The Process of Speech-acting Specifies Methods for Grasping Meaning. Ten Operations. A Contribution to Hermeneutics

How does speech-acting theory explain thegeneration of meaning and meaningful collective action? What place does firstperson subjective experience have in the theory? What are some of themethodological implications of the theory? The purpose here is to outline the Searlean theory of meaning formationand to draw some directions for research into meaning formation andorganization from that outline. Searle assumes a deep intentionality, adirectedness towards the world of all the human capacities. Searle asks: how dohumans from external inputs from the world and through language produceknowledge of the world and organized projects that implemented change theworld. Reasoning implies meaning. Reasons to act identify conditions of success= a meaningful act. Research directions (10) are drawn from elements of the speech act theory: the locutionary process, status assignments and meanings,willfulness, types of speech acts, decision-making and organization

Когнитивни процеси, емоции и интелигентни интерфејси

Студијата презентира истражувања од повеќе научни дисциплини, како вештачка интелигенција, невронауки, психологија, лингвистика и филозофија, кои имаат потенцијал за креирање на интелигентни антропоморфни агенти и интерактивни технологии. Се разгледуваат системите од симболичка и конекционистичка вештачка интелигенција за моделирање на човековите когнитивни процеси, мислење, донесување одлуки, меморија и учење. Се анализираат моделите во вештачка интелигенција и роботика кои користат емоции како механизам за контрола на остварување на целите на роботот, како реакција на одредени ситуации, за одржување на процесот на социјална интеракција и за создавање на поуверливи антропормфни агенти. Презентираните интердисциплинарни методологии и концепти се мотивација за создавање на анимирани агенти кои користат говор, гестови, интонација и други невербални модалитети при конверзација со корисниците во интелигентните интерфејси

Fake News Analysis on Supreme Court Decisions Relating to Law No. 11 of 2008 From Linguistic Forensic Perspective

The purpose of this study is to analysis speech act form and speech event of a person from forensic linguistic perspective who break the rule of Law No. 11 of 2008 about Information and Electronic Transactions, Article 28 Paragraph (2), “Intentionally and Without Right Spread Information Intended to Induce Hate Speech or Hostility towards Certain Individuals and/or Community Groups Based on Ethnicity, Religion, Race, Group, and Intergroup (SARA).” This study used a desvriptive qualitative method. The data of conversation text includes identification, classification, analysis, and discussion. Participant who became the object of this research was a 54-year-old man who was accused of spreading fake news. The form of speech act data analysis refers to the theory of Austin Searle (1969), while speech events refer to Hymes (1972). The results of the research show that there are four speech acts, such as; assertive, directive, expressive, commissive, and declarative. The results of the research on speech events found eight speech events, starting from setting scene, participant, ending, action, instrumentality, key, norm, and genre

Toward Online Linguistic Surveillance of Threatening Messages

Threats are communicative acts, but it is not always obvious what they communicate or when they communicate imminent credible and serious risk. This paper proposes a research- and theory-based set of over 20 potential linguistic risk indicators that may discriminate credible from non-credible threats within online threat message corpora. Two prongs are proposed: (1) Using expert and layperson ratings to validate subjective scales in relation to annotated known risk messages, and (2) Using the resulting annotated corpora for automated machine learning with computational linguistic analyses to classify non-threats, false threats, and credible threats. Rating scales are proposed, existing threat corpora are identified, and some prospective computational linguistic procedures are identified. Implications for ongoing threat surveillance and its applications are explored

Computing the meaning of the assertive speech act by a software agent

[EN] This paper examines the nature of the assertive speech act of Irish. We examine the syntactical constructional form of the assertive to identify its constructional signature. We consider the speech act as a construction whose meaning as an utterance depends on the framing situation and context, along with the common ground of the interlocutors. We identify how the assertive speech act is formalised to make it computer tractable for a software agent to compute its meaning, taking into account the contribution of situation, context and a dynamic common ground. Belief, desire and intention play a role in what is meant as against what is said. The nature of knowledge, and how it informs common ground, is explored along with the relationship between knowledge and language. Computing the meaning of a speech act in the situation requires us to consider the level of the interaction of all these dimensions. We argue that the contribution of lexicon and grammar, with the recognition of belief, desire and intentions in the situation type and associated illocutionary force, sociocultural conventions of the interlocutors along with their respective general and cultural knowledge, their common ground and other sources of contextual information are all important for representing meaning in communication. We show that the influence of the situation, context and common ground feeds into the utterance meaning derivation. The ‘what is said’ is reflected in the event and its semantics, while the ‘what is meant’ is derived at a higher level of abstraction within a situation.Nolan, B. (2017). Computing the meaning of the assertive speech act by a software agent. Journal of Computer-Assisted Linguistic Research. 1(1):20-39. doi:10.4995/jclr.2017.7786.SWORD20391

Deception detection in dialogues

In the social media era, it is commonplace to engage in written conversations. People sometimes even form connections across large distances, in writing. However, human communication is in large part non-verbal. This means it is now easier for people to hide their harmful intentions. At the same time, people can now get in touch with more people than ever before. This puts vulnerable groups at higher risk for malevolent interactions, such as bullying, trolling, or predatory behavior. Furthermore, such growing behaviors have most recently led to waves of fake news and a growing industry of deceit creators and deceit detectors. There is now an urgent need for both theory that explains deception and applications that automatically detect deception. In this thesis I address this need with a novel application that learns from examples and detects deception reliably in natural-language dialogues. I formally define the problem of deception detection and identify several domains where it is useful. I introduce and evaluate new psycholinguistic features of deception in written dialogues for two datasets. My results shed light on the connection between language, deception, and perception. They also underline the challenges and difficulty of assessing perceptions from written text. To automatically learn to detect deception I first introduce an expressive logical model and then present a probabilistic model that simplifies the first and is learnable from labeled examples. I introduce a belief-over-belief formalization, based on Kripke semantics and situation calculus. I use an observation model to describe how utterances are produced from the nested beliefs and intentions. This allows me to easily make inferences about these beliefs and intentions given utterances, without needing to explicitly represent perlocutions. The agents’ belief states are filtered with the observed utterances, resulting in an updated Kripke structure. I then translate my formalization to a practical system that can learn from a small dataset and is able to perform well using very little structural background knowledge in the form of a relational dynamic Bayesian network structure

Conversation and behavior games in the pragmatics of dialogue

In this article we present the bases for a computational theory of the cognitive processes underlying human communication. The core of the article is devoted to the analysis of the phases in which the process of comprehension of a communicative act can be logically divided: (1) literal meaning, where the reconstruction of the mental states literally expressed by the actor takes place; (2) speaker’s meaning. where the partner reconstructs the communicative intentions of the actor; (3) communicative effect, where the partner possibly modifies his own beliefs and intentions; (4) reaction, where the intentions for the generation of the response are produced: and (5) response, where an overt response is constructed. The model appears to be compatible with relevant facts about human behavior. Our hypothesis is that, through communication, on actor tries to exploit the motivational structures of a partner so that the desired goal is generated. A second point is that social behavior requires that cooperation be maintained at some level. In the case of communication, cooperation is, in general, pursued even when the partner does not adhere to the actor’s goals, and therefore no cooperation occurs at the behavioral level. This important distinction is reflected in the two kinds of games we introduce to account for communication. The main concept implied in communication is that two agents overtly reach a situation of shared mental states. Our model deols with sharedness through two primitives: shared beliefs and communicative intentions

Automatic extraction of agendas for action from news coverage of violent conflict

Words can make people act. Indeed, a simple phrase ‘Will you, please, open the window?’ can cause a person to do so. However, does this still hold, if the request is communicated indirectly via mass media and addresses a large group of people? Different disciplines have approached this problem from different angles, showing that there is indeed a connection between what is being called for in media and what people do. This dissertation, being an interdisciplinary work, bridges different perspectives on the problem and explains how collective mobilisation happens, using the novel term ‘agenda for action’. It also shows how agendas for action can be extracted from text in automated fashion using computational linguistics and machine learning. To demonstrate the potential of agenda for action, the analysis of The NYT and The Guardian coverage of chemical weapons crises in Syria in 2013 is performed. Katsiaryna Stalpouskaya has always been interested in applied and computational linguistics. Pursuing this interest, she joined FP7 EU-INFOCORE project in 2014, where she was responsible for automated content analysis. Katsiaryna’s work on the project resulted in a PhD thesis, which she successfully defended at Ludwig-Maximilians-Universität München in 2019. Currently, she is working as a product owner in the field of text and data analysis

Automatic extraction of agendas for action from news coverage of violent conflict

Words can make people act. Indeed, a simple phrase ‘Will you, please, open the window?’ can cause a person to do so. However, does this still hold, if the request is communicated indirectly via mass media and addresses a large group of people? Different disciplines have approached this problem from different angles, showing that there is indeed a connection between what is being called for in media and what people do. This dissertation, being an interdisciplinary work, bridges different perspectives on the problem and explains how collective mobilisation happens, using the novel term ‘agenda for action’. It also shows how agendas for action can be extracted from text in automated fashion using computational linguistics and machine learning. To demonstrate the potential of agenda for action, the analysis of The NYT and The Guardian coverage of chemical weapons crises in Syria in 2013 is performed. Katsiaryna Stalpouskaya has always been interested in applied and computational linguistics. Pursuing this interest, she joined FP7 EU-INFOCORE project in 2014, where she was responsible for automated content analysis. Katsiaryna’s work on the project resulted in a PhD thesis, which she successfully defended at Ludwig-Maximilians-Universität München in 2019. Currently, she is working as a product owner in the field of text and data analysis