (Anderson) pursue this question by reviewing the opinions and research of leading experts on both sides of this divide. We conclude that human analysis should be used on a much more selective basis, and we offer suggestions on how these two types indexing might be allocated to best advantage. Part I of the essay critiques the comparative research, then explores the nature of human analysis of messages or texts and efforts to formulate rules to make human practice more rigorous and predictable. We find that research comparing human versus automatic approaches has done little to change strongly held beliefs, in large part because many associated variables have not been isolated or controlled. Part II focuses on current methods in automatic indexing, its gradual adoption by major indexing and abstracting services, and ways for allocating human and machine approaches. Overall, we conclude that both approaches to indexing have been found to be effective by researchers and searchers, each with particular advantages and disadvantages. However, automatic indexing has the over-arching advantage of decreasing cost, as human indexing becomes ever more expensive.
12617679
Human Symbol Manipulation Within an Integrated Cognitive Architecture
This article describes the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture (Anderson et al., 2004; Anderson & Lebiere, 1998) and its detailed application to the learning of algebraic symbol manipulation. The theory is applied to modeling the data from a study by Qin, Anderson, Silk, Stenger, & Carter (2004) in which children learn to solve linear equations and perfect their skills over a 6-day period. Functional MRI data show that: (a) a motor region tracks the output of equation solutions, (b) a prefrontal region tracks the retrieval of declarative information, (c) a parietal region tracks the transformation of mental representations of the equation, (d) an anterior cingulate region tracks the setting of goal information to control the information flow, and (e) a caudate region tracks the firing of productions in the ACT-R model. The article concludes with an architectural comparison of the competence children display in this task and the competence that monkeys have shown in tasks that require manipulations of sequences of elements.
6937633
A Theory of Language Acquisition Based on General Learning Principles
A simulation model is described for the acquisition of the control of syntax in language generation. This model makes use of general learning principles and general principles of cognition. Language generation is modelled as a problem solving process involving principly the decomposition of a lobe-communicated semantic structure into a hierarchy of subunits for generation. The syntax of the language controls this decomposition. It is shown how a sentence and semantic structure can be compared to infer the decomposition that led to the sentence. The learning processes involve generalizing rules to classes of words, learning by discrimination the various contextual constraints on a rule application, and a strength process which monitors a rule's history of success and failure. This system is shown to apply to the learning of noun declensions in Latin, relative clause constructions in French, and verb auxiliary structures in English.
206565835
Strategy Choice and Change in Programming
Abstract This research studied looping or iterative choice and change, especially between the "while-do" and "repeat-until" looping constructs in the PASCAL programming language. The empirical results from the first experiment, in which subjects were free to choose between the two looping alternatives, indicated that most PASCAL programmers are quite sensitive to the nature of the problems being solved and adaptable in choosing appropriate looping strategies. Another two experiments were performed in which subjects were either forced or induced to use one of the two looping strategies. These two experiments indicated that subjects are quite tenacious in using the appropriate strategy and their performance deteriorates when they are forced to use a different strategy.
2301308
Recovering from Distributable Thread Failures with Assured Timeliness in Real-Time Distributed Systems
We consider the problem of recovering from failures of distributable threads with assured timeliness. When a node hosting a portion of a distributable thread fails, it causes orphans - i.e., thread segments that are disconnected from the thread's root. We consider a termination model for recovering from such failures, where the orphans must be detected and aborted, and failure-exception notification must be delivered to the farthest, contiguous surviving thread segment for resuming thread execution. We present a realtime scheduling algorithm called AUA, and a distributable thread integrity protocol called TP-TR. We show that AUA and TP-TR bound the orphan cleanup and recovery time, thereby bounding thread starvation durations, and maximize the total thread accrued timeliness utility. We implement AUA and TP-TR in a real-time middleware that supports distributable threads. Our experimental studies with the implementation validate the algorithm/protocol's time-bounded recovery property and confirm their effectiveness
14213624
Achieving Efficient and Cognitively Plausible Learning in Backgammon
Traditionally, computer applications to game domains have taken a brute-force approach, relying on sheer computational power to overcome the complexity of the domain. Although many of these programs have been quite successful, it is interesting to note that humans can still perform extremely well against them. Thus we are compelled to ask, if no human could match the computational power of most of these programs, are there methods for learning and performance in game domains that more closely reflect human cognition? In response to this question, this paper attempts to model how humans learn and play games by developing a Backgammon-playing algorithm based on cognition. Analysis of this algorithm shows that it is efficient and commensurate with human abilities suggesting that it provides a cognitively plausible theory of learning in Backgammon.