Incremental learning for interactive e-mail filtering

In this article, we propose a framework, namely, Prediction-Learning-Distillation (PLD) for interactive document classification and distilling misclassified documents. Whenever a user points out misclassified documents, the PLD learns from the mistakes and identifies the same mistakes from all other classified documents. The PLD then enforces this learning for future classifications. If the classifier fails to accept relevant documents or reject irrelevant documents on certain categories, then PLD will assign those documents as new positive/negative training instances. The classifier can then strengthen its weakness by learning from these new training instances. Our experiments’ results have demonstrated that the proposed algorithm can learn from user-identified misclassified documents, and then distil the rest successfully.

A misspelt youth: Spelling and orthographic learning in university students as a function of spelling-sound consistency

Pseudowords with inconsistent vs. consistent spellings (e.g., nurch, with rhyme neighbours search, lurch & perch, vs. mish, with neighbours dish, wish) were presented with definitions for naming either twice or 6 times. In an oral spelling test, there were main and interactive effects of consistency and the number of training trials on accuracy and main effects only on response latency, with the improvement in accuracy from 2 to 6 training trials greater for the more poorly learned inconsistent items. Of most interest, the smaller effect of training on accuracy in the consistent condition was reliable; contrary to the most obvious prediction of dual route spelling models that the sublexical procedure should produce correct spellings for consistent items early in training. In a second task students wrote spellings of multisyllabic words containing unstressed indeterminate (schwa) vowels. In their errors on the schwa vowel, students showed sensitivity to the most common spelling overall but also they were influenced by differences in schwa spellings in English words as a function of the number of syllables and schwa position. These results indicate that dual route models of spelling will need to accommodate the consistency of spellings within categories defined by lexical structure variables.

Interactive email filtering: Learning from misclassified examples

Learning from mistakes has proven to be an effective way of learning in the interactive document classifications. In this paper we propose an approach to effectively learning from mistakes in the email filtering process. Our system has employed both SVM and Winnow machine learning algorithms to learn from misclassified email documents and refine the email filtering process accordingly. Our experiments have shown that the training of an email filter becomes much effective and faster

SQLator: An online SQL learning workbench

SQL (Structured Query Language) is one of the essential topics in foundation databases courses in higher education. Due to its apparent simple syntax, learning to use the full power of SQL can be a very difficult activity. In this paper, we introduce SQLator, which is a web-based interactive tool for learning SQL. SQLator's key function is the evaluate function, which allows a user to evaluate the correctness of his/her query formulation. The evaluate engine is based on complex heuristic algorithms. The tool also provides instructors the facility to create and populate database schemas with an associated pool of SQL queries. Currently it hosts two databases with a query pool of 300+ across the two databases. The pool is divided into 3 categories according to query complexity. The SQLator user can perform unlimited executions and evaluations on query formulations and/or view the solutions. The SQLator evaluate function has a high rate of success in evaluating the user's statement as correct (or incorrect) corresponding to the question. We will present in this paper, the basic architecture and functions of SQLator. We will further discuss the value of SQLator as an educational technology and report on educational outcomes based on studies conducted at the School of Information Technology and Electrical Engineering, The University of Queensland.

Hemispheric contributions to lexical ambiguity resolution: Evidence from individuals with complex language impairment following left-hemisphere lesions

Nine individuals with complex language deficits following left-hemisphere cortical lesions and a matched control group (n 5 9) performed speeded lexical decisions on the third word of auditory word triplets containing a lexical ambiguity. The critical conditions were concordant (e.g., coin–bank–money), discordant (e.g., river–bank–money), neutral (e.g., day–bank– money), and unrelated (e.g., river–day–money). Triplets were presented with an interstimulus interval (ISI) of 100 and 1250 ms. Overall, the left-hemisphere-damaged subjects appeared able to exhaustively access meanings for lexical ambiguities rapidly, but were unable to reduce the level of activation for contextually inappropriate meanings at both short and long ISIs, unlike control subjects. These findings are consistent with a disruption of the proposed role of the left hemisphere in selecting and suppressing meanings via contextual integration and a sparing of the right-hemisphere mechanisms responsible for maintaining alternative meanings.

Parameter setting and statistical learning

Three main models of parameter setting have been proposed: the Variational model proposed by Yang (2002; 2004), the Structured Acquisition model endorsed by Baker (2001; 2005), and the Very Early Parameter Setting (VEPS) model advanced by Wexler (1998). The VEPS model contends that parameters are set early. The Variational model supposes that children employ statistical learning mechanisms to decide among competing parameter values, so this model anticipates delays in parameter setting when critical input is sparse, and gradual setting of parameters. On the Structured Acquisition model, delays occur because parameters form a hierarchy, with higher-level parameters set before lower-level parameters. Assuming that children freely choose the initial value, children sometimes will miss-set parameters. However when that happens, the input is expected to trigger a precipitous rise in one parameter value and a corresponding decline in the other value. We will point to the kind of child language data that is needed in order to adjudicate among these competing models.

Mummy! Ball! Fish!: Why English-learning children produce nouns earlier than verbs

When English-learning children begin using words the majority of their early utterances (around 80%) are nouns. Compared to nouns, there is a paucity of verbs or non-verb relational words, such as 'up' meaning 'pick me up'. The primary explanations to account for these differences in use either argue in support of a 'cognitive account', which claims that verbs entail more cognitive complexity than nouns, or they provide evidence challenging this account. In this paper I propose an additional explanation for children's noun/verb asymmetry. Presenting a 'multi-modal account' of word-learning based on children's gesture and word combinations, I show that at the one-word stage English-learning children use gestures to express verb-like elements which leaves their words free to express noun-like elements.