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Extracting and formally representing the knowledge embedded in textbooks, such as the concepts explained and the relations between them, can support the provision of advanced knowledge-based services for learning environments and digital libraries. In this paper, we consider a specific type of relation in textbooks referred to as prerequisite relations (PR). PRs represent precedence relations between concepts aimed to provide the reader with the knowledge needed to understand a further concept(s). Their annotation in educational texts produces datasets that can be represented as a graph of concepts connected by PRs. However, building good-quality and reliable datasets of PRs from a textbook is still an open issue, not just for automated annotation methods but even for manual annotation. In turn, the lack of good-quality datasets and well-defined criteria to identify PRs affect the development and validation of automated methods for prerequisite identification. As a contribution to this issue, in this paper, we propose PREAP, a protocol for the annotation of prerequisite relations in textbooks aimed at obtaining reliable annotated data that can be shared, compared, and reused in the research community. PREAP defines a novel textbook-driven annotation method aimed to capture the structure of prerequisites underlying the text. The protocol has been evaluated against baseline methods for manual and automatic annotation. The findings show that PREAP enables the creation of prerequisite knowledge graphs that have higher inter-annotator agreement, accuracy, and alignment with text than the baseline methods. This suggests that the protocol is able to accurately capture the PRs expressed in the text. Furthermore, the findings show that the time required to complete the annotation using PREAP are significantly shorter than with the other manual baseline methods. The paper includes also guidelines for using PREAP in three annotation scenarios, experimentally tested. We also provide example datasets and a user interface that we developed to support prerequisite annotation. [ABSTRACT FROM AUTHOR]

Purpose: The authors' goal is to investigate variations in the writing style of book reviews published on different social reading platforms and referring to books of different genres, which enables acquiring insights into communication strategies adopted by readers to share their reading experiences. Design/methodology/approach: The authors propose a corpus-based study focused on the analysis of A Good Review, a novel corpus of online book reviews written in Italian, posted on Amazon and Goodreads, and covering six literary fiction genres. The authors rely on stylometric analysis to explore the linguistic properties and lexicon of reviews and the authors conducted automatic classification experiments using multiple approaches and feature configurations to predict either the review's platform or the literary genre. Findings: The analysis of user-generated reviews demonstrates that language is a quite variable dimension across reading platforms, but not as much across book genres. The classification experiments revealed that features modelling the syntactic structure of the sentence are reliable proxies for discerning Amazon and Goodreads reviews, whereas lexical information showed a higher predictive role for automatically discriminating the genre. Originality/value: The high availability of cultural products makes information services necessary to help users navigate these resources and acquire information from unstructured data. This study contributes to a better understanding of the linguistic characteristics of user-generated book reviews, which can support the development of linguistically-informed recommendation services. Additionally, the authors release a novel corpus of online book reviews meant to support the reproducibility and advancements of the research. [ABSTRACT FROM AUTHOR]

The outstanding performance recently reached by neural language models (NLMs) across many natural language processing (NLP) tasks has steered the debate towards understanding whether NLMs implicitly learn linguistic competence. Probes, i.e., supervised models trained using NLM representations to predict linguistic properties, are frequently adopted to investigate this issue. However, it is still questioned if probing classification tasks really enable such investigation or if they simply hint at surface patterns in the data. This work contributes to this debate by presenting an approach to assessing the effectiveness of a suite of probing tasks aimed at testing the linguistic knowledge implicitly encoded by one of the most prominent NLMs, BERT. To this aim, we compared the performance of probes when predicting gold and automatically altered values of a set of linguistic features. Our experiments were performed on Italian and were evaluated across BERT's layers and for sentences with different lengths. As a general result, we observed higher performance in the prediction of gold values, thus suggesting that the probing model is sensitive to the distortion of feature values. However, our experiments also showed that the length of a sentence is a highly influential factor that is able to confound the probing model's predictions. [ABSTRACT FROM AUTHOR]

The act of lying and its detection have raised interest in many fields, from the legal system to our daily lives. Considering that testimonies are commonly based on linguistic parameters, natural language processing, a research field concerned with programming computers to process and analyse natural language texts or speech, is a topic of interest on this front. This study aimed to examine the linguistic styles of simulated deception and true testimonies collected with the aim of studying witness memory. Study participants were asked to act as a witness of a crime by retelling the story they had just read. Cognitive interviewing techniques were used to collect testimony under two conditions: truth and simulated deception. A sample of 48 participants volunteered to participate in the study. Analyses of the linguistic indicators and content were carried out. Specifically, we performed a comparison of testimonies of the same participant by condition to analyse the variation between (i) lexical and (ii) linguistic features and (iii) content and speech characteristics (disfluencies) depending on the narrative condition. Concerning lexical properties, adjectives were the most-varying grammatical category between truthful and deceptive testimonies. Furthermore, in the linguistic analysis, we observed that truthful testimonies were generally longer than deceptive ones in terms of the number of words and sentences and also characterised by more articulated sentence structures, and these differences were also statistically significant. Regarding the analysis of the content, cognitive criteria (details) and admitting lack of memory were more present in truthful statements. By providing an objective measure, these results are of interest in developing NLP tools for assessing the credibility of testimonies in forensics. [ABSTRACT FROM AUTHOR]