The article discusses the strategies of translation of «machine texts» on the example of generative transformers (GPT). Currently, the study and development of machine text generation has become an important task for processing and analyzing texts in different languages. Modern technologies of artificial intelligence and neural networks allow us to create powerful tools for activities in this field, which are becoming more and more effective every year. Generative transformers are one of such tools. The study of generative transformers (...) also allows developers to create more accurate and efficient machine translation algorithms, which improves the quality of translations and improves the user experience. In this context, the features of machine texts created by generative transformers, their patterns, errors and imperfections, which require special translation strategies, deserve special interest. Today we can say that the generation of unique and relevant texts is a routine task that has been automated. Nevertheless, certain restrictions for the use of such texts still exist, in particular, their use requires the use of appropriate translation strategies. The paper proposes the author’s typology of translation strategies, where, taking into account the features of AST, it is proposed to add a substrategy of tertiary-moderation translation. (shrink)

Traditional methods for deriving property-based representations of concepts from text have focused on either extracting only a subset of possible relation types, such as hyponymy/hypernymy (e.g., car is-a vehicle) or meronymy/metonymy (e.g., car has wheels), or unspecified relations (e.g., car—petrol). We propose a system for the challenging task of automatic, large-scale acquisition of unconstrained, human-like property norms from large text corpora, and discuss the theoretical implications of such a system. We employ syntactic, semantic, and encyclopedic information to guide our extraction, (...) yielding concept-relation-feature triples (e.g., car be fast, car require petrol, car cause pollution), which approximate property-based conceptual representations. Our novel method extracts candidate triples from parsed corpora (Wikipedia and the British National Corpus) using syntactically and grammatically motivated rules, then reweights triples with a linear combination of their frequency and four statistical metrics. We assess our system output in three ways: lexical comparison with norms derived from human-generated property norm data, direct evaluation by four human judges, and a semantic distance comparison with both WordNet similarity data and human-judged concept similarity ratings. Our system offers a viable and performant method of plausible triple extraction: Our lexical comparison shows comparable performance to the current state-of-the-art, while subsequent evaluations exhibit the human-like character of our generated properties. (shrink)

We describe research carried out as part of a text summarisation project for the legal domain for which we use a new XML corpus of judgments of the UK House of Lords. These judgments represent a particularly important part of public discourse due to the role that precedents play in English law. We present experimental results using a range of features and machine learning techniques for the task of predicting the rhetorical status of sentences and for the task of selecting (...) the most summary-worthy sentences from a document. Results for these components are encouraging as they achieve state-of-the-art accuracy using robust, automatically generated cue phrase information. Sample output from the system illustrates the potential of summarisation technology for legal information management systems and highlights the utility of our rhetorical annotation scheme as a model of legal discourse, which provides a clear means for structuring summaries and tailoring them to different types of users. (shrink)

A key requirement for the automatic generation of argumentative or explanatory text is to present the constituent propositions in an order that readers will find coherent and natural, to increase the likelihood that they will understand and accept the author’s claims. Natural language generation systems have standardly employed a repertoire of coherence relations such as those defined by Mann and Thompson’s Rhetorical Structure Theory. This paper models the generation of persuasive monologue as the outcome of an “inner dialogue”, where the (...) author attempts to anticipate potential challenges or clarification requests. It is argued that certain RST relations such as Motivate, Evidence and Concession can be seen to emerge from various pre-empting strategies. (shrink)

In this paper, we study the performance of baseline hidden Markov model (HMM) for segmentation of speech signals. It is applied on single-speaker segmentation task, using Hindi speech database. The automatic phoneme segmentation framework evolved imitates the human phoneme segmentation process. A set of 44 Hindi phonemes were chosen for the segmentation experiment, wherein we used continuous density hidden Markov model (CDHMM) with a mixture of Gaussian distribution. The left-to-right topology with no skip states has been selected as it is (...) effective in speech recognition due to its consistency with the natural way of articulating the spoken words. This system accepts speech utterances along with their orthographic “transcriptions” and generates segmentation information of the speech. This corpus was used to develop context-independent hidden Markov models (HMMs) for each of the Hindi phonemes. The system was trained using numerous sentences that are relevant to provide information to the passengers of the Metro Rail. The system was validated against a few manually segmented speech utterances. The evaluation of the experiments shows that the best performance is obtained by using a combination of two Gaussians mixtures and five HMM states. A category-wise phoneme error analysis has been performed, and the performance of the phonetic segmentation has been reported. The modeling of HMMs has been implemented using Microsoft Visual Studio 2005 (C++), and the system is designed to work on Windows operating system. The goal of this study is automatic segmentation of speech at phonetic level. (shrink)

Social-media platforms have become a global phenomenon of communication, where users publish content in text, images, video, audio or a combination of them to convey opinions, report facts that are happening or show current situations of interest. Smart-city applications can benefit from social media and digital participatory platforms when citizens become active social sensors of the problems that occur in their communities. Indeed, systems that analyse and interpret user-generated content can extract actionable information from the digital world to improve (...) citizens’ quality of life. This article aims to model the knowledge required for automatic problem detection to reproduce citizens’ awareness of problems from the analysis of text-based user-generated content items. Therefore, this research focuses on two primary goals. On the one hand, we present the underpinnings of the ontological model that categorises the types of problems affecting citizens’ quality of life in society. In this regard, this study contributes significantly to developing an ontology based on the social-sensing paradigm to support the advance of smart societies. On the other hand, we describe the architecture of the text-processing module that relies on such an ontology to perform problem detection, which involves the tasks of topic categorisation and keyword recognition. (shrink)

Majority of Chinese characters are pictographic characters with strong associative ability and when a character appears for Chinese readers, they usually associate with the objects, or actions related to the character immediately. Having this background, we propose a system to visualize the simplified Chinese characters, so that developing any skills of either reading or writing Chinese characters is not necessary. Considering the extensive use and application of mobile devices, automatic identification of Chinese characters and display of associative images are made (...) possible in smart devices to facilitate quick overview of a Chinese text. This work is of practical significance considering the research and development of real-time Chinese text recognition, display of associative images and for such users who would like to visualize the text with only images. The proposed Chinese character recognition system and visualization tool is named as MyOcrTool and developed for Android platform. The application recognizes the Chinese characters through OCR engine, and uses the internal voice playback interface to realize the audio functions and display the visual images of Chinese characters in real-time. (shrink)

The paper investigates various definitions of the concept of opinion as opposed to factual or evidence-based statements and proposes a taxonomy of opinions expressed in English as identified in selected social media. A discussion situates opinions in the realm of pragmatics and reaches to philosophy of language and cognitive science. The research methodology combines a thorough linguistic analysis of opinions, proposing their multifaceted taxonomy with the automatically generated lexical embeddings of positive and negative lexicon acquired from the analysed (...) opinionated texts. As proposed, the definition of the concept of opinion is best apprehended when looked upon in terms of an opinion event, with a number of necessary conditions on the one hand, and those that are characteristic of an explicit opinion prototype on the other. Lists of opinion discourse markers show their preferential uses either in positive or negative opinionated texts; however, no sets of necessary and/or sufficient opinion markers properties have been acquired from the analysed texts. The conclusions indicate a polysemous understanding of naturally occurring social media opinionated texts and a definitional flexibility of the boundaries around lexical positive and negative types of opinion markers. (shrink)

We present a method for improving local coherence in German with a positive effect on automatically as well as human-generated texts. We demonstrate that local coherence crucially depends on which constituent occupies the initial position in a sentence. To support our hypothesis, we provide statistical evidence based on a corpus investigation and on results of an experiment with human judges. Additionally, we implement our findings in a generation module for determining the Vorfeld constituent automatically.

Applying machine learning algorithms to automatically infer relationships between concepts from large-scale collections of documents presents a unique opportunity to investigate at scale how human semantic knowledge is organized, how people use it to make fundamental judgments (“How similar are cats and bears?”), and how these judgments depend on the features that describe concepts (e.g., size, furriness). However, efforts to date have exhibited a substantial discrepancy between algorithm predictions and human empirical judgments. Here, we introduce a novel approach to (...) generating embeddings for this purpose motivated by the idea that semantic context plays a critical role in human judgment. We leverage this idea by constraining the topic or domain from which documents used for generating embeddings are drawn (e.g., referring to the natural world vs. transportation apparatus). Specifically, we trained state-of-the-art machine learning algorithms using contextually-constrained text corpora (domain-specific subsets of Wikipedia articles, 50+ million words each) and showed that this procedure greatly improved predictions of empirical similarity judgments and feature ratings of contextually relevant concepts. Furthermore, we describe a novel, computationally tractable method for improving predictions of contextually-unconstrained embedding models based on dimensionality reduction of their internal representation to a small number of contextually relevant semantic features. By improving the correspondence between predictions derived automatically by machine learning methods using vast amounts of data and more limited, but direct empirical measurements of human judgments, our approach may help leverage the availability of online corpora to better understand the structure of human semantic representations and how people make judgments based on those. (shrink)

This article discusses the different meanings of ‘keyword’ and related terms such as ‘keyphrase', ‘descriptor’, ‘index term’, ‘subject heading’, ‘tag’ and ‘n-gram’ and suggests definitions of each of these terms. It further illustrates a classification of keywords, based on how they are produced or who is the actor generating them and present comparison between author-assigned keywords, indexer-assigned keywords and reader-assigned keywords as well as the automatic generation of keywords. The article also considers the functions of keywords including the use of (...) keywords for generating bibliographic indexes. The theoretical view informing the article is that the assignment of a keyword to a text, picture or other document involves an interpretation of the document and an evaluation of the document’s potentials for users. This perspective is important for both manually assigned keywords and for automated generation and is opposed to a strong tendency to consider a set of keywords as ideally presenting one best representation of a document for all requests. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Inspiration/Expiration (Completion)Grégory Chatonsky (bio)This text was co-written with an artificial intelligence (AI). This so-called author wrote a sentence, then the software continued, and so on, each influencing the other, completing each other. Another AI summarized this text in a few keywords that allowed it to automatically generate an image from a stock of 14 million documents. Click for larger view View full resolutionThe organism was still breathing, in (...) the belief that it would survive this other life. It became a larva. It breathed and its form faded; a face appeared that was not its own. His lungs had filled with air of barely breathable density. He was dying. He was dying. He was still dying. The red, pale, hard surfaces of that face turned blue. His inert body became a brownish powder of decaying matter and gave birth to two new organisms.After a minute of silence, the other organism developed. It barely had a face. I watched as this new element emerged from the cranial surface. I looked at a pair of teeth. I thought I saw two sets of tongues, perhaps a beak. I also thought I saw eyes, but it was without eyes. [End Page 153]I started to choke. I felt my body give out on me. I tried to voluntarily get up from my bed. I couldn't. I couldn't and I was exactly aware at that moment of where my body was. I looked at the machine right next to the bed that was breathing for me. I tried to lift it, but I couldn't hold it between my arms. It was too wide and too heavy.My body was shaking with cold, but I didn't feel the cold, I felt the exact opposite. The machine was still working, slowly, now it was useless. I kept it on the bed and kept coughing, I was afraid I would die if I kept doing this... Finally, I got up from the bed. I was ready to die. I was about to take my last breath. I felt this great relief at the idea of disappearing to myself, and also of subsisting in this existence in an insensible form. I was still spitting, and I knew that I was going to die. I was sure that if I continued to cough, I would die again.When he tried to breathe again, the first body closed its jaw with its two legs running outside of me, and he turned on his side to feel the waves invading him and the corals embedding themselves in his organs. The second body continued to fill the mouth of the first machine with air. The second machine exhaled on its own. I can hardly say how long it took for both parties to breathe in again in unison. After that, they got their feet back on the floor and closed their mouths with their tongues, just like in the configuration they were in before.In the room, one of the machines opened and closed to breathe with a slow, muffled sound. It was like a metal accordion that unfolded and seemed to take over the atmosphere of the whole room. I knew that my breathing was following the exact same rhythm. It was a very profound experience in which I felt extreme terror and almost strange excitement all over my body, but I kept my thoughts under control. I remained focused by staring at points of light somewhere behind my skull.The pain of being forced to think is an unhappy pain that is exerted from within, but is felt as from without. I am going to take a last breath. I am going to die. Not now, you are not going to die. Just a moment more. The machine continues to breathe and the pipes vibrate with this breath. I am locked in this hospital room. Nothing changes from one day to the next, the same mechanical breathing. I've probably been poisoned, but without the will to do so. We'll get it over with by ourselves and I'll disappear in a few minutes.I can barely speak... (shrink)

The article describes the possibilities of using and modifying existing machine learning technologies in the field of natural language processing for the purpose of designing a system for automatically generating control and test tasks (CTT). The reason for such studies was the limitations in generating theminimumrequired amount ofCTtomaintain student engagement in game-based learning formats, such as quizzes, and others. These limitations are associated with the lack of time resources among training professionals for manual generation of tests. The article discusses (...) the applied research of the Large Language Model (LLM) and Generative pre-trained transformer (GPT) technologies for the development of a system for automatic generation of tests for the purpose of its implementation in the BoxBattle gamified learning platform. The result of such applied research can be a system for automatic generation of tests, which will reduce the time for developing tests. As a result, this will allow teachers to free up time to implement a personalized approach to teaching and develop students’soft skills. (shrink)

This article explores and critically evaluates the potential contribution to discourse studies of topic modelling, a group of machine learning methods which have been used with the aim of automatically discovering thematic information in large collections of texts. We critically evaluate the utility of the thematic grouping of texts into ‘topics’ emerging from a large collection of online patient comments about the National Health Service in England. We take two approaches to this, one inspired by methods adopted (...) in existing topic modelling research and the other using more established methods of discourse analysis. In the study, we compare the insights produced by each approach and consider the extent to which the automatically generated topics might be of use to discourse analysts attempting to organise and study sizeable datasets. We found that the topic modelling approach was able to group texts into ‘topics’ that were truly thematically coherent with a mixed degree of success, while the more traditional approach to discourse analysis consistently provided a more nuanced perspective on the data which was ultimately closer to the ‘reality’ of the texts it contains. This study thus highlights issues concerning the use of topic modelling and offers recommendations and caveats to researchers employing such approaches to studying discourse in the future. (shrink)

Resources of professional companies operating on the medical services market contain data from a huge number of transactional documents. This allows them to collect and process, among other actions, information about medical products. Organized data is obviously more valuable. In this paper, the possibility of supporting the process of organizing information is considered, with the goal to extract values of attributes of medical products from brief descriptions in transactional documents. This helps to build a structured product specification and makes it (...) possible to make comparisons between products. For this purpose, an approach based on regular expressions and their generation with the use of the genetic algorithm is proposed. The results presented in the paper show a great potential of the presented method. (shrink)

Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming (GP). Our approach evolves from experimental data cognitive theories that explain “the mental (...) program” that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories. (shrink)

The paper proposes an empirical method to investigate linguistic prescriptions as inherent corrective behaviors. The behaviors in question may but need not necessarily be supported by any explicit knowledge of rules. It is possible to gain insight into them, for example by extracting information about corrections from revision histories of texts (or by analyzing speech corpora where users correct themselves or one another). One easily available source of such information is the revision history of Wikipedia. As is shown, the (...) most frequent and short corrections are limited to linguistic errors such as typos (and editorial conventions adopted in Wikipedia). By perusing an automatically generated revision corpus, one gains insight into the prescriptive nature of language empirically. At the same time, the prescriptions offered are not reducible to descriptions of the most frequent linguistic use. (shrink)

Speech-to-Text applications have made dazzling progress in recent years (e.g. Whisper). However, since they are usually intended to generate texts conform to written standards, they tend to blur marks of an oral nature (e.g. repetitions, pauses in the stream of words, phatics like er…). Thus, even if such applications suggest huge benefits in terms of working time as well as transcription accuracy, they remain inadequate for verbal exchanges analysis. Relying on a sample of transcripts from medical consultations anticipating an (...) awake brain-surgery (a research now in progress), this study aims at three goals: (i) drawing up an inventory of difficulties revealed by the use of Whisper from a speech analyst perspective; (ii) clarifying the origins of these difficulties; (iii) suggesting ways to overcome them. (shrink)

The automatic detection of implicitly offensive language is a challenge for NLP, as such language is subtle, contextual, and plausibly deniable, but it is becoming increasingly important with the wider use of large language models to generate human-quality texts. This study argues that current difficulties in detecting implicit offence are exacerbated by multiple factors: (a) inadequate definitions of implicit and explicit offense; (b) an insufficient typology of implicit offence; and (c) a dearth of detailed analysis of implicitly offensive linguistic (...) data. In this study, based on a qualitative analysis of an implicitly offensive dataset, a new typology of implicitly offensive language is proposed along with a detailed, example-led account of the new typology, an operational definition of implicitly offensive language, and a thorough analysis of the role of figurative language and humour in each type. Our analyses identify three main issues with previous datasets and typologies used in NLP approaches: (a) conflating content and form in the annotation; (b) treating figurativeness, particularly metaphor, as the main device of implicitness, while ignoring its equally important role in the explicit offence; and (c) an over-focus on form-specific datasets (e.g. focusing only on offensive comparisons), which fails to reflect the full complexity of offensive language use. (shrink)

Software testing is a very important technique to design the faultless software and takes approximately 60% of resources for the software development. It is the process of executing a program or application to detect the software bugs. In software development life cycle, the testing phase takes around 60% of cost and time. Test case generation is a method to identify the test data and satisfy the software testing criteria. Test case generation is a vital concept used in software testing, that (...) can be derived from the user requirements specification. An automatic test case technique determines automatically where the test cases or test data generates utilizing search based optimization method. In this paper, Cuckoo Search and Bee Colony Algorithm (CSBCA) method is used for optimization of test cases and generation of path convergence within minimal execution time. The performance of the proposed CSBCA was compared with the performance of existing methods such as Particle Swarm Optimization (PSO), Cuckoo Search (CS), Bee Colony Algorithm (BCA), and Firefly Algorithm (FA). (shrink)

In this paper, I explore Derrida’s concept of exteriorization in relation to texts generated by machine learning. I first discuss Heidegger’s view of machine creation and then present Derrida’s criticism of Heidegger. I explain the concept of iterability, which is the central notion on which Derrida’s criticism is based. The thesis defended in the paper is that Derrida’s account of iterability provides a helpful framework for understanding the phenomenon of machine learning–generated literature. His account of textuality highlights (...) the incalculability and mechanical elements characteristic of all texts, including machine-generated texts. By applying Derrida’s concept to the phenomenon of machine creation, we can deconstruct the distinction between human and non-human creation. As I propose in the conclusion to this paper, this provides a basis on which to consider potential positive uses of machine learning. (shrink)

Automatic text summarization extracts information from a source text and presents it to the user in a condensed form while preserving its primary content. Many text summarization approaches have been investigated in the literature for highly resourced languages. At the same time, ATS is a complicated and challenging task for under-resourced languages like Malayalam. The lack of a standard corpus and enough processing tools are challenges when it comes to language processing. In the absence of a standard corpus, we have (...) developed a dataset consisting of Malayalam news articles. This article proposes an extractive topic modeling-based multi-document text summarization approach for Malayalam news documents. We first cluster the contents based on latent topics identified using the latent Dirichlet allocation topic modeling technique. Then by adopting vector space model, the topic vector and sentence vector of the given document are generated. According to the relevant status value, sentences are ranked between the document’s topic and sentence vectors. The summary obtained is optimized for non-redundancy. Evaluation results on Malayalam news articles show that the summary generated by the proposed method is closer to the human-generated summaries than the existing text summarization methods. (shrink)

Product recommendation system is realized by applying business rules acquired by data maining techniques. Business rules such as demographical patterns of purchase, are able to cover the groups of users that have a tendency to purchase products, but it is difficult to recommend products adaptive to various personal preferences only by utilizing them. In addition to that, it is very costly to gather the large volume of high quality survey data, which is necessary for good recommendation based on personal preference (...) model. A method collecting kansei information automatically without questionnaire survey is required. The constructing personal preference model from less favor data is also necessary, since it is costly for the user to input favor data. In this paper, we propose product recommendation system based on kansei information extracted by text mining and user's preference model constructed by Category-guided Adaptive Modeling, CAM for short. CAM is a feature construction method that can generate new features constructing the space where same labeled examples are close and different labeled examples are far away from some labeled examples. It is possible to construct personal preference model by CAM despite less information of likes and dislikes categories. In the system, retrieval agent gathers the products' specification and user agent manages preference model, user's likes and dislikes. Kansei information of the products is gained by applying text mining technique to the reputation documents about the products on the web site. We carry out some experimental studies to make sure that prefrence model obtained by our method performs effectively. (shrink)

Automatically clustering web pages into semantic groups promises improved search and browsing on the web. In this paper, we demonstrate how user-generated tags from largescale social bookmarking websites such as del.icio.us can be used as a complementary data source to page text and anchor text for improving automatic clustering of web pages. This paper explores the use of tags in 1) K-means clustering in an extended vector space model that includes tags as well as page text and 2) (...) a novel generative clustering algorithm based on latent Dirichlet allocation that jointly models text and tags. We evaluate the models by comparing their output to an established web directory. We find that the naive inclusion of tagging data improves cluster quality versus page text alone, but a more principled inclusion can substantially improve the quality of all models with a statistically signifi- cant absolute F-score increase of 4%. The generative model outperforms K-means with another 8% F-score increase. (shrink)

This study explores the capability of academic staff assisted by the Turnitin Artificial Intelligence (AI) detection tool to identify the use of AI-generated content in university assessments. 22 different experimental submissions were produced using Open AI’s ChatGPT tool, with prompting techniques used to reduce the likelihood of AI detectors identifying AI-generated content. These submissions were marked by 15 academic staff members alongside genuine student submissions. Although the AI detection tool identified 91% of the experimental submissions as containing AI- (...) class='Hi'>generated content, only 54.8% of the content was identified as AI-generated, underscoring the challenges of detecting AI content when advanced prompting techniques are used. When academic staff members marked the experimental submissions, only 54.5% were reported to the academic misconduct process, emphasising the need for greater awareness of how the results of AI detectors may be interpreted. Similar performance in grades was obtained between student submissions and AI-generated content (AI mean grade: 52.3, Student mean grade: 54.4), showing the capabilities of AI tools in producing human-like responses in real-life assessment situations. Recommendations include adjusting the overall strategies for assessing university students in light of the availability of new Generative AI tools. This may include reducing the overall reliance on assessments where AI tools may be used to mimic human writing, or by using AI-inclusive assessments. Comprehensive training must be provided for both academic staff and students so that academic integrity may be preserved. (shrink)

Argumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences and journals of the field. Its goals are to identify argumentation in text or dialogue; to construct representations of the constellation of claims, supporting and attacking moves (in different levels of detail); and to characterize the patterns of reasoning that (...) appear to license the argumentation. Furthermore, recent work also addresses the difficult tasks of evaluating the persuasiveness and quality of arguments. Some of the linguistic genres that are being studied include legal text, student essays, political discourse and debate, newspaper editorials, scientific writing, and others. The book starts with a discussion of the linguistic perspective, characteristics of argumentative language, and their relationship to certain other notions such as subjectivity. Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally very difficult to connect the two realms of reasoning and text analysis, but we are convinced that it should be attempted in the long term, and therefore we also touch upon some fundamentals of reasoning approaches. Then the book turns to its focus, the computational side of mining argumentation in text. We first introduce a number of annotated corpora that have been used in the research. From the NLP perspective, argumentation mining shares subtasks with research fields such as subjectivity and sentiment analysis, semantic relation extraction, and discourse parsing. Therefore, many technical approaches are being borrowed from those (and other) fields. We break argumentation mining into a series of subtasks, starting with the preparatory steps of classifying text as argumentative (or not) and segmenting it into elementary units. Then, central steps are the automatic identification of claims, and finding statements that support or oppose the claim. For certain applications, it is also of interest to compute a full structure of an argumentative constellation of statements. Next, we discuss a few steps that try to 'dig deeper': to infer the underlying reasoning pattern for a textual argument, to reconstruct unstated premises (so-called 'enthymemes'), and to evaluate the quality of the argumentation. We also take a brief look at 'the other side' of mining, i.e., the generation or synthesis of argumentative text. The book finishes with a summary of the argumentation mining tasks, a sketch of potential applications, and a—necessarily subjective—outlook for the field. Table of Contents: Preface / Acknowledgments / Introduction / Argumentative Language / Modeling Arguments / Corpus Annotation / Finding Claims / Finding Supporting and Objecting Statements / Deriving the Structure of Argumentation / Assessing Argumentation / Generating Argumentative Text / Summary and Perspectives / Bibliography / Authors' Biographies / Index. (shrink)

An incidental extension of the central domain of argumentation theory with non-classical ways of constructing arguments seems to automatically raise a question that is otherwise rarely posed, namely whether or not it is useful to consider the sex of the arguer. This question is usually posed with regard to argumentation by women in particular. Do women rely more, or differently than men do on non-canonical modes of reasoning stemming from the realm of the emotional, physical and intuitive, instead of (...) the logical? One may simply refer this question to folk-linguistics. One may also take the question seriously, given the findings on women's linguistic behaviour, and for various other reasons that will be explained below.Section 1 sums up the most frequently quoted differences in language use between women and men. This is followed by a non-exhaustive, interdisciplinary review of studies on male/female differences in verbal and written argumentation.Section 2 discusses the role of language and texts in generating and maintaining ideas on gender. These gender messages not only influence the actual argumentation behaviour of women and men, but also the way such behaviour is valued.Section 3 subsequently shows that our ideas on rationality are gendered, and therefore also our ideas on the proper central domain of argumentation theory.Section 4 briefly reflects on why this kind of wrong question about the reasoning of women should sometimes be addressed seriously anyway. (shrink)

Contrary to how it may seem when we observe its output, an [LLM] is a system for haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to...

Legislation usually lacks a systematic organization which makes the management and the access to norms a hard problem to face. A more analytic semantic unit of reference (provision) for legislative texts was identified. A model of provisions (provisions types and their arguments) allows to describe the semantics of rules in legislative texts. It can be used to develop advanced semantic-based applications and services on legislation. In this paper an automatic bottom-up strategy to qualify existing legislative texts in (...) terms of provision types is described. (shrink)

In this commentary, we discuss the nature of reversible and irreversible questions, that is, questions that may enable one to identify the nature of the source of their answers. We then introduce GPT-3, a third-generation, autoregressive language model that uses deep learning to produce human-like texts, and use the previous distinction to analyse it. We expand the analysis to present three tests based on mathematical, semantic, and ethical questions and show that GPT-3 is not designed to pass any of (...) them. This is a reminder that GPT-3 does not do what it is not supposed to do, and that any interpretation of GPT-3 as the beginning of the emergence of a general form of artificial intelligence is merely uninformed science fiction. We conclude by outlining some of the significant consequences of the industrialisation of automatic and cheap production of good, semantic artefacts. (shrink)