A Lightweight Regression Method to Infer Psycholinguistic Properties for Brazilian Portuguese

Psycholinguistic properties of words have been used in various approaches to Natural Language Processing tasks, such as text simplification and readability assessment. Most of these properties are subjective, involving costly and time-consuming surveys to be gathered. Recent approaches use the limited datasets of psycholinguistic properties to extend them automatically to large lexicons. However, some of the resources used by such approaches are not available to most languages. This study presents a method to infer psycholinguistic properties for Brazilian Portuguese (BP) using regressors built with a light set of features usually available for less resourced languages: word length, frequency lists, lexical databases composed of school dictionaries and word embedding models. The correlations between the properties inferred are close to those obtained by related works. The resulting resource contains 26,874 words in BP annotated with concreteness, age of acquisition, imageability and subjective frequency.Comment: Paper accepted for TSD201

NILC-Metrix : assessing the complexity of written and spoken language in Brazilian Portuguese

This paper presents and makes publicly available the NILC-Metrix, a computational system comprising 200 metrics proposed in studies on discourse, psycholinguistics, cognitive and computational linguistics, to assess textual complexity in Brazilian Portuguese (BP). These metrics are relevant for descriptive analysis and the creation of computational models and can be used to extract information from various linguistic levels of written and spoken language. The metrics in NILC-Metrix were developed during the last 13 years, starting in 2008 with Coh-Metrix-Port, a tool developed within the scope of the PorSimples project. Coh-Metrix-Port adapted some metrics to BP from the Coh-Metrix tool that computes metrics related to cohesion and coherence of texts in English. After the end of PorSimples in 2010, new metrics were added to the initial 48 metrics of Coh-Metrix-Port. Given the large number of metrics, we present them following an organisation similar to the metrics of Coh-Metrix v3.0 to facilitate comparisons made with metrics in Portuguese and English. In this paper, we illustrate the potential of NILC-Metrix by presenting three applications: (i) a descriptive analysis of the differences between children's film subtitles and texts written for Elementary School I and II (Final Years); (ii) a new predictor of textual complexity for the corpus of original and simplified texts of the PorSimples project; (iii) a complexity prediction model for school grades, using transcripts of children's story narratives told by teenagers. For each application, we evaluate which groups of metrics are more discriminative, showing their contribution for each task

Speaking while listening: Language processing in speech shadowing and translation

Contains fulltext : 233349.pdf (Publisher’s version ) (Open Access)Radboud University, 25 mei 2021Promotores : Meyer, A.S., Roelofs, A.P.A.199 p

A survey on lexical simplification

Lexical Simplification is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning. This task has wide applicability both as an assistive technology for readers with cognitive impairments or disabilities, such as Dyslexia and Aphasia, and as a pre-processing tool for other Natural Language Processing tasks, such as machine translation and summarisation. The problem is commonly framed as a pipeline of four steps: the identification of complex words, the generation of substitution candidates, the selection of those candidates that fit the context, and the ranking of the selected substitutes according to their simplicity. In this survey we review the literature for each step in this typical Lexical Simplification pipeline and provide a benchmarking of existing approaches for these steps on publicly available datasets. We also provide pointers for datasets and resources available for the task

Acessibilidade do termo de consentimento na pesquisa clínica no Brasil

Resumo O termo de consentimento livre e esclarecido tem a função de informar o participante de pesquisas clínicas sobre a natureza da pesquisa e seus direitos, formalizando sua decisão de participar. Estudos indicam que esse documento é redigido de modo complexo, comprometendo a autonomia do participante. Para este trabalho, foram redigidos dois termos de consentimento da mesma pesquisa hipotética, com estilos de redação diferentes. Ambos os termos foram analisados pela ferramenta Coh-Metrix Port, que avalia métricas linguísticas e acessibilidade textual. A análise indicou que os textos são complexos e exigem alta escolaridade para serem entendidos. Esses achados reforçam a percepção de que, no Brasil, os termos de consentimento podem ter sua real função comprometida e apontam a importância de modificar sua forma de elaboração

Lexical Simplification for Non-Native English Speakers

Lexical Simplification is the process of replacing complex words in texts to create simpler, more easily comprehensible alternatives. It has proven very useful as an assistive tool for users who may find complex texts challenging. Those who suffer from Aphasia and Dyslexia are among the most common beneficiaries of such technology. In this thesis we focus on Lexical Simplification for English using non-native English speakers as the target audience. Even though they number in hundreds of millions, there are very few contributions that aim to address the needs of these users. Current work is unable to provide solutions for this audience due to lack of user studies, datasets and resources. Furthermore, existing work in Lexical Simplification is limited regardless of the target audience, as it tends to focus on certain steps of the simplification process and disregard others, such as the automatic detection of the words that require simplification. We introduce a series of contributions to the area of Lexical Simplification that range from user studies and resulting datasets to novel methods for all steps of the process and evaluation techniques. In order to understand the needs of non-native English speakers, we conducted three user studies with 1,000 users in total. These studies demonstrated that the number of words deemed complex by non-native speakers of English correlates with their level of English proficiency and appears to decrease with age. They also indicated that although words deemed complex tend to be much less ambiguous and less frequently found in corpora, the complexity of words also depends on the context in which they occur. Based on these findings, we propose an ensemble approach which achieves state-of-the-art performance in identifying words that challenge non-native speakers of English. Using the insight and data gathered, we created two new approaches to Lexical Simplification that address the needs of non-native English speakers: joint and pipelined. The joint approach employs resource-light neural language models to simplify words deemed complex in a single step. While its performance was unsatisfactory, it proved useful when paired with pipelined approaches. Our pipelined simplifier generates candidate replacements for complex words using new, context-aware word embedding models, filters them for grammaticality and meaning preservation using a novel unsupervised ranking approach, and finally ranks them for simplicity using a novel supervised ranker that learns a model based on the needs of non-native English speakers. In order to test these and previous approaches, we designed LEXenstein, a framework for Lexical Simplification, and compiled NNSeval, a dataset that accounts for the needs of non-native English speakers. Comparisons against hundreds of previous approaches as well as the variants we proposed showed that our pipelined approach outperforms all others. Finally, we introduce PLUMBErr, a new automatic error identification framework for Lexical Simplification. Using this framework, we assessed the type and number of errors made by our pipelined approach throughout the simplification process and found that combining our ensemble complex word identifier with our pipelined simplifier yields a system that makes up to 25% fewer mistakes compared to the previous state-of-the-art strategies during the simplification process

Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy

Computational approaches to semantic change (Volume 6)

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans