An eye-tracking evaluation of some parser complexity metrics

Information theoretic measures of incremental parser load were generated from a phrase structure parser and a dependency parser and then compared with incremental eye movement metrics collected for the same temporarily syntactically ambiguous sentences, focussing on the disambiguating word. The findings show that the surprisal and entropy reduction metrics computed over a phrase structure grammar make good candidates for predictors of text readability for human comprehenders. This leads to a suggestion for the use of such metrics in Natural Language Generation (NLG

New Data-Driven Approaches to Text Simplification

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of PhilosophyMany texts we encounter in our everyday lives are lexically and syntactically very complex. This makes them difficult to understand for people with intellectual or reading impairments, and difficult for various natural language processing systems to process. This motivated the need for text simplification (TS) which transforms texts into their simpler variants. Given that this is still a relatively new research area, many challenges are still remaining. The focus of this thesis is on better understanding the current problems in automatic text simplification (ATS) and proposing new data-driven approaches to solving them. We propose methods for learning sentence splitting and deletion decisions, built upon parallel corpora of original and manually simplified Spanish texts, which outperform the existing similar systems. Our experiments in adaptation of those methods to different text genres and target populations report promising results, thus offering one possible solution for dealing with the scarcity of parallel corpora for text simplification aimed at specific target populations, which is currently one of the main issues in ATS. The results of our extensive analysis of the phrase-based statistical machine translation (PB-SMT) approach to ATS reject the widespread assumption that the success of that approach largely depends on the size of the training and development datasets. They indicate more influential factors for the success of the PB-SMT approach to ATS, and reveal some important differences between cross-lingual MT and the monolingual v MT used in ATS. Our event-based system for simplifying news stories in English (EventSimplify) overcomes some of the main problems in ATS. It does not require a large number of handcrafted simplification rules nor parallel data, and it performs significant content reduction. The automatic and human evaluations conducted show that it produces grammatical text and increases readability, preserving and simplifying relevant content and reducing irrelevant content. Finally, this thesis addresses another important issue in TS which is how to automatically evaluate the performance of TS systems given that access to the target users might be difficult. Our experiments indicate that existing readability metrics can successfully be used for this task when enriched with human evaluation of grammaticality and preservation of meaning

An Automatic Modern Standard Arabic Text Simplification System: A Corpus-Based Approach

This thesis brings together an overview of Text Readability (TR) about Text Simplification (TS) with an application of both to Modern Standard Arabic (MSA). It will present our findings on using automatic TR and TS tools to teach MSA, along with challenges, limitations, and recommendations about enhancing the TR and TS models. Reading is one of the most vital tasks that provide language input for communication and comprehension skills. It is proved that the use of long sentences, connected sentences, embedded phrases, passive voices, non- standard word orders, and infrequent words can increase the text difficulty for people with low literacy levels, as well as second language learners. The thesis compares the use of sentence embeddings of different types (fastText, mBERT, XLM-R and Arabic-BERT), as well as traditional language features such as POS tags, dependency trees, readability scores and frequency lists for language learners. The accuracy of the 3-way CEFR (The Common European Framework of Reference for Languages Proficiency Levels) classification is F-1 of 0.80 and 0.75 for Arabic-Bert and XLM-R classification, respectively and 0.71 Spearman correlation for the regression task. At the same time, the binary difficulty classifier reaches F-1 0.94 and F-1 0.98 for the sentence-pair semantic similarity classifier. TS is an NLP task aiming to reduce the linguistic complexity of the text while maintaining its meaning and original information (Siddharthan, 2002; Camacho Collados, 2013; Saggion, 2017). The simplification study experimented using two approaches: (i) a classification approach and (ii) a generative approach. It then evaluated the effectiveness of these methods using the BERTScore (Zhang et al., 2020) evaluation metric. The simple sentences produced by the mT5 model achieved P 0.72, R 0.68 and F-1 0.70 via BERTScore while combining Arabic- BERT and fastText achieved P 0.97, R 0.97 and F-1 0.97. To reiterate, this research demonstrated the effectiveness of the implementation of a corpus-based method combined with extracting extensive linguistic features via the latest NLP techniques. It provided insights which can be of use in various Arabic corpus studies and NLP tasks such as translation for educational purposes

Readability assessment and automatic text simplification, the analysis of basque complex structures

301 p.(eus); 217 (eng)Tesi-lan honetan, euskarazko testuen konplexutasuna eta sinplifikazioa automatikoki aztertzeko lehen urratsak egin ditugu. Testuen konplexutasuna aztertzeko, testuen sinplifikazio automatikoa helburu duten beste hizkuntzetako lanetan eta euskarazko corpusetan egindako azterketa linguistikoan oinarritu gara. Azterketa horietatik testuak automatikoki sinplifikatzeko oinarri linguistikoak ezarri ditugu. Konplexutasuna automatikoki analizatzeko, ezaugarri linguistikoetan eta ikasketa automatikoko tekniketan oinarrituta ErreXail sistema sortu eta inplementatu dugu.Horretaz gain, testuak automatikoki sinplifikatuko dituen Euskarazko Testuen Sinplifikatzailea (EuTS) sistemaren arkitektura diseinatu dugu, sistemaren modulu bakoitzean egingo diren eragiketak definituz eta, kasu-azterketa bezala,informazio biografikoa duten egitura parentetikoak sinplifikatuko dituen Biografix tresna eleaniztuna inplementatuz.Amaitzeko, Euskarazko Testu Sinplifikatuen Corpusa (ETSC) corpusa osatu dugu. Corpus hau baliatu dugu gure sinplifikaziorako azterketetatik ateratako hurbilpena beste batzuekin erkatzeko. Konparazio horiek egiteko, etiketatze-eskema bat ere definitu dugu

Beyond topic-based representations for text mining

A massive amount of online information is natural language text: newspapers, blog articles, forum posts and comments, tweets, scientific literature, government documents, and more. While in general, all kinds of online information is useful, textual information is especially important—it is the most natural, most common, and most expressive form of information. Text representation plays a critical role in application tasks like classification or information retrieval since the quality of the underlying feature space directly impacts each task's performance. Because of this importance, many different approaches have been developed for generating text representations. By far, the most common way to generate features is to segment text into words and record their n-grams. While simple term features perform relatively well in topic-based tasks, not all downstream applications are of a topical nature and can be captured by words alone. For example, determining the native language of an English essay writer will depend on more than just word choice. Competing methods to topic-based representations (such as neural networks) are often not interpretable or rely on massive amounts of training data. This thesis proposes three novel contributions to generate and analyze a large space of non-topical features. First, structural parse tree features are solely based on structural properties of a parse tree by ignoring all of the syntactic categories in the tree. An important advantage of these "skeletons" over regular syntactic features is that they can capture global tree structures without causing problems of data sparseness or overfitting. Second, SyntacticDiff explicitly captures differences in a text document with respect to a reference corpus, creating features that are easily explained as weighted word edit differences. These edit features are especially useful since they are derived from information not present in the current document, capturing a type of comparative feature. Third, Cross-Context Lexical Analysis is a general framework for analyzing similarities and differences in both term meaning and representation with respect to different, potentially overlapping partitions of a text collection. The representations analyzed by CCLA are not limited to topic-based features

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal

Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition. Volume 2 : Traitement Automatique des Langues Naturelles

@ 6ème conférence conjointe: JEP-TALN-RECITAL 2020no abstrac

Contextual Understanding of Sequential Data Across Multiple Modalities

In recent years, progress in computing and networking has made it possible to collect large volumes of data for various different applications in data mining and data analytics using machine learning methods. Data may come from different sources and in different shapes and forms depending on their inherent nature and the acquisition process. In this dissertation, we focus specifically on sequential data, which have been exponentially growing in recent years on platforms such as YouTube, social media, news agency sites, and other platforms. An important characteristic of sequential data is the inherent causal structure with latent patterns that can be discovered and learned from samples of the dataset. With this in mind, we target problems in two different domains of Computer Vision and Natural Language Processing that deal with sequential data and share the common characteristics of such data. The first one is action recognition based on video data, which is a fundamental problem in computer vision. This problem aims to find generalized patterns from videos to recognize or predict human actions. A video contains two important sets of information, i.e. appearance and motion. These information are complementary, and therefore an accurate recognition or prediction of activities or actions in video data depend significantly on our ability to extract them both. However, effective extraction of these information is a non-trivial task due to several challenges, such as viewpoint changes, camera motions, and scale variations, to name a few. It is thus crucial to design effective and generalized representations of video data that learn these variations and/or are invariant to such variations. We propose different models that learn and extract spatio-temporal correlations from video frames by using deep networks that overcome these challenges. The second problem that we study in this dissertation in the context of sequential data analysis is text summarization in multi-document processing. Sentences consist of sequence of words that imply context. The summarization task requires learning and understanding the contextual information from each sentence in order to determine which subset of sentences forms the best representative of a given article. With the progress made by deep learning, better representations of words have been achieved, leading in turn to better contextual representations of sentences. We propose summarization methods that combine mathematical optimization, Determinantal Point Processes (DPPs), and deep learning models that outperform the state of the art in multi-document text summarization

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe