Developing TODAM: Three models for serial-order information

Developing an Efficiency Based Target Setting Model for K-Stage Serial tructures

DEA as an effective approach to evaluate the relative efficiency of DMUs, recently applied in many production and operation systems. This paper proposed a new k-stage DEA based MOLP model for target setting in the serial network structures, considering undesirable outputs. Since the proposed model takes the size of DMUs into account, the new idea ensures the possibility of the results. The resu...

First-Order Probabilistic Models for Information Extraction

Information extraction (IE) is the problem of constructing a knowledge base from a corpus of text documents. In this paper, we argue that firstorder probabilistic models (FOPMs) are a promising framework for IE, for two main reasons. First, FOPMs allow us to reason explicitly about entites that are mentioned in multiple documents, and compute the probability that two strings refer to the same e...

Part-set cuing of order information: implications for associative theories of serial order memory.

In three experiments, the effect of cuing, at the point of test, on memory for order and/or position was investigated. Experiment 1 used a partial reconstruction of order task to demonstrate a mnemonic benefit of part-set cuing at the time of test; this result is used to argue that people may commonly use interitem associative information, rather than just position information, to help them rem...

On Interfering with Item versus Order Information in Serial Recall

Serial Order in Reading Aloud: Connectionist Models and Neighborhood Structure

Dual-Route and Connectionist Single-Route models of reading have been at odds over claims as to the correct explanation of the reading process. Recent Dual-Route models predict that subjects should show an increased naming latency for irregular words when the irregularity is earlier in the word (e.g. chef is slower than glow)-a prediction that has been connrmed in human experiments. Since this ...