Improving Automated Prediction of E nglish Lexical Blends Through the Use of Observable Linguistic Features (2023)

Abstract: The process of lexical blending is difficult to reliably predict. This difficulty has been shown by machine learning approaches in blend modeling, including attempts using then state-of-the-art LSTM deep neural networks trained on character embeddings, which were able to predict lexical blends given the ordered constituent words in less than half of cases, at maximum. This project introduces a novel model architecture which dramatically increases the correct prediction rates for lexical blends, using only Polynomial regression and Random Forest models. This is achieved by generating multiple possible blend candidates for each input word pairing and evaluating them based on observable linguistic features. The success of this model architecture illustrates the potential usefulness of observable linguistic features for problems that elude more advanced models which utilize only features discovered in the latent space.

## introduction 1.descriptive research on lexical blends lexical blends have long been noted as a linguistic phenomenon with little consistent predictability. researchers have described many different factors which affect how much of two given input words will be preserved in the resulting blend. this is often described in terms of the "switchpoint," or point at which each input word is truncated. factors described in the literature include a tendency for words to split at syllable constituent boundaries @xcite , an observation that blends tend to match the length of the 2 nd input word @xcite , and a finding that the prosodic structure @xcite . none of these noted tendencies or a combination thereof has thus far been used to create a predictive model of blending. ## methods the model architecture laid out in this paper included the following elements: the generation process was performed by iteratively creating prefixes from the first input word and suffixes from the second input word using grapheme-phoneme alignments, such that the substring consisted of a contiguous sequence of phonemes and their corresponding graphemes. each prefix was then concatenated with each suffix to produce the full candidate set for each input word pair. feature values were calculated for each candidate using a set of phonemically-defined features, modified from arndt-lappe & plag (2013). labels were assigned to each candidate based on whether the graphemes of the candidate matched the desired blend output. candidates with feature values which were identical to a candidate already in the feature set were removed. given the feature values for all candidates, we used random forest classifiers and polynomial regression models to learn probabilities for each candidate based on the extracted feature values. rather than assign each data instance to a class, probabilities are retained for each candidate so that the candidate with the maximum probability can be selected. random forest and polynomial regression were chosen for this experiment because they are easier to train and interpret than deep neural approaches. finally, a selection component was used to find the candidate from each input word pairing with the greatest probability of being a valid blend of english. this candidate was then chosen as the model's predicted output for that input word pairing. ## experiments the specific trials the model was used for are given here, along with the datasets utilized in training/testing for those trials. ## results model predictions were evaluated on the average percentage of blends correctly predicted and the average levenshtein edit distance between the predicted output form and the correct blend form.