Pairing Orthographically Variant Literary Words to Standard Equivalents Using Neural Edit Distance Models (2024)

Abstract: We present a novel corpus consisting of orthographically variant words found in works of 19th century U.S. literature annotated with their corresponding “standard” word pair. We train a set of neural edit distance models to pair these variants with their standard forms, and compare the performance of these models to the performance of a set of neural edit distance models trained on a corpus of orthographic errors made by L2 English learners. Finally, we analyze the relative performance of these models in the light of different negative training sample generation strategies, and offer concluding remarks on the unique challenge literary orthographic variation poses to string pairing methodologies.

## introduction using orthographic information to pair similar strings from a list of variants has a number of uses, from spelling correction to cognate detection. beyond character level similarity, what it means to be a "good" neighbor to a given source string might entail phonetic similarity (in the case of many misspellings), some sort of cognitive proximity (in certain "point" misspellings, say "k" for "c") or it may reflect a shared linguistic history (in the case of cognates). using orthographic information to achieve a string-pair ranking that incorporates these axes of meaning requires that the orthography of a string also captures this other meaningful dimension, to a greater or lesser degree. we introduce the domain of "literary orthographic variants," a set of orthographic modifications motivated by literary aesthetic concerns instead of purely linguistic or cognitive principle, and posit that the orthographic results of these unique motivations necessitates re-evaluating modeling approaches that have proven successful in more linguistically motivated domains (such as the aforementioned cognate detection and spelling correction tasks). we evaluate this claim using string pairing, a task where a model compares two strings and outputs a probability that they are a match. these strings can be considered a match if their pair probability exceeds a certain threshold. furthermore, these probabilities can also be used to rank a set of possible string pairs. we obtain these probabilities using a neural edit distance model architecture, an approach that has proven effective at pairing cognate words. specifically we train neural edit distances models on a corpus of nonliterary orthographic variants produced by l2 english learners and a novel corpus of literary variants in order to offer the following contributions: ## results and analysis results of the experiments can be found in on the other hand, models trained on gb perform uniformly the best when provided negatives generated by the mixed strategy, a combination of random and close ld pairs. this implies that in the case of gb, but not fce, the two sources of negative examples provide orthogonal information that are each of their own particular use during the training process. the specific character of the generated negative examples may explain this performance disparity. figure 2 shows the average ld from the target variant tokens for each the of negative generation strategies. random generation, the best performing strategy over fce, produces negative samples on average about 8 ld from the target variant token, no matter the number generated. logically, the mixed strategy, which performed best over gb, produced a set of samples with average an ld falling between the uniformly high ld of the randomly generated samples and the low ld of the samples generated by the ld process, which, for gb range from just below to just over 3 ld on average. in short, fce trained models benefit most from uniformly high ld negative examples, while gb trained models benefit most from a mixture of high and low ld negative examples. this may speak to the distinct nature of the positive examples found in these corpora. the fce corpus is comprised of samples produced by multiple authors. however, the range of possible orthovariant forms they employ is limited by their shared intent to adhere to a standard form of english orthography as best as they can. this overriding principle could lead fce's variant forms to conform more closely to a centralized set of possible edits, typified by common character substitutions or phonetic misspellings -it would be understandable for a writer making a good faith attempt at producing standard english orthography to replace a "c" with a "k", but never, say, an elision apostrophe ("'"). if this is the case, much of the information the model would need to distinguish between low ld brown candidate tokens is already contained in the fairly uniform set of possibilities demonstrated by the positive examples -the types of transformations embodied by these examples closely resembles the set of transformations resident in the fce corpus as a whole. in contrast, the gb corpus contains samples drawn from multiple authors who each employ their own looser set of orthographic constraints. these authors do not attempt in good faith to adhere to a particular standard orthography. rather, they use orthography as an expressive tool, and may not rely as heavily on further orthographic principles. consequently, the positive examples may lose some significant amount of explanatory value. examples of this effect drawn from the corpus can be found in each variant is a phonetic or pseudo-phonetic rendering of a given word in a form of particularly motivated variant english orthography, yet each set of character-level substitutions varies to a large degree. 4 indeed, even though all of these forms are relatively low ld from their standard token, the set of transformation principles encoded in one teaches us relatively little about the set found in any of the others. this could explain the the mixed strategy's superior performance on gb, as the positive examples under-determine the space of likely transformations among low ld candidates, leaving the generated low ld negative examples more room to provide useful information. the complexities inherent in literary variant orthography offers many axes on which to continue these studies. further experiments could be performed to validate the hypothesis concerning the mixed strategy's success on the gb corpus. this could be