Enhancing Lexical Complexity Prediction in Italian through Automatic Morphological Segmentation
Authors: Laura Occhipinti (Università di Bologna, 2024)
Topic: Lexical and Semantic Resources and Analysis

Abstract
Morphological analysis is essential for various Natural Language Processing (NLP) tasks, as it reveals the internal structure of words and deepens our understanding of their morphological and syntactic relationships. This study focuses on surface morphological segmentation for the Italian language, addressing the limited representation of detailed morphological information in existing corpora. Using an automatic segmentation tool, we extract quantitative morphological parameters to investigate their impact on the perception of word complexity by native Italian speakers. Through correlation analysis, we demonstrate that morphological features, such as the number of morphemes and lexical morpheme frequency, significantly influence how complex words are perceived. These insights contribute to improving automatic lexical complexity prediction models and offer a deeper understanding of the role of morphology in word comprehension.

Introduction
Morphological analysis is crucial for various NLP tasks, as it provides insights into the internal structures of words and helps us better understand the morphological and syntactic relationships between words [1]. The Italian language, with its rich morphology and extensive use of inflection and derivation, presents unique challenges and opportunities for morphological segmentation. Automatic segmentation, a key component of morphology learning, involves dividing word forms into meaningful units such as roots, prefixes, and suffixes [2]. This task falls under the broader category of subword segmentation [3] but is distinct due to its linguistic motivation. Computational approaches typically identify subwords based on purely statistical considerations, which often results in subunits that do not correspond to recognizable linguistic units [4, 5, 6, 7]. Making this task more morphologically oriented could enable models to generalize better to new words or forms, as basic roots or morphemes are often shared among words, and it could also facilitate the interpretation of model results. When discussing morphological segmentation, we can refer to two types: (1) Surface segmentation, which involves dividing words into morphs, the surface forms of morphemes; (2) Canonical segmentation, which involves dividing words into morphemes and reducing them to their standard forms [8]. For instance, consider the Italian word mangiavano (they were eating). The resulting surface segmentation would be mangi- + -avano, where mangi- is a morph derived from the root of the verb mangiare, and -avano is the suffix indicating the third person plural of the imperfect tense. In contrast, the canonical segmentation would yield mangiare + -avano, with mangiare as the canonical morpheme and -avano as the suffix. In this study, we focus on surface morphological segmentation for the Italian language. Morphological features are often not adequately represented in available corpora for this language, or they refer exclusively to morphosyntactic information, such as the grammatical category of words and a macro-level descriptive analysis mainly related to inflection. Information about the internal structure of words, such as derivation or composition, is often lacking. The primary objective of this work is to use an automatic segmenter to extract a series of quantitative morphological parameters. We believe that our approach does not require the detailed analysis provided by canonical segmentation, which could entail longer processing times. In addition to examining classic parameters reported in the literature that influence complexity [9], such as word frequency, length, and number of syllables, we aim to explore how morphological features integrate with these factors to affect word complexity perception. Specifically, we seek to understand how the internal structure of words contributes to the cognitive load that speakers experience when processing more complex lexical items. Our premise is that words with more morphemes are more complex because they contain more information to decode [10]. For example, consider the word infelicità (unhappiness). To decode it, one must know the word felice (happy), from which it is derived, as well as the prefix in-, which negates the quality expressed by the base term, and the suffix -ità, which transforms the adjective into an abstract noun. Therefore, to fully understand the meaning of infelicità, the reader or listener must be able to correctly recognize and interpret each of these morphemes and their contribution to the overall meaning of the word. The main contributions of this work are: (1) Providing a tool capable of automatically segmenting words into linguistically motivated base forms; (2) presenting the dataset constructed for training our model; (3) evaluating the impact of different linguistic features on speakers’ perception of word complexity, with a particular focus on morphological features.

Conclusion
This study highlights the significance of integrating morphological features into automatic models to enhance the comprehension and prediction of lexical complexity. The high performance of the Neural Morpheme Segmentation model demonstrates the efficacy of convolutional neural networks in capturing the detailed patterns of morphological segmentation in the Italian language. The correlation analysis reveals that while traditional metrics like word length and frequency are valuable predictors of complexity, incorporating morphological features provides additional insights that enrich our understanding of lexical complexity. Notably, the positive correlation between the number of morphemes and perceived complexity suggests that words with more morphemes are inherently more complex. Conversely, frequent lexical morphemes tend to reduce perceived complexity, highlighting the importance of familiarity in complexity perception. Our study also emphasizes the need for diverse linguistic features, including both surface characteristics and morphological traits, to create more robust and accurate models for predicting word complexity. The statistically significant correlations for most features validate their relevance in complexity prediction. However, it is important to note that our findings are based on a relatively small dataset of annotated complexity perceptions. To obtain more robust and generalizable results, it would be highly beneficial to have access to a larger and more diverse dataset of complexity annotations. Expanding the dataset to include a wider variety of texts and contexts would enhance the reliability of the correlations observed and improve the training and evaluation of automatic complexity prediction models. Future research should focus on gathering more extensive annotated datasets and exploring additional linguistic features that may influence complexity perception. By doing so, we can further refine our models and develop more effective tools for lexical simplification and other applications aimed at improving text accessibility.