Paloma Moreda Pozo

Also published as: Paloma Moreda


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A Review of Research-Based Automatic Text Simplification Tools
Isabel Espinosa-Zaragoza | José Abreu-Salas | Elena Lloret | Paloma Moreda | Manuel Palomar
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing

In the age of knowledge, the democratisation of information facilitated through the Internet may not be as pervasive if written language poses challenges to particular sectors of the population. The objective of this paper is to present an overview of research-based automatic text simplification tools. Consequently, we describe aspects such as the language, language phenomena, language levels simplified, approaches, specific target populations these tools are created for (e.g. individuals with cognitive impairment, attention deficit, elderly people, children, language learners), and accessibility and availability considerations. The review of existing studies covering automatic text simplification tools is undergone by searching two databases: Web of Science and Scopus. The eligibility criteria involve text simplification tools with a scientific background in order to ascertain how they operate. This methodology yielded 27 text simplification tools that are further analysed. Some of the main conclusions reached with this review are the lack of resources accessible to the public, the need for customisation to foster the individual’s independence by allowing the user to select what s/he finds challenging to understand while not limiting the user’s capabilities and the need for more simplification tools in languages other than English, to mention a few.

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Automatic Text Simplification for People with Cognitive Disabilities: Resource Creation within the ClearText Project
Isabel Espinosa-Zaragoza | José Abreu-Salas | Paloma Moreda | Manuel Palomar
Proceedings of the Second Workshop on Text Simplification, Accessibility and Readability

This paper presents the ongoing work conducted within the ClearText project, specifically focusing on the resource creation for the simplification of Spanish for people with cognitive disabilities. These resources include the CLEARSIM corpus and the Simple.Text tool. On the one hand, a description of the corpus compilation process with the help of APSA is detailed along with information regarding whether these texts are bronze, silver or gold standard simplification versions from the original text. The goal to reach is 18,000 texts in total by the end of the project. On the other hand, we aim to explore Large Language Models (LLMs) in a sequence-to-sequence setup for text simplification at the document level. Therefore, the tool’s objectives, technical aspects, and the preliminary results derived from early experimentation are also presented. The initial results are subject to improvement, given that experimentation is in a very preliminary stage. Despite showcasing flaws inherent to generative models (e.g. hallucinations, repetitive text), we examine the resolutions (or lack thereof) of complex linguistic phenomena that can be learned from the corpus. These issues will be addressed throughout the remainder of this project. The expected positive results from this project that will impact society are three-fold in nature: scientific-technical, social, and economic.


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A Domain and Language Independent Named Entity Classification Approach Based on Profiles and Local Information
Isabel Moreno | María Teresa Romá-Ferri | Paloma Moreda Pozo
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

This paper presents a Named Entity Classification system, which employs machine learning. Our methodology employs local entity information and profiles as feature set. All features are generated in an unsupervised manner. It is tested on two different data sets: (i) DrugSemantics Spanish corpus (Overall F1 = 74.92), whose results are in-line with the state of the art without employing external domain-specific resources. And, (ii) English CONLL2003 dataset (Overall F1 = 81.40), although our results are lower than previous work, these are reached without external knowledge or complex linguistic analysis. Last, using the same configuration for the two corpora, the difference of overall F1 is only 6.48 points (DrugSemantics = 74.92 versus CoNLL2003 = 81.40). Thus, this result supports our hypothesis that our approach is language and domain independent and does not require any external knowledge or complex linguistic analysis.


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Pattern Construction for Extracting Domain Terminology
Yusney Marrero García | Paloma Moreda Pozo | Rafael Muñoz-Guillena
Proceedings of the International Conference Recent Advances in Natural Language Processing


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Mining Lexical Variants from Microblogs: An Unsupervised Multilingual Approach
Alejandro Mosquera | Paloma Moreda Pozo
Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM)


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Improving Web 2.0 Opinion Mining Systems Using Text Normalisation Techniques
Alejandro Mosquera | Paloma Moreda Pozo
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013


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The Use of Metrics for Measuring Informality Levels in Web 2.0 Texts
Alejandro Mosquera | Paloma Moreda
Proceedings of the 8th Brazilian Symposium in Information and Human Language Technology