Recently, language models (LMs) have produced excellent results in many natural language processing (NLP) tasks. However, their effectiveness is highly dependent on available pre-training resources, which is particularly challenging for low-resource languages such as Sinhala. Furthermore, the scarcity of benchmarks to evaluate LMs is also a major concern for low-resource languages. In this paper, we address these two challenges for Sinhala by (i) collecting the largest monolingual corpus for Sinhala, (ii) training multiple LMs on this corpus and (iii) compiling the first Sinhala NLP benchmark (Sinhala-GLUE) and evaluating LMs on it. We show the Sinhala LMs trained in this paper outperform the popular multilingual LMs, such as XLM-R and existing Sinhala LMs in downstream NLP tasks. All the trained LMs are publicly available. We also make Sinhala-GLUE publicly available as a public leaderboard, and we hope that it will enable further advancements in developing and evaluating LMs for Sinhala.

Experts in machine learning leverage domain knowledge to navigate decisions in model selection, hyperparameter optimization, and resource allocation.This is particularly critical for fine-tuning language models (LMs), where repeated trials incur substantial computational overhead and environmental impact.However, no existing automated framework simultaneously tackles the entire model selection and hyperparameter optimization (HPO) task for resource-efficient LM fine-tuning.We introduce XAutoLM, a meta-learning-augmented AutoML framework that reuses past experiences to optimize discriminative and generative LM fine-tuning pipelines efficiently.XAutoLM learns from stored successes and failures by extracting task-and system-level meta-features to bias its sampling toward valuable configurations and away from costly dead ends.On four text classification and two question-answering benchmarks, XAutoLM surpasses zero-shot optimizer's peak F 1 on five of six tasks, cuts mean evaluation time of pipelines by up to 4.5x, reduces search error ratios by up to sevenfold, and uncovers up to 50% more pipelines above the zero-shot Pareto front.In contrast, simpler memory-based baselines suffer negative transfer.We release XAutoLM and our experience store to catalyze resource-efficient, Green AI fine-tuning in the NLP community.

Abstract The goal of information extraction is to extract structural knowledge (such as entities, relations and events) from plain and unstructured texts. Information extraction in legal documents has recently gained a lot of attention in the natural language processing (NLP) community due to the high demand for efficient information extraction for legal practitioners and companies. Given that the legal documents are unique and their processing is challenging, there is a pressing need for applications of NLP techniques to tackle these challenges. In this research, we present a survey on the recent advancements in legal information extraction focusing on three tasks: named entity recognition, relationship extraction and event detection. We report language resources and systems in multiple jurisdictions and languages for each task. Based on the thorough review conducted, we identify insights into the techniques employed and promising research directions that merit further exploration in future studies. We maintain a public repository and consistently update related resources at https://github.com/DamithDR/legalinformationextraction .

This article presents an approach to readability estimation that focuses on conceptual rather than linguistic complexity, using the extensive SaudiTextBooks textbooks. We introduce DARES 2.0 , an enhanced concept-based readability training dataset designed to estimate the readability of Saudi educational texts. Building on DARES 1.0, DARES 2.0 extends the scope of conceptual complexity by replacing repetitive concepts and manually revising the input features with unique terms and their surrounding contexts from the SaudiTextBooks, spanning grades 1 to 12. The refined DARES 2.0 is employed to fine-tune pre-trained transformer models, including XLM-R Base, mBERT, AraELECTRA, AraBERTv2, and CAMeLBERTmix. The findings suggest that both the dataset and experimental setup require further development to ensure a larger, higher-quality dataset and to support more extensive fine-tuning experiments, in addition to exploring transfer learning from other languages and enhancing the diversity and richness of Arabic concepts. These developments pave the way for further advancements in concept-based readability estimation in educational contexts in future work.

Definition modelling (DM) is the task of automatically generating a dictionary definition of a specific word. Computational systems that are capable of DM can have numerous applications benefiting a wide range of audiences. As DM is considered a supervised natural language generation problem, these systems require large annotated datasets to train the machine learning (ML) models. Several DM datasets have been released for English and other high-resource languages. While Portuguese is considered a mid/high-resource language in most natural language processing tasks and is spoken by more than 200 million native speakers, there is no DM dataset available for Portuguese. In this research, we fill this gap by introducing DORE; the first dataset for Definition MOdelling for PoRtuguEse containing more than 100,000 definitions. We also evaluate several deep learning based DM models on DORE and report the results. The dataset and the findings of this paper will facilitate research and study of Portuguese in wider contexts.

Extracting biographical information from online documents is a popular research topic among the information extraction (IE) community. Various natural language processing (NLP) techniques such as text classification, text summarisation and relation extraction are commonly used to achieve this. Among these techniques, RE is the most common since it can be directly used to build biographical knowledge graphs. RE is usually framed as a supervised machine learning (ML) problem, where ML models are trained on annotated datasets. However, there are few annotated datasets for RE since the annotation process can be costly and time-consuming. To address this, we developedBiographical, the first semi-supervised dataset for RE. The dataset, which is aimed towards digital humanities (DH) and historical research, is automatically compiled by aligning sentences from Wikipedia articles with matching structured data from sources including Pantheon and Wikidata. By exploiting the structure of Wikipedia articles and robust named entity recognition (NER), we match information with relatively high precision in order to compile annotated relation pairs for ten different relations that are important in the DH domain. Furthermore, we demonstrate the effectiveness of the dataset by training a state-of-the-art neural model to classify relation pairs, and evaluate it on a manually annotated gold standard set.Biographical is primarily aimed at training neural models for RE within the domain of digital humanities and history, but as we discuss at the end of this paper, it can be useful for other purposes as well.

Recent years have seen big advances in the field of sentence-level quality estimation (QE), largely as a result of using neural-based architectures.However, the majority of these methods work only on the language pair they are trained on and need retraining for new language pairs.This process can prove difficult from a technical point of view and is usually computationally expensive.In this paper we propose a simple QE framework based on cross-lingual transformers, and we use it to implement and evaluate two different neural architectures.Our evaluation shows that the proposed methods achieve state-of-the-art results outperforming current open-source quality estimation frameworks when trained on datasets from WMT.In addition, the framework proves very useful in transfer learning settings, especially when dealing with low-resourced languages, allowing us to obtain very competitive results.

Matching and retrieving previously translated segments from the Translation Memory is a key functionality in Translation Memories systems. However this matching and retrieving process is still limited to algorithms based on edit distance which we have identified as a major drawback in Translation Memories systems. In this paper, we introduce sentence encoders to improve matching and retrieving process in Translation Memories systems - an effective and efficient solution to replace edit distance-based algorithms.

When processing a text, humans and machines must disambiguate between different uses of the pronoun it, including non-referential, nominal anaphoric or clause anaphoric ones.In this paper, we use eye-tracking data to learn how humans perform this disambiguation.We use this knowledge to improve the automatic classification of it.We show that by using gaze data and a POS-tagger we are able to significantly outperform a common baseline and classify between three categories of it with an accuracy comparable to that of linguisticbased approaches.In addition, the discriminatory power of specific gaze features informs the way humans process the pronoun, which, to the best of our knowledge, has not been explored using data from a natural reading task.

The Journal of Natural Language Engineering (JNLE) has enjoyed another very successful year. The impact factor of the journal has increased for the third consecutive year, with the journal being listed among both Linguistics and Computer Science categories. Against the background of a record number of submissions, JNLE has, since 2016, been offering six 160-page issues per year, which by far exceeds the four 96-page issues offered less than 10 years ago!

Our passion to continually improve the journal and, as a result, our contribution to the community drives our quest for highly diversified and up-to-date content. We are launching a new category of papers, i.e., ‘keynote’ or ‘invited’ papers, which will be penned by leading lights in the field.

In one of my previous editorial notes I promised that the positive developments of the Journal of Natural Language Engineering (JNLE) would be a continuous and common practice. I am proud to report that I have been able to keep this promise. JNLE has enjoyed another very successful year. The impact factor of the journal increased for the second consecutive year, with the journal listed both among the Linguistics and Computer Science categories. From 2016 onwards, JNLE is offering six 160-page issues per year, which by far exceeds the four 96-page issues from less than 10 years ago!

Recognizing negative and speculative information is highly relevant for sentiment analysis. This paper presents a machine‐learning approach to automatically detect this kind of information in the review domain. The resulting system works in two steps: in the first pass, negation/speculation cues are identified, and in the second phase the full scope of these cues is determined. The system is trained and evaluated on the Simon Fraser University Review corpus, which is extensively used in opinion mining. The results show how the proposed method outstrips the baseline by as much as roughly 20% in the negation cue detection and around 13% in the scope recognition, both in terms of F1. In speculation, the performance obtained in the cue prediction phase is close to that obtained by a human rater carrying out the same task. In the scope detection, the results are also promising and represent a substantial improvement on the baseline (up by roughly 10%). A detailed error analysis is also provided. The extrinsic evaluation shows that the correct identification of cues and scopes is vital for the task of sentiment analysis.

People with Autism Spectrum Disorder (ASD) are known to experience difficulties in reading comprehension, as well as to have unusual attention patterns, which makes the development of user-centred tools for this population a challenging task. This paper presents the first study to use eye-tracking technology with ASD participants in order to evaluate text documents. Its aim is two-fold. First, it evaluates the use of images in texts and provides evidence of a significant difference in the attention patterns of participants with and without autism. Sets of two types of images, photographs and symbols, are compared to establish which ones are more useful to include in simple documents. Second, the study evaluates human-produced easy-read documents, as a gold standard for accessible documents, on 20 adults with autism. The results provide an understanding of the perceived level of difficulty of easy-read documents according to this population, as well as the preferences of autistic individuals in text presentation. The results are synthesized as set of guidelines for creating accessible text for autism.

The Journal of Natural Language Engineering (JNLE) has enjoyed another very successful year. Two years after being accepted into Thomson Reuters Citation Index and being indexed in many of their products (including both the Science and the Social Science editions of the Journals Citation Rankings (JCR)), the journal further established itself as a leading forum for high-quality articles covering all aspects of Natural Language Processing research, including, but not limited to, the engineering of natural language methods and applications. I am delighted to report an increased number of submissions reaching a total of 92 between January–September 2014.

Natural Language Engineering (NLE) has enjoyed another year promoting research of applied Natural Language Processing and serving the research community in the field. We were particularly pleased to register an increasing number of submissions on a wide range of topics reflecting the growing importance of the field. We were also delighted to receive a number of submissions representing the new article types announced in 2010, such as surveys and squibs.

Natural Language Engineering (NLE) enters the second decade of the twenty-first century having established itself as a leading forum for high-quality articles covering all aspects of applied Natural Language Processing research, including, but not limited to, the engineering of natural language methods and applications. It continues to promote first class original research and bridge the gap between traditional computational linguistics research and the implementation of practical applications with potential real-world use. The journal has responded in several ways to the ongoing interest in and growth of research in this area. In 2007 NLE increased its number of pages per issue, thus enabling the publication of more articles. As of January 2010, new publication types are also promoted. In addition to welcoming articles which report on original, unpublished research, the journal now invites surveys presenting the state of the art in important areas of Natural Language Engineering and Natural Language Processing (such as tasks, tools, resources or applications) as well as squibs discussing specific problems. Book reviews and reports on industrial applications will continue to have a prominent place in the Journal. Conference reports, comparative discussions of Natural Language Engineering products and policy-orientated papers examining, for example, funding programmes or market opportunities, are welcome too. Special issues will remain an important feature of the Journal. We envisage one special issue per year, on average. Special issues are selected on a competitive basis after regular calls for proposals.

The paper outlines a system for generating multiple-choice tests on the choice of English verbs. In particular, the selection of candidate sentences and distractors are discussed in detail. The system is an extension of a system which generates language tests for Basque.

I am honoured to address you as the new Executive Editor of the journal, a role I took on recently from Professor John Tait. As someone who, along with the other editors and members of the Editorial Board, has the responsibility for the overall quality of the journal, my main goal is to continue actively pursuing the journal objectives and to raise the standards even higher. These objectives are concerned with promoting applied natural language processing (NLP) research in the form of first-class original research and with bridging the gap between traditional computational linguistics research and the implementation of practical applications with potential real-world use.

This paper discusses the comparative evaluation of five well‐known pronoun resolution algorithms conducted with the help of a purpose‐built tool for consistent evaluation in anaphora resolution, termed the evaluation workbench. The workbench enables the evaluation and comparison of pronoun resolution algorithms on the basis of the same preprocessing tools and test data. The tool is controlled by the user who can conduct the evaluation according to a variety of parameters, with regard to the types of anaphors and the samples used for evaluation. The extensive comparative evaluation of the pronoun resolution algorithms showed that their performance was significantly lower than the figures reported in the original papers describing the algorithms. The evaluation study concluded that the main reason for this drop in performance is the fact that all algorithms operate in a fully automatic mode.

This paper describes a novel computer-aided procedure for generating multiple-choice test items from electronic documents. In addition to employing various Natural Language Processing techniques, including shallow parsing, automatic term extraction, sentence transformation and computing of semantic distance, the system makes use of language resources such as corpora and ontologies. It identifies important concepts in the text and generates questions about these concepts as well as multiple-choice distractors, offering the user the option to post-edit the test items by means of a user-friendly interface. In assisting test developers to produce items in a fast and expedient manner without compromising quality, the tool saves both time and production costs.

The reader learns from this book's introduction (written by Ali Farghaly) that the objective of the book is to "equip linguists embarking on NLP assignments."The introduction also explains why language engineers are needed and summarizes the contents of each chapter."Domain Analysis and Representation" by Farghaly and Bruce Hedin offers a good discussion of the importance of domain analysis in natural language processing (NLP).It provides a helpful background on the notion of sublanguages and correctly notes that distinctions between the different domains can be blurred, as domains often overlap.The chapter would have been more convincing if there were further examples of NLP applications that benefit from narrowing down the domain of their operation.The chapter also discusses the analysis of domain into topics.Section 2.4.4 covers statistical approaches to classification, but no references or further reading pointers are given."The Language of the Internet" by Naomi Baron gives an easy-to-read and useful chronological overview of the developments on the Internet.It provides information about a number of technologies that are used on the Internet, comments on the changing styles of natural language use, and briefly overviews Web markup and programming languages and the Semantic Web.At the same time, I found this chapter too general.It would have been useful if the chapter had covered specific new NLP applications that are relevant to the Internet, such as question answering, and had covered in greater detail low-quality machine translation for e-mail and chat or text categorization of Web pages."Grammar Writing, Testing and Evaluation" by Miriam Butt and Tracy Holloway King provides a very good and accessible historical and linguistic account of grammars and parsing in NLP.It covers deep and shallow parsing and associated techniques as well as testing and evaluation of grammars and parsers.Morphological analyzers and part-of-speech taggers are briefly outlined too.A good practical point is the section on documentation of grammar writing."Ontologies" by Natalya Noy is a concise and plainly written introduction to the topic of ontologies and their development.It clarifies key terms in the ontology development jargon and includes an overview of major ontologies, ontology libraries, and

In this paper we argue that comparative evaluation in anaphora resolution has to be performed using the same pre-processing tools and on the same set of data. The paper proposes an evaluation environment for comparing anaphora resolution algorithms which is illustrated by presenting the results of the comparative evaluation of three methods on the basis of several evaluation measures.

Americanae nace como un proyecto conjunto que surge dentro de la Red Europea de Información y Documentación sobre América Latina (REDIAL), y que ha afrontado la Biblioteca de la Agencia Española de Cooperación Internacional para el Desarrollo (AECID). Esta nueva biblioteca virtual hace más accesibles los libros digitales de tema americanista a los investigadores y usuarios interesados de cualquier parte del mundo.