Natural Language Discourse Research Articles

Flowing Time: Emergentism and Linguistic Diversity

Humans are complex systems, ‘macro-entities’, whose existence, behaviour and consciousness stem out of the configurations of physical entities on the micro-level of the physical world. But an explanation of what humans do and think cannot be found through ‘tracking us back’, so to speak, to micro-particles. So, in explaining human behaviour, including linguistic behaviour on which this paper focuses, emergentism opens up a powerful opportunity to explain what it is exactly that emerged on that level, bearing in mind the end product in the form of the intra- and inter-cultural diversity. Currently there is a gap in emergentism research. On one hand, there are discussions in philosophy of the emergent human reality; on the other, there are discussions of social, cultural, or individual variation of these emergent aspects of humanity in the fields of anthropology, sociology, linguistics or psychology. What I do in this paper is look for a way to ‘trace’ some such diversified emergents from what is universal about their ‘coming to being’, all the way through to their diversification. My chosen emergent is human time, my domain of inquiry is natural-language discourse, and the drive behind this project is to understand the link between ‘real’ time of spacetime on the micro-level from which we emerged and the human time devised by us, paying close attention to the overwhelming diversity in which temporal reference is expressed in human languages. The main question is, where does this diversity fit in? Does understanding of this diversity, as well as of what lurks under the surface of this diversity, aid the emergentism story? My contribution to this volume on ‘the nature of structure and the structure of nature’ thus takes the following take on the title. The structure of human communication is at the same time uniform, universal, and relative to culture, in that it is emergent as a human characteristic, and as such compatible with the micro-level correlates in some essential ways, but also free to fly in different directions that are specific to societies and cultures. I explore here the grey area between the micro-level and the linguistic reflections of time—the middle ground that is emergent itself but that tends to be by-passed by those who approach the question of human flowing time from either end: metaphysics and the philosophy of time on the one hand, and contrastive linguistics, anthropological linguistics and language documentation on the other. I illustrate the debate with examples from tensed and tenseless languages from different language families, entertaining the possibility of a conceptual universal pertaining to time as degrees of epistemic modality. Needless to say, putting the question in this way also sets out my (not unassailable) methodology.

How to Disrupt a Social Script

AbstractSocial scripts, like A gives a compliment, B says ‘thank you’, pervade and shape natural language discourse and social interactions. Scripts usually promote cooperation between conversational participants, but not always. For example, if A pays B a ‘compliment’ like ‘nice legs’, A puts B in a double bind of either abiding by the compliment script by saying ‘thank you’ and being humiliated, or breaking the script and risking escalation. In this paper, I take a philosophical lens to the notion of a social script. I give a theoretical overview of what it would mean to disrupt a social script and explain why and when it is prudential to do so. Then I give several examples of disruptions of social scripts. This essay makes four key contributions to the philosophical literature on social scripts: (1) it introduces a new distinction between interpersonal and structural scripts; (2) it illuminates how interpersonal social scripts can be pernicious by creating a double bind; (3) it analyzes what it is to disrupt a social script; and (4) in doing so, it challenges the orthodoxy about the relationship between cooperation and disruption in political action.

Review: Cognitive Linguistics: Textbook for Universities / S.M. Kolesnikova, E.V. Altabaeva, A.T. Gryaznova; ed. by S.M. Kolesnikova. — Moscow: Yurite, 2022–2023. — 192 p

Modern cognitive linguistics is becoming a basic philological discipline, allowing students to understand the relationship between language and human thinking, to explain the linguistic picture of the world and its role in the formation of social consciousness. The new generation textbook “Cognitive Linguistics” edited by S.M. Kolesnikova reveals the most important concepts and terms of cognitive linguistics and shows the algorithms of analyzing the conceptosphere and its interpretation. The textbook consists of three thematic parts, consistently revealing the theoretical material, giving an idea of the basic directions of cognitive linguistics and algorithms of analyzing the concepts of natural language and artistic discourse. In addition to the theory of the issue, the textbook contains extensive reference material and bibliography, which helps undergraduate, graduate and postgraduate students in writing research papers.

Cognition and hypocognition: Discursive and simulation-supported decision-making within complex systems

Homo sapiens is currently believed to have evolved in the African savannah several hundreds of thousands of years ago. Since then, human societies have become, through technological innovation and application, powerful influencers of the planet’s ecological, hydrological and meteorological systems – for good and ill. They have experimented with many different systems of governance, in order to manage their societies and the environments they inhabit – using computer simulations as a tool to help make decisions concerning highly complex systems, is only the most recent of these. In questioning whether, when and how computer simulations should play a role in determining decision-making in these systems of governance, it is also worth reflecting on whether, when and how humans, or groups of humans, have the capability to make such decisions without the aid of such technology. This paper looks at and compares the characteristics of natural language-based and simulation-based decision-making. We argue that computational tools for decision-making can and should be complementary to natural language discourse approaches, but that this requires that both systems are used with their limitations in mind. All tools and approaches – physical, social and mental – have dangers when used inappropriately, but it seems unlikely humankind can survive without them. The challenge is how to do so.

Visions of deliverance: Social scientization, functionalism, and the expansive purposiveness of state schooling in nineteenth-century British parliamentary politics

AbstractEarly in the nineteenth century, members in the UK Parliament (MPs) hardly ever debated education. When they did, it was nearly always in the context of aid for the religious instruction of the poor. Indeed, even by 1850, nearly two decades after the first Great Reform Act (1832), the Prime Minister Lord John Russell made the case that a system of compulsory state schooling would be immoral and un-British. Yet, by the ‘80s, MPs debating in Westminster routinely drew connections between schooling and the most critical social issues of the day: social-class mobility and equity, child welfare, national development, emigration, and the civil service, among others. What explains the expanding, and expansive, political uses that elite policymakers put to schooling? How did schooling and education take on such an aggrandized role in society for British statesmen? To address these questions, this paper combines natural language processing techniques, semantic network, discourse, and regression analyses to read and interpret the ∼1.1 million political speeches given in the UK Houses of Parliament during the long nineteenth century (1804–1913). In contrast to explanations emphasizing the direct role that economic, social, and political development as well as conflict played in the UK state’s historic expansion, this piece demonstrates how social scientization, the sweeping international epistemic movement that institutionalized and diffused functionalist social theory, created the context that made it possible for political elites to see and promote schooling as an effective policy instrument of greater cultural rationalization supporting the development of capitalist industrial society.

Natural language processing and discourse complexity studies

The study presents an overview of discursive complexology, an integral paradigm of linguistics, cognitive studies and computer linguistics aimed at defining discourse complexity. The article comprises three main parts, which successively outline views on the category of linguistic complexity, history of discursive complexology and modern methods of text complexity assessment. Distinguishing the concepts of linguistic complexity, text and discourse complexity, we recognize an absolute nature of text complexity assessment and relative nature of discourse complexity, determined by linguistic and cognitive abilities of a recipient. Founded in the 19th century, text complexity theory is still focused on defining and validating complexity predictors and criteria for text perception difficulty. We briefly characterize the five previous stages of discursive complexology: formative, classical, period of closed tests, constructive-cognitive and period of natural language processing. We also present the theoretical foundations of Coh-Metrix, an automatic analyzer, based on a five-level cognitive model of perception. Computing not only lexical and syntactic parameters, but also text level parameters, situational models and rhetorical structures, Coh-Metrix provides a high level of accuracy of discourse complexity assessment. We also show the benefits of natural language processing models and a wide range of application areas of text profilers and digital platforms such as LEXILE and ReaderBench. We view parametrization and development of complexity matrix of texts of various genres as the nearest prospect for the development of discursive complexology which may enable a higher accuracy of inter- and intra-linguistic contrastive studies, as well as automating selection and modification of texts for various pragmatic purposes.

Pronominalization and Expectations for Re-Mention: Modeling Coreference in Contexts With Three Referents

The relationship between pronoun production and pronoun interpretation has been proposed to follow Bayesian principles, combining a comprehender’s expectation about which referent will be mentioned next and their estimate of how likely it is that a potential referent will be re-mentioned using a pronoun. The Bayesian Model has received support from studies in several languages (English, Mandarin Chinese, Catalan, German), but tested contexts have been limited to two event participants, whereas natural language discourse often involves contexts with more than two event participants. In this study, we conducted three story continuation experiments to assess how the Bayesian Model performs in more complex contexts. Our results show that even in contexts with three event participants, comprehenders can behave rationally when interpreting pronouns, but that they appear to require sufficient context to build up a coherent representation of the situation to do so. In addition to testing the basic claim of the Bayesian Model (Weak Bayes), we test the central prediction of the Strong form of the hypothesis: that the two components of the model (next-mention expectations and choice of referring expression) are influenced by dissociated sets of factors. In a model comparison, Experiments 2 and 3 confirm the closest fit from the Bayesian Model, which supports Weak Bayes, and none of our experiments find evidence that the predictability of a referent affects pronominalization rates, which corroborates Strong Bayes. Finally, we test whether the rate of pronominalization is sensitive to factors related to ambiguity and argument/adjunct status of referents; we find that participants vary their production of pronouns most strongly based on the grammatical role of the antecedent (subject or not), with a smaller effect from the presence/absence of a gender-matched competitor and no effect from the syntactic position of this competing referent.

Discourse expectations: explaining the implicit causality biases of verbs

Abstract This article presents a linguistic account explaining particular mechanisms underlying the generation of expectations at the discourse level. We further develop a linguistic theory – the Empty Slot Theory – explaining the phenomenon of implicit verb causality. According to our proposal, implicit causality (IC) verbs introduce lexically determined slots for causal content of specific types. If the required information is not derivable from the current or preceding context, IC verbs generate the expectation that these slots will be filled in the upcoming discourse. The cognitive mechanism underlying the bias is grounded in the general processing strategy of avoiding accommodation. Empirical evidence for the proposed theory is provided in three continuation experiments in German with comprehensive semantic annotation of the continuations provided by the participants. The reported experiments consistently show that IC bias can be manipulated in systematic ways. Experiment 1 demonstrates important ontological constraints on causal content crucial for our theory. Experiments 2 and 3 show how IC biases can be manipulated in predictable ways by filling the hypothesized slots in the prompt. Experiment 2 illustrates that stimulus-experiencer (experiencer-object) verbs in contrast to causative agent-patient verbs can be manipulated with respect to coherence and coreference by employing adverbial modification. Filling the lexically determined slot of psychological verbs resulted in predictable changes in coherence relations and types of explanations, resulting in the predicted effects on coreference. Experiment 3 extends the empirical investigations to so-called “agent-evocator” verbs. Again, filling the semantic slot as part of the prompt resulted in predictable shifts in coherence relations and explanation types with transparent effects on coreference. The reported experiments shed further light on the close correspondence between coherence and coreference as a hallmark of natural language discourse.

What is the abdomen? Rationalising clinical and anatomical perspectives using formal semantics.

The meaning of the term ‘abdomen’ has become increasingly ambiguous, as it has to satisfy the contemporary requirements of natural language discourse, literature, gross and radiological anatomy and its role in ontologies supporting electronic records and data modelling. It is critical that there is an agreed understanding of the semantics of the abdominopelvic cavity, its component volumes including the abdomen proper, true and false pelvic cavities, and its boundaries and regional contents. The expression of part–whole (meronymic) relationships is essential for inferences to be drawn by computer algorithms, but unless these are rigorously reviewed and tested incorrect assumptions are drawn. The SNOMED CT terminology descriptions and hierarchy of anatomical concepts relating to the trunk were scrutinised for ambiguity and sub‐optimal relationships using a panel of reference sources. Any identified errors were corrected and the impact of any changes reviewed iteratively by evaluating their effect on dependant hierarchies (modelled with the associated anatomical concepts). Anatomical concepts are generally structured according to a traditional gross standpoint, but in clinical practice covert complex regional notions are frequently used and during the evaluation process a new viewpoint relating to projectional (transmissive) or emissive radiological perspective was identified. The subtle but important differences in the boundaries, volumes and contents of these distinctive perspectives of the ‘abdomen’ are presented. Three significant complex variants have been identified which relate to the most common uses of the word ‘abdomen’. The merits and disadvantages of using ‘abdomen’ as common synonym to more than one concept (polysemy) are briefly discussed and the solution adopted by SNOMED International described. The review of existing ontologies and academic literature confirmed the frequent varied use of the word ‘abdomen’, which raises concerns when derived data are increasingly being used remotely from the point of clinical contact, potentially leading to incorrect inferences. The documented regional truncal volumes from an anatomical regional, segmental and cross‐sectional perspective have been integrated into a logical and comprehensive model suitable for computer processing. The robust modelling of meronymic hierarchies has to be rigorous to avoid systematic errors and it is thus timely that a proposed standard description of these subtly related volumes and structures is made available for discussion and comment.

Leveraging Machine Learning to Explain the Nature of Written Genres

The analysis of discourse and the study of what characterizes it in terms of communicative objectives is essential to most tasks of Natural Language Processing. Consequently, research on textual genres as expressions of such objectives presents an opportunity to enhance both automatic techniques and resources. To conduct an investigation of this kind, it is necessary to have a good understanding of what defines and distinguishes each textual genre. This research presents a data-driven approach to discover and analyze patterns in several textual genres with the aim of identifying and quantifying the differences between them, considering how language is employed and meaning expressed in each particular case. To identify and analyze patterns within genres, a set of linguistic features is first defined, extracted and computed by using several Natural Language Processing tools. Specifically, the analysis is performed over a corpora of documents-containing news, tales and reviews-gathered from different sources to ensure an heterogeneous representation. Once the feature dataset has been generated, machine learning techniques are used to ascertain how and to what extent each of the features should be present in a document depending on its genre. The results show that the set of features defined is relevant for characterizing the different genres. Furthermore, the findings allow us to perform a qualitative analysis of such features, so that their usefulness and suitability is corroborated. The results of the research can benefit natural language discourse processing tasks, which are useful both for understanding and generating language.

From Boltzmann to Zipf through Shannon and Jaynes.

The word-frequency distribution provides the fundamental building blocks that generate discourse in natural language. It is well known, from empirical evidence, that the word-frequency distribution of almost any text is described by Zipf’s law, at least approximately. Following Stephens and Bialek (2010), we interpret the frequency of any word as arising from the interaction potentials between its constituent letters. Indeed, Jaynes’ maximum-entropy principle, with the constrains given by every empirical two-letter marginal distribution, leads to a Boltzmann distribution for word probabilities, with an energy-like function given by the sum of the all-to-all pairwise (two-letter) potentials. The so-called improved iterative-scaling algorithm allows us finding the potentials from the empirical two-letter marginals. We considerably extend Stephens and Bialek’s results, applying this formalism to words with length of up to six letters from the English subset of the recently created Standardized Project Gutenberg Corpus. We find that the model is able to reproduce Zipf’s law, but with some limitations: the general Zipf’s power-law regime is obtained, but the probability of individual words shows considerable scattering. In this way, a pure statistical-physics framework is used to describe the probabilities of words. As a by-product, we find that both the empirical two-letter marginal distributions and the interaction-potential distributions follow well-defined statistical laws.

Towards Compositional Distributional Discourse Analysis

Categorical compositional distributional semantics provide a method to derive the meaning of a sentence from the meaning of its individual words: the grammatical reduction of a sentence automatically induces a linear map for composing the word vectors obtained from distributional semantics. In this paper, we extend this passage from word-to-sentence to sentence-to-discourse composition. To achieve this we introduce a notion of basic anaphoric discourses as a mid-level representation between natural language discourse formalised in terms of basic discourse representation structures (DRS); and knowledge base queries over the Semantic Web as described by basic graph patterns in the Resource Description Framework (RDF). This provides a high-level specification for compositional algorithms for question answering and anaphora resolution, and allows us to give a picture of natural language understanding as a process involving both statistical and logical resources.

Sign Levels Synopsis

This Tractatus-style sequence of propositions describes logical features of natural language discourse, pre-linguistic levels of signs interpreted in associative learning and animal communication, and the specialized discourses of the institutions of science, religion, law, politics, and the arts. Its comprehensive scope is designed to help overcome the compartmentalization of philosophy into its branches of epistemology, ethics, political philosophy, and aesthetics. The general perspective is that of pragmatic naturalism as developed by the classical pragmatists Peirce, James, Schiller, and Dewey. Central to pragmatism is its emphasis on the use of practical inferences combining descriptive and expressive premisses with prescriptive “ought” conclusions guiding conduct. I argue that by making such inferences its focus, pragmatism enables contemporary philosophy to offset the effects of social specialization.

Profiles of Dialogue for Relevance

This paper uses argument diagrams, argumentation schemes, and some tools from formal argumentation systems developed in artificial intelligence to build a graph-theoretic model of relevance shown to be applicable (with some extensions) as a practical method for helping a third party judge issues of relevance or irrelevance of an argument in real examples. Examples used to illustrate how the method works are drawn from disputes about relevance in natural language discourse, including a criminal trial and a parliamentary debate.

Truth Analysis of the Gettier Argument

AbstractGettier (1963) presented the now famous Gettier problem as a challenge to epistemology. The methods Gettier used to construct his challenge, however, utilized certain principles of formal logic that are actually inappropriate for the natural language discourse of the Gettier cases. In that challenge to epistemology, Gettier also makes truth claims that would be considered controversial in analytic philosophy of language. The Gettier challenge has escaped scrutiny in these other relevant academic disciplines, however, because of its façade as an epistemological analysis. This article examines Gettier's methods with the analytical tools of logic and analytic philosophy of language.

The geometry and dynamics of lifelogs: discovering the organizational principles of human experience.

A correlation dimension analysis of people’s visual experiential streams captured by a smartphone shows that visual experience is two-scaled with a smaller dimension at shorter length scales than at longer length scales. The bend between the two scales is a phase transition point where the lower scale primarily captures relationships within the same context and the higher dimensional scale captures relationships between different contexts. The dimensionality estimates are confirmed using Takens’ delay embedding procedure on the image stream, while the randomly permuted stream is shown to be space-filling thereby establishing that the two-scaled structure is a consequence of the dynamics. We note that the structure of visual experience closely resembles the structure of another domain of experience: natural language discourse. The emergence of an identical structure across different domains of human experience suggests that the two-scaled geometry reflects a general organizational principle.

A Logical System of Metaphor Understanding within Context

The research of metaphor understanding is a significant field of cognitive science. Metaphor plays an important role in natural language discourse. There is a close relationship between logic and metaphor. Metaphor can be described by epistemic logic. In this article, we try to put forward a logical system to describe metaphor understanding within context. First, we introduce some basic concepts of this metaphor understanding logical system. Then, a logical system of metaphor understanding within context is constructed, including the language, semantics, axioms and so on. Finally, some discussions about this logical system are showed.

Arguments from Fairness and Misplaced Priorities in Political Argumentation

Argumentation schemes are formal structures that are used to represent types of arguments that are prominent in natural language discourse. Recent research in argumentation studies has identified and investigated many of these schemes. The two types of arguments studied in this paper, found to be used during a pilot study of kinds of arguments used by the candidates in a recent Ontario provincial election, have not so far been formalized as schemes. We call them argument from fairness and argument from misplaced priorities. We formulate schemes for them, and analyze several examples of them from our corpus by means of constructing argument diagrams that fit the argument into the scheme.

Granular Causality Applications

Causal markers, syntactic structures and connectives have been the sole identifying features for automatically extracting causal relations in natural language discourse. However, various connective...

Granular Causality Applications

Causal markers, syntactic structures and connectives have been the sole identifying features for automatically extracting causal relations in natural language discourse. However, various connectives such as “and”, prepositions such as “as”, and other syntactic structures are highly ambiguous in nature, as they have multiple meanings besides causality. As a result, one cannot solely rely on lexico-syntactic markers for detection of causal phenomenon in discourse. This paper introduces the Theory of Granular Causality and describes a new approach to identify causality in natural language. Causality is often granular in nature (Mulkar-Mehta, 2011; Mazlack, 2004), and this property of causality is used to discover and infer the presence of causal relations in text. This is compared with causal relations identified using just causal markers. A precision of 0.91 and a recall of 0.79 is achieved using granularity for causal relation detection, as compared to a precision of 0.79 and a recall of 0.44 using text-based causal words for causality detection. Next, the author presents the findings for discovering causal relations between two sentences in an article. The system achieves a precision of 0.60 for discovering causality between two sentences using granular causality markers as features. The results are encouraging, and show that the granular causality is an important phenomenon in natural language