Common Acronym Words

As computerized word lists become readily available to the loglogists, many of the previously difficult problems that have filled the pages of Word Ways become trivial. It is therefore interesting to begin researching problems that cannot be solved with word lists. A dictionary provides four pieces of information about a word: spelling, pronunciation, etymology and meaning. This suggests three types of problems for systematic logological research, in ascending order of difficulty: pronunciation (homophones, refractory rhymes, syllables, etc.), etymology (this article) and meaning (homographs, autantonyms, contronyms, etc.). Although these topics have appeared in Word Ways, they have not received the kind of systematic treatment accorded spelling

Acronym-Meaning Extraction from Corpora Using Multi-Tape Weighted Finite-State Machines

The automatic extraction of acronyms and their meaning from corpora is an important sub-task of text mining. It can be seen as a special case of string alignment, where a text chunk is aligned with an acronym. Alternative alignments have different cost, and ideally the least costly one should give the correct meaning of the acronym. We show how this approach can be implemented by means of a 3-tape weighted finite-state machine (3-WFSM) which reads a text chunk on tape 1 and an acronym on tape 2, and generates all alternative alignments on tape 3. The 3-WFSM can be automatically generated from a simple regular expression. No additional algorithms are required at any stage. Our 3-WFSM has a size of 27 states and 64 transitions, and finds the best analysis of an acronym in a few milliseconds.Comment: 6 pages, LaTe

A Supervised Learning Approach to Acronym Identification

This paper addresses the task of finding acronym-definition pairs in text. Most of the previous work on the topic is about systems that involve manually generated rules or regular expressions. In this paper, we present a supervised learning approach to the acronym identification task. Our approach reduces the search space of the supervised learning system by putting some weak constraints on the kinds of acronym-definition pairs that can be identified. We obtain results comparable to hand-crafted systems that use stronger constraints. We describe our method for reducing the search space, the features used by our supervised learning system, and our experiments with various learning schemes

Don't gamble with Melanoma

A poster highlighting an acronym for the early recognition of melanoma on the foo

From computer assisted language learning (CALL) to mobile assisted language use

This article begins by critiquing the long-established acronym CALL (Computer Assisted Language Learning). We then go on to report on a small-scale study which examines how student non-native speakers of English use a range of digital devices beyond the classroom in both their first (L1) and second (L2) languages. We look also at the extent to which they believe that their L2-based activity helps consciously and/or unconsciously with their language learning, practice, and acquisition. We argue that these data, combined with other recent trends in the field, suggest a need to move from CALL towards a more accurate acronym: mobile assisted language use (MALU). We conclude with a definition of MALU together with a brief discussion of a potential alignment of MALU with the notion of the digital resident and a newly emerging educational theory of connectivism

PRAISE: Christians Educators and the Difficult Student

This article defends the role of the Christian educator in reaching the difficult student. It further offers tips for handling the challenging student from a Christian perspective, coupled with tried-and-true research using the acronym PRAISE: being proactive, using reinforcements, assessing and analyzing the intent of misbehavior, being sincere, and empowering students and the Holy Spirit in them

Classification of bi-qutrit positive partial transpose entangled edge states by their ranks

We construct $3\otimes 3$ PPT entangled edge states with maximal ranks, to complete the classification of $3\otimes 3$ PPT entangled edge states by their types. The ranks of the states and their partial transposes are 8 and 6, respectively. These examples also disprove claims in the literature.Comment: correct the title to avoid an acronym, correct few text

Bayesian ACRONYM Tuning

We provide an algorithm that uses Bayesian randomized benchmarking in concert with a local optimizer, such as SPSA, to find a set of controls that optimizes that average gate fidelity. We call this method Bayesian ACRONYM tuning as a reference to the analogous ACRONYM tuning algorithm. Bayesian ACRONYM distinguishes itself in its ability to retain prior information from experiments that use nearby control parameters; whereas traditional ACRONYM tuning does not use such information and can require many more measurements as a result. We prove that such information reuse is possible under the relatively weak assumption that the true model parameters are Lipschitz-continuous functions of the control parameters. We also perform numerical experiments that demonstrate that over-rotation errors in single qubit gates can be automatically tuned from 88% to 99.95% average gate fidelity using less than 1kB of data and fewer than 20 steps of the optimizer