Guessing the Age of Acquisition of I talian Lemmas through Linear Regression (2020)

Abstract: The age of acquisition of a word is a psycholinguistic variable concerning the age at which a word is typically learned. It correlates with other psycholinguistic variables such as familiarity, concreteness, and imageability. Existing datasets for multiple languages also include linguistic variables such as the length and the frequency of lemmas in different corpora. There are substantial sets of normative values for English, but for other languages, such as Italian, the coverage is scarce. In this paper,a set of regression experiments investigates whether it is possible to guess the age of acquisition of Italian lemmas that have not been previously rated by humans. An intrinsic evaluation is proposed, correlating estimated Italian lemmas’ AoA with English lemmas’ AoA. An extrinsic evaluation - using AoA values as features for the classification of literary excerpts labeled by age appropriateness - shows how es-sential is lexical coverage for this task.

## introduction the age of acquisition (aoa, henceforth) of a word as the age at which a word was typically learned is a well-know psycholinguistic variable investigated for multiple languages @xcite @xcite . it correlates with other variables such as concreteness of a word, frequency in a corpus, length of the word in letters and syllables. aoa estimates can be obtained by asking parents to record data about their children while they grow up. they are more frequently obtained by requesting the experiment's participants to indicate at which age they learned different words. the collection of such ratings in an experimental setting is time-consuming. for this reason, existing datasets tend to have low coverage. with the advent of crowdsourcing platforms, there have been multiple efforts to enlarge the lists of aoa estimates ask-ing for words' ratings. participants self-assessed the age (in years) at which they thought they had learned the word, meaning that they would have understood that word even if they would not have been able to use, read, or write it. crowdsourced aoa estimates show a good correlation with analogous ratings from traditional experimental settings, validating this methodology to improve aoa lists' coverage in less time @xcite . for psycholinguists, the aoa of a word is a crucial variable in the selection process of the stimuli for lexical decision task experiments. its correlation with other variables indicates that multiple factors should be taken into account when testing hypotheses involving lexical semantics, to ensure the right level of variability and complexity in the stimuli set. these multiple factors correlated with aoa of a lemma make it possible to use a regression model to guess lemmas' aoa foot_0 . in this paper, a set of regression experiments investigates which features help to guess the age of acquisition of italian lemmas that humans have not previously rated. an intrinsic evaluation is proposed, correlating estimated italian lemmas' aoa with english lemmas' aoa, as proposed by @xcite to test the generalizability of aoa ratings. the extrinsic evaluation investigates if a list of aoa with better coverage could be beneficial for all those nlp tasks that classify or order texts according to their cognitive complexity, following the research hypotheses investigated for english @xcite . more specifically, through a regression-as-classification approach, preliminary results on the role of aoa of lemmas for the classification of children's literature excerpts labeled with age appropriateness information are proposed. ## related works several studies analyze how to extract psycholinguistic variables with corpus-based methodologies, with the aim of testing if this kind of knowledge is implicitly contained in the language. one practical aim is to create larger datasets of psycholinguistic norms avoiding the time-consuming phase of collections involving participants. the seminal work of @xcite proposes latent semantic analysis to estimate lexical norms of words, obtaining satisfactory results for concreteness, imagery, and valence but less good for arousal and dominance. the age of acquisition is not included among the variables. @xcite build a semantic similarity space and apply machine learning techniques to extrapolate existing ratings of previously unrated words for five psycholinguistic properties (age of acquisition, concreteness, arousal, dominance and valence). the results are encouraging in terms of correlation with human norms. for example, the pearson correlation is 0.737 for the age of acquisition of words, meaning that words related to similar topics are more likely to be acquired around the same age. however, according to the authors, evaluating the results in a lexical decision task is not satisfactory from a psycholinguistic perspective. the methodology used may introduce artifacts to the data and produce results that could lead to different conclusions that would be reached based on human ratings. @xcite use principal component analysis to understand the semantic dimensions along which the skip-gram model organizes meaning, finding how many dimensions are correlated with a particular semantic and lexical variable among the ones relevant for psycholinguistic research. their findings confirm that the age of acquisition is in some way encoded in the semantic vector representation for word meanings, but no predictive methodology to assign a value to unrated words is implemented. @xcite compare a distributional semantic model derived from word cooccurrences and a word association based model (with data collected from human subjects) in predicting psycholinguistic properties of words that affect lexical processing for dutch. overall, the correlations show a better performance of the method-ology implemented (knn) when word association information is used. among the variables, estimates for the age of acquisition display the lowest correlation, while affective variables such as valence and concreteness are clearly encoded in the semantic models tested. ## datasets a short description of all the datasets used in the experiments reported in section 3 is provided in this section. montefinese et al. (2019) : this dataset contains ratings for 1,957 italian content lemmas evaluated by 507 native italian speakers recruited online. the stimuli were distributed over 20 lists containing 97-98 words each; each lemma was rated by 25 participants. the collected judgments are plausible because of strong internal reliability and a good correlation (pearson @xmath0.697) with translated english norms @xcite . this dataset also contains information about the word length (i.e., number of characters), the word frequency from two different corpora (la repubblica and itwac), and the number of orthographic neighbors. table1 reports the composition of the norms in terms of parts of speech. it is composed of dense annotations of objects, attributes, and relationships between objects for 108k images. as a pre-processing step, the descriptions have been annotated with treetagger @xcite and extracted the list of lemmas ordered by frequency. frequencies in visual genome are included as a feature in linear regression experiments for estimating aoa of italian lemmas to counterbalance la repubblica frequency list where abstract meanings are more frequent. ## experiments the training set is a subset of the italian aoa norms dataset @xcite resulting from the intersection of all datasets that contain features used for the linear regression experiments. as a consequence, it is smaller than the original dataset (see the following features are included in the training set: a linear regression model is implemented and its performance is evaluated through 10-cross fold validation on the italian norms training set. the results for different combination of features are reported in english translation found in the open multilingual wordnet @xcite . the estimaed aoa ratings are evaluated through a comparison with the english ones provided by @xcite . this comparison represents an intrinsic evaluation of the models since pearson correlation between italian and english aoa lemmas has been used by @xcite to validate the generalizability of the collected norms. the correlation reported by the authors was 0.697. the best combination of features was found experimenting with @xcite 's dataset, considering accuracy after rounding the linear regression outputs. in ## conclusions and future works in this paper, a set of regression experiments investigates if it is possible to guess the age of acquisition of italian lemmas that humans have not previously rated by humans. an intrinsic and extrinsic evaluation of the output is proposed. the results show that the overall quality of the estimated ratings enables their inclusion in nlp systems, even if they would not probably be satisfying for psycholinguistic experiments. more specifically, increasing the coverage of lexical resources containing aoa is beneficial for age appropriateness text classification. as future work, the testing of semantic models for estimating the age of acquisition of italian lemmas is relevant. since the difficulty of a text could be assessed taking into account psycholinguistic variables that influence the cognitive complexity of the reading process, another interesting working hypothesis concerns the use of aoa features in other experiments involving the complexity of texts, such as readability assessment, l2 learners' written production, automatic assessment of text fluency for natural language generation outputs' evaluation.