Computational linguistics and discourse complexology: Paradigms and research methods

The study presents a scientometric analysis of the development of Malay linguistics research over the past three decades. Due to rapid technological changes in linguistics and policy needs, this study examines publication trends, institution and country contributions, themes, and co-citation cluster networks to provide an overview of the field's intellectual structure. The study utilises CiteSpace version 6.3. R3 Advance to analyse records from the Scopus database (1994-2024). The findings indicate substantial growth in publication output, with Malaysia publishing the most papers, followed by Indonesia, Australia, Singapore, and Brunei Darussalam. Universiti Kebangsaan Malaysia, Universiti Sains Malaysia, Universiti Malaya, Universiti Putra Malaysia, Universiti Teknologi Mara, and International Islamic University of Malaysia are the top contributing institutions to research in Malay linguistics. The emerging themes indicate a shift in research focus from traditional linguistic studies toward computational linguistics, digital text processing, and AI-driven applications. The key research clusters were identified, spanning dialectology, historical linguistics, sociolinguistics, and computational linguistics. Among influential contributors, Asmah Haji Omar, with a citation count of 55 and burstness of 4.08, is the most influential author, significantly shaping the discipline. The GEMA Online Journal of Language Studies has the highest citation bursts (1.81), which are crucial in advancing research in computational linguistics, sociolinguistics, and language identity. This study provides critical insights into the evolution of Malay linguistics research, offering valuable evidence to guide researchers and education policymakers in formulating strategic policies for academic innovation and disciplinary advancement.

William C. Mann passed away on August 13, 2004, after a long struggle with leukemia. He is known to readers of Computational Linguistics as a keen supporter and past president (1987) of ACL, a pioneer in the development of text generation as a field of research in computational linguistics, the originator of rhetorical structure theory (RST) and dialogue game theory (DGT), and the developer (together with David Weber) of dialect adaptation as a technique within machine translation. And he’ll be remembered for these and many other contributions, but above all, in this context, he’ll be remembered as a unique visionary in computational linguistics (CL) and as a truly wonderful and extraordinarily generous colleague. Bill Mann made huge contributions to many people’s intellectual development, to their careers, and to their lives in general. What follows are my own recollections of him, selected from a crowded jungle of memories. I will leave it to others to fill out the picture, but I shall attempt to give an indication of the intellectual and historical context in which Bill was working and also of the network of researchers that he created; his ideas have radiated throughout this network of scholars around the world. I met Bill in April 1980 and worked as a research linguist on a succession of projects he directed, or codirected, at the Information Sciences Institute (ISI) at the University of Southern California (USC), until I left the institute to take up a position at the University of Sydney in August 1988. Through that period (which happened to coincide, more or less, with the Reagan years), not only was Bill a very wise and wonderful project leader, but he also became a mentor and guide and a true friend. I followed from afar the further developments in his life: his departure from ISI and move to Kenya with his family to take up a position with the Summer Institute of Linguistics (SIL) as a consultant responsible for work on discourse and computational matters, their return to the United States, and his new phase of research with initiatives in RST and DGT.

Shlomo Argamon is Professor of Computer Science and Director of the Master of Data Science Program at the Illinois Institute of Technology (USA). In this article, he reflects on the current and potential relationship between register and the field of computational linguistics. He applies his expertise in computational linguistics and machine learning to a variety of problems in natural language processing. These include stylistic variation, forensic linguistics, authorship attribution, and biomedical informatics. He is particularly interested in the linguistic structures used by speakers and writers, including linguistic choices that are influenced by social variables such as age, gender, and register, as well as linguistic choices that are unique or distinctive to the style of individual authors. Argamon has been a pioneer in computational linguistics and NLP research in his efforts to account for and explore register variation. His computational linguistic research on register draws inspiration from Systemic Functional Linguistics, Biber’s multi-dimensional approach to register variation, as well as his own extensive experience accounting for variation within and across text types and authors. Argamon has applied computational methods to text classification and description across registers – including blogs, academic disciplines, and news writing – as well as the interaction between register and other social variables, such as age and gender. His cutting-edge research in these areas is certain to have a lasting impact on the future of computational linguistics and NLP.

Given that context-free grammars (CFG) cannot adequately describe natural languages, grammar formalisms beyond CFG that are still computationally tractable are of central interest for computational linguists. This book provides an extensive overview of the formal language landscape between CFG and PTIME, moving from Tree Adjoining Grammars to Multiple Context-Free Grammars and then to Range Concatenation Grammars while explaining available parsing techniques for these formalisms. Although familiarity with the basic notions of parsing and formal languages is helpful when reading this book, it is not a strict requirement. The presentation is supported with many illustrations and examples relating to the different formalisms and algorithms, and chapter summaries, problems and solutions. The book will be useful for students and researchers in computational linguistics and in formal language theory.

Categorization is a central capability of human cognition, and a number of theories have been developed to account for properties of categorization. Even though many tasks in semantics also involve categorization of some kind, theories of categorization do not play a major role in contemporary research in computational linguistics. This paper follows the idea that embedding-based models of semantics lend themselves well to being formulated in terms of classical categorization theories. The benefit is a space of model families that enables (a) the formulation of hypotheses about the impact of major design decisions, and (b) a transparent assessment of these decisions. We instantiate this idea on the task of frame-semantic frame identification. We define four models that cross two design variables: (a) the choice of prototype vs. exemplar categorization, corresponding to different degrees of generalization applied to the input; and (b) the presence vs. absence of a fine-tuning step, corresponding to generic vs. task-adaptive categorization. We find that for frame identification, generalization and task-adaptive categorization both yield substantial benefits. Our prototype-based, fine-tuned model, which combines the best choices for these variables, establishes a new state of the art in frame identification.

The PAP900 dataset centers on the semantic relationship between affective words in Portuguese. It contains 900 word pairs, each annotated by at least 30 human raters for both semantic similarity and semantic relatedness. In addition to the semantic ratings, the dataset includes the word categorization used to build the word pairs and detailed sociodemographic information about annotators, enabling the analysis of the influence of personal factors on the perception of semantic relationships. Furthermore, this article describes in detail the dataset construction process, from word selection to agreement metrics.Data was collected from Portuguese university psychology students, who completed two rounds of questionnaires. In the first round annotators were asked to rate word pairs on either semantic similarity or relatedness. The second round switched the relation type for most annotators, with a small percentage being asked to repeat the same relation. The instructions given emphasized the differences between semantic relatedness and semantic similarity, and provided examples of expected ratings of both.There are few semantic relations datasets in Portuguese, and none focusing on affective words. PAP900 is distributed in distinct formats to be easy to use for both researchers just looking for the final averaged values and for researchers looking to take advantage of the individual ratings, the word categorization and the annotator data. This dataset is a valuable resource for researchers in computational linguistics, natural language processing, psychology, and cognitive science.

Much research in computational linguistics and artificial intelligence has gone into characterizing natural language discourse structures, developing discourse models, and using these to develop more useful natural language interfaces to databases and expert systems. However, this knowledge about the nature of discourse has not been used to any advantage in graphical expert system interfaces. This paper proposes that discourse elements and discourse models used in the world of natural language understanding can be extended to graphical expert system interfaces, promoting continuity of interactions and allowing relationships among interactions to be exploited, redundancy reduced, operations tracked, and higher-level operations recognized. fj-^-, V' 1 f, T 1 4^ ^'' Jj-v' *. - fci L

The intrinsic value of Indian traditional knowledge sources in modern sciences is examined by evaluating 2431 publications of the last 200 years culled from the Scopus database. The results showed growing interest among scholars and researchers from about 60 countries who have researched these knowledge sources. Although major publications belong to the fields of humanities and social sciences, researchers in the areas of computer science, engineering, medicine, and environmental science have shown research interests in these domains. A shift of research interest has been noticed as researchers in computational linguistics, semantics, cultural heritage, and sustainable development have identified emerging themes. The analysis of secondary sources showed that publications appeared between 1700 and 1950 on various traditional sources, mostly by non-Indic authors. This research concludes that the fusion of traditional knowledge in fields such as medicine, agriculture, and environmental sciences may be helpful for the study of contemporary problems, achieving sustainable goals, and enhancing both academic inquiry and practical applications. It is urged that the Governments in India should support researchers to work in the areas of social welfare using the Indian traditional knowledge sources.

Common knowledge, also called intersubjectivity, is a core theme in the study of international cooperation and diplomacy. Yet International Relations (IR) lacks a method to systematically measure the degree of common knowledge. Drawing from research in computational linguistics, psychology, and communication, we introduce latent semantic analysis (LSA) to measure common knowledge in specific communicative exchanges between actors. We argue that the extent to which speaking partners use words in the same way and get in synch linguistically can be used to measure the degree of common knowledge, and this can be measured by the LSA method. We outline several ways LSA can be valuable to IR scholars and provide an empirical illustration of using this method in the case of Bretton Woods negotiations. The LSA method promises to help IR scholars seize the research opportunities offered by the digital age and build a bridge between qualitative and quantitative methods.

Distributional semantics provides multidimensional, graded, empirically induced word representations that successfully capture many aspects of meaning in natural languages, as shown by a large body of research in computational linguistics; yet, its impact in theoretical linguistics has so far been limited. This review provides a critical discussion of the literature on distributional semantics, with an emphasis on methods and results that are relevant for theoretical linguistics, in three areas: semantic change, polysemy and composition, and the grammar–semantics interface (specifically, the interface of semantics with syntax and with derivational morphology). The goal of this review is to foster greater cross-fertilization of theoretical and computational approaches to language as a means to advance our collective knowledge of how it works.

In recent years, researchers in computational linguistics have shown a growing interest in the style of text, with a specific focus on the text style transfer (TST) task. While numerous innovative methods have been proposed, it has been observed that the existing evaluations are insufficient to validate the claims and precisely measure the performance. This challenge primarily stems from rapid advancements and diverse settings of these methods, with the associated (re)implementation and reproducibility hurdles. To bridge this gap, we introduce a comprehensive benchmark for TST known as TSTBench. TSTBench includes a codebase encompassing implementations of 13 state-of-the-art algorithms and a standardized protocol for text style transfer. Based on the codebase and protocol, we have conducted thorough experiments across seven datasets, resulting in a total of 7000+ evaluations. Our work provides extensive analysis from various perspectives, explores the performance of representative baselines across various datasets, and offers insights into the task and evaluation processes to guide future research in TST.

Introduction. Donald Walker: a Remembrance. Section 1: The Task of Natural Language Processing. Natural Language Processing: an Historical Review K. Sparck Jones. On Getting a Computer to Listen J. Robinson. Utterance and Objective: Issues in Natural Language Communication B. Grosz. On the Proper Place of Semantics in Machine Translation M. King. Developing a Natural Language Interface to Complex Data G.G. Hendrix, E.D. Sacerdoti. User-Needs Analysis and Design Methodology for an Automated Document Generator K. Kukich, K. McKeown, J. Shaw, J. Robin, J. Lim, N. Morgan, J. Philips. Section 2: Building Computational Lexicons. Machine-Readable Dictionaries and Computational Linguistics Research B. Boguraev. Research Toward the Development of a Lexical Knowledge Base for Natural Language Processing R.A. Amsler. Discovering Relationships Among Word Senses R.J. Byrd. Machine Readable Dictionary as a Source of Grammatical Information E. Hajicova, A. Rosen. The ITT Lexical Database: Dream and Reality S. Pin-Ngern Conlon. Visions of the Digital Library: Views on Using Computational Linguistics and Semantic Nets in Information Retrieval J.L. Klavans. Anatomy of a Verb Entry: from Linguistic Theory to Lexicographic Practice B.T. Atkins, J. Kegl, B. Levin. Issues for Lexicon Building N. Calzolari. Outline of a Model for Lexical Databases N. Ide, J. le Maitre, J. Veronts. Construction-Based MT Lexicons L. Levin, S. Nirenburg. Dependency-Based Grammatical Information in the Lexicon P. Sgall. Semantics in the Brain's Lexicon -- Some Preliminary Remarks on its Epistemology H. Schnelle. Section 3: The Acquisition and Use of LargeCorpora. The Ecology of Language D.E. Walker. Representativeness in Corpus Design D. Biber. The Text Encoding Initiative C.M. Sperberg-McQueen. Discrimination Decisions for 100,000 Dimensional W.A. Gale, K.W. Church, D. Yarowsky. Acquisition and Exploitation of Textual Resources for NLP S. Armstrong-Warwick. The Center for Electronic Texts in the Humanities S. Hockey. Design Principles for Electronic Textual Resources: Investigating Users and Uses of Scholarly Information N.J. Belkin. Section 4: Topics, Methods and Formalisms in Syntax, Semantics and Pragmatics. Evaluating English Sentences in a Logical Model J. Friedman, D.B. Moran, D.S. Warren. Recovering Implicit Information M.S. Palmer, D.A. Dahl, R.J. Schiffman, L. Hirschman, M. Linebarger, J. Downing. Flexible Generation: Taking the User into Account C.L. Paris, V.O. Mittal. Two Principles of Parse Preference J.R. Hobbs, J. Bear. UD, yet Another Unification Device R. Johnson, M. Rosner. Varieties of Heuristics in Sentence Parsing M. Nagao. Some Recent Trends in Natural Language Processing A.K. Joshi. Stone Soup and the French Room Y. Wilks.

Incremental Conceptualization for Language Production discusses the simultaneous actions involved in thinking and speaking, as well as the piecemeal way in which individuals construct an internal representation of the external world and use this internal representation for speaking. Author Markus Guhe presents the first computational model that captures these observations in a cognitively adequate fashion. The volume is an innovative look at the mind’s process of producing semantic representations that can be transformed into language. The first section of the book illustrates four stages of conceptualization: construction of a conceptual representation; selection of content to be verbalized; linearization of the selected content; and generation of preverbal messages. Guhe then analyzes incremental processing — processing that takes place in a piecemeal fashion — and offers a blueprint of incremental models while discussing the dimensions along which the processing principles and the blueprint varies. Finally, incremental processing and conceptualization merge to form the incremental conceptualiser model (inC). The effective use of inC is demonstrated through simulations carried out with the implementation of the model. Intended for researchers in cognitive science, particularly cognitive modeling of language, this volume will also interest researchers in artificial intelligence, computational linguistics, psycholinguistics, and linguistics and psychology.

This book is the first dedicated to linguistic parsing—the processing of natural language according to the rules of a formal grammar—in the minimalist framework. While the Minimalist Program has been at the forefront of generative grammar for several decades, it often remains inaccessible to computer scientists and others in adjacent fields. In particular, minimalism reveals a surprising paradox: human language is simpler than we thought, and yet it cannot be processed by the machinery used by computer scientists. In this volume, experts in the field show how to resolve this apparent paradox, and how to turn Chomsky’s abstract theories into working computer programs that can process sentences or make predictions about the time course of brain activity when dealing with language. The book will appeal to graduate students and researchers in formal syntax, computational linguistics, psycholinguistics, and computer science.

This workshop anticipates that an important class of learning analytic will emerge at the intersection of research into learning dynamics, online discussion platforms, and computational linguistics. Written discourse is arguably the primary class of data that can give us insights into deeper learning and higher order qualities such as critical thinking, argumentation, mastery of complex ideas, empathy, collaboration and interpersonal skills. Moreover, the ability to write in a scholarly manner is a core competence, often taking the form of discourse with oneself and the literature. Computational linguistics research has developed a rich array of tools for machine interpretation of human discourse, but work to develop these tools in the context of learning is at a relatively early stage. Moreover, there is a significant difference between designing tools to assist researchers in discourse analysis, and their deployment on platforms to provide meaningful analytics for the learners and educators who are conducting that discourse. This workshop aims to catalyse ideas and build community connections among those who want to shape this field.