Sentence simplification for semantic role labelling and information extraction

In this paper, we report on the extrinsic evaluation of an automatic sentence simplification method with respect to two NLP tasks: semantic role labelling (SRL) and information extraction (IE). The paper begins with our observation of challenges in the intrinsic evaluation of sentence simplification systems, which motivates the use of extrinsic evaluation of these systems with respect to other NLP tasks. We describe the two NLP systems and the test data used in the extrinsic evaluation, and present arguments and evidence motivating the integration of a sentence simplification step as a means of improving the accuracy of these systems. Our evaluation reveals that their performance is improved by the simplification step: the SRL system is better able to assign semantic roles to the majority of the arguments of verbs and the IE system is better able to identify fillers for all IE template slots

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

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

Identifying Signs of Syntactic Complexity for Rule-Based Sentence Simplification

This article presents a new method to automatically simplify English sentences. The approach is designed to reduce the number of compound clauses and nominally bound relative clauses in input sentences. The article provides an overview of a corpus annotated with information about various explicit signs of syntactic complexity and describes the two major components of a sentence simplification method that works by exploiting information on the signs occurring in the sentences of a text. The first component is a sign tagger which automatically classifies signs in accordance with the annotation scheme used to annotate the corpus. The second component is an iterative rule-based sentence transformation tool. Exploiting the sign tagger in conjunction with other NLP components, the sentence transformation tool automatically rewrites long sentences containing compound clauses and nominally bound relative clauses as sequences of shorter single-clause sentences. Evaluation of the different components reveals acceptable performance in rewriting sentences containing compound clauses but less accuracy when rewriting sentences containing nominally bound relative clauses. A detailed error analysis revealed that the major sources of error include inaccurate sign tagging, the relatively limited coverage of the rules used to rewrite sentences, and an inability to discriminate between various subtypes of clause coordination. Despite this, the system performed well in comparison with two baselines. This finding was reinforced by automatic estimations of the readability of system output and by surveys of readers’ opinions about the accuracy, accessibility, and meaning of this output

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

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

Computational Etymology: Word Formation and Origins

While there are over seven thousand languages in the world, substantial language technologies exist only for a small percentage of these. The large majority of world languages do not have enough bilingual or even monolingual data for developing technologies like machine translation using current approaches. The computational study and modeling of word origins and word formation is a key step in developing comprehensive translation dictionaries for low-resource languages. This dissertation presents novel foundational work in computational etymology, a promising field which this work is pioneering. The dissertation also includes novel models of core vocabulary, dictionary information distillation, and of the diverse linguistic processes of word formation and concept realization between languages, including compounding, derivation, sense-extension, borrowing, and historical cognate relationships, utilizing statistical and neural models trained on the unprecedented scale of thousands of languages. Collectively these are important components in tackling the grand challenges of universal translation, endangered language documentation and revitalization, and supporting technologies for speakers of thousands of underserved languages

Handbook of Easy Languages in Europe

The Handbook of Easy Languages in Europe describes what Easy Language is and how it is used in European countries. It demonstrates the great diversity of actors, instruments and outcomes related to Easy Language throughout Europe. All people, despite their limitations, have an equal right to information, inclusion, and social participation. This results in requirements for understandable language. The notion of Easy Language refers to modified forms of standard languages that aim to facilitate reading and language comprehension. This handbook describes the historical background, the principles and the practices of Easy Language in 21 European countries. Its topics include terminological definitions, legal status, stakeholders, target groups, guidelines, practical outcomes, education, research, and a reflection on future perspectives related to Easy Language in each country. Written in an academic yet interesting and understandable style, this Handbook of Easy Languages in Europe aims to find a wide audience

Handbook of Easy Languages in Europe

The Handbook of Easy Languages in Europe describes what Easy Language is and how it is used in European countries. It demonstrates the great diversity of actors, instruments and outcomes related to Easy Language throughout Europe. All people, despite their limitations, have an equal right to information, inclusion, and social participation. This results in requirements for understandable language. The notion of Easy Language refers to modified forms of standard languages that aim to facilitate reading and language comprehension. This handbook describes the historical background, the principles and the practices of Easy Language in 21 European countries. Its topics include terminological definitions, legal status, stakeholders, target groups, guidelines, practical outcomes, education, research, and a reflection on future perspectives related to Easy Language in each country. Written in an academic yet interesting and understandable style, this Handbook of Easy Languages in Europe aims to find a wide audience

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