Generating anagrams from multiple core strings employing user-defined vocabularies and orthographic parameters

Abstract

Anagrams are used widely in psychological research. However, generating a range of strings with the same letter content is an inherently difficult and time-consuming task for humans, and current computer-based anagram generators do not provide the controls necessary for psychological research. In this article, we present a computational algorithm that overcomes these problems. Specifically, the algorithm processes automatically each word in a user-defined source vocabulary and outputs, for each word, all possible anagrams that exist as words (or as nonwords, if required) as defined by the same source vocabulary. Moreover, we show how the output of the algorithm can be filtered to produce anagrams within specific user-defined orthographic parameters. For example, the anagrams produced can be filtered to produce words that share, with each other or with other words in the source vocabulary, letters in only certain positions. Finally, we provide free access to the complete Windows-based program and source code containing these facilities for anagram generation.

Assessing the importance of letter pairs in initial, exterior, and interior positions in reading.

“…To overcome this problem, we conducted comparisons between initial, exterior, and interior letter pair conditions in a pairwise fashion by creating three sets of matched pairs of anagrams, one set for each comparison (initial vs. exterior, initial vs. interior, and exterior vs. interior). The enormity of the task of generating the required anagram sets (that each set must comprise two anagrams containing not merely the same letters but letters in the positions appropriate for the letter pairs compared, matched for letter content) required custom software (Jordan & Monteiro, 2003), allied to the MRC psycholinguistic database (Coltheart, 1981), to ensure that all possible combinations were available for use in the experiment.…”

Reevaluating split-fovea processing in word recognition: Hemispheric dominance, retinal location, and the word-nonword effect

“…Following the requirements of the Reicher-Wheeler task, words were selected to form matched pairs in which the members of each pair differed by just one letter (e.g., read, road ) and these differences occurred equally often at each of the four letter positions across all stimuli. Matched nonword stimuli were constructed for each pair, using custom software (Jordan & Monteiro, 2003) to rearrange the three noncritical letters in each word to form unpronounceable nonwords that had minimal orthographic structure but shared the same critical letter in the same serial position (e.g., the words read and road formed the nonwords aedr and aodr, respectively). An additional 16 pairs of four-letter words and nonwords For foveal stimuli, the ANOVA showed only a main effect of stimulus type [F(1,15) 12.15, p .01, 2 .45].…”

Generating Natural Anagrams: Towards Language Generation Under Hard Combinatorial Constraints

“…Although anagram generation is NP-hard, the basic algorithm that can output a set of words as anagrams is relatively simple (Jordan and Monteiro, 2003). As a result many anagram generation software and web services exist including the Internet Anagram Server 2 and the Anagram Artist 3 .…”

“…The automatic generation of anagrams is a challenging task since it is NP-hard 1 . Existing automatic anagram generation methods mainly focus on search efficiency in finding word combinations that can form anagrams (Jordan and Monteiro, 2003). However, they place little consideration on the word orders that seem natural to humans.…”