We describe a knowledge representation and inference formalism, based on an intensional propositional semantic network, in which variables are structures terms consisting of quantifier, type, and other information. This has three important consequences for natural language processing. First, this leads to an extended, more natural formalism whose use and representations are consistent with the use of variables in natural language in two ways: the structure of representations mirrors the structure of the language and allows re-use phenomena (...) such as pronouns and ellipsis. Second, the formalism allows the specification of description subsumption as a partial ordering on related concepts (variable nodes in a semantic network) that relates more general concepts to more specific instances of that concept, as is done in language. Finally, this structured variable representation simplifies the resolution of some representational difficulties with certain classes of natural language sentences, namely, donkey sentences and sentences involving branching quantifiers. The implementation of this formalism is called ANALOG (A NAtural LOGIC) and its utility for natural language processing tasks is illustrated. (shrink)

This paper explores two important ways in which the practice of close reading differs from the technique of natural language processing, the use of computer programming to decode, process, and replicate messages within a human language. It does so in order to highlight distinctive features of close reading that are not replicated by natural language processing. The first point of distinction concerns the nature of the meaning generated in each case. While natural language (...) class='Hi'>processing proceeds on the principle that a text’s meaning can be deciphered by applying the rules governing the language in which the text is written, close reading is premised on the idea that this meaning lies in the interplay that the text prompts within readers. While the semantic theory of meaning upon which natural language processing programs are based is often taken for granted today, I draw from phenomenological and hermeneutic theories, particularly Wolfgang Iser and Hans-Georg Gadamer, to explain why a different theory of meaning is necessary for understanding the meaning generated by close reading. Second, while natural language processing programs are considered successful when they generate what epistemologists call true beliefs about a text, I argue that close reading aims first and foremost at the development, not of true belief, but of understanding. To develop this distinction, I draw from recent scholarship on the epistemology of education, including work by Duncan Pritchard, to explain how understanding differs from true belief and why attainment of the latter is less educationally significant than the former. (shrink)

This paper presents two projects concerned with the application of natural language processing technology for improving communication between Public Administration and citizens. The first project, GIST,is concerned with automatic multilingual generation of instructional texts for form-filling. The second project, TAMIC, aims at providing an interface for interactive access to information, centered on natural language processing and supposed to be used by the clerk but with the active participation of the citizen.

Natural language processing (NLP) has undergone significant changes in its methods, reflecting advances in computing technology and cognitive research. This article reviews the key stages of the evolution of natural language processing methods. The article touches on the topic of the first NLP systems developed, provides justification for the reasons for the complexity of some processed texts and the possible depth of analysis. In addition, it describes not only NLP methods before and after the GPT revolution, (...) but also current trends and prospects in the field of natural language processing. The article allows us to trace how the idea of natural language text has changed during the development of computer analysis methods, as well as to understand what text is in the mirror of natural language processing, what is really the subject of natural language processing research and what cannot be seen through the eyes of a simple researcher who does not use NLP methods. (shrink)

The contract review process can be a costly and time-consuming task for lawyers and clients alike, requiring significant effort to identify and evaluate the legal implications of individual clauses. To address this challenge, we propose the use of natural language processing techniques, specifically text classification based on deontic tags, to streamline the process. Our research question is whether natural language processing techniques, specifically dense vector embeddings, can help semi-automate the contract review process and reduce time and (...) costs for legal professionals reviewing deontic modalities in contracts. In this study, we create a domain-specific dataset and train both baseline and neural network models for contract sentence classification. This approach offers a more efficient and cost-effective solution for contract review, mimicking the work of a lawyer. Our approach achieves an accuracy of 0.90, showcasing its effectiveness in identifying and evaluating individual contract sentences. (shrink)

Pickering & Garrod (P&G) claim that the automatic mechanisms that underlie language processing in dialogue are absent in monologue. We disagree with this claim, and argue that dialogue simply provides a different context in which the same basic processes operate.

COVID-19 is a disease that affects the quality of life in all aspects. However, the government policy applied in 2020 impacted the lifestyle of the whole world. In this sense, the study of sentiments of people in different countries is a very important task to face future challenges related to lockdown caused by a virus. To contribute to this objective, we have proposed a natural language processing model with the aim to detect positive and negative feelings in open-text (...) answers obtained from a survey in pandemic times. We have proposed a distilBERT transformer model to carry out this task. We have used three approaches to perform a comparison, obtaining for our best model the following average metrics: Accuracy: 0.823, Precision: 0.826, Recall: 0.793 and F1 Score: 0.803. (shrink)

Knowledge is power. Knowledge about human psychology is increasingly being produced using natural language processing (NLP) and related techniques. The power that accompanies and harnesses this knowledge should be subject to ethical controls and oversight. In this chapter, we address the ethical pitfalls that are likely to be encountered in the context of such research. These pitfalls occur at various stages of the NLP pipeline, including data acquisition, enrichment, analysis, storage, and sharing. We also address secondary uses of (...) the results and tools developed through psychometric NLP, such as profit-driven targeted advertising, political campaigns, and domestic and international psyops. Along the way, we reflect on potential ethical guidelines and considerations that may help researchers navigate these pitfalls. (shrink)

Progress in philosophy is difficult to achieve because our methods are evidentially and rhetorically weak. In the last two decades, experimental philosophers have begun to employ the methods of the social sciences to address philosophical questions. However, the adequacy of these methods has been called into question by repeated failures of replication. Experimental philosophers need to incorporate more robust methods to achieve a multi-modal perspective. In this chapter, we describe and showcase cutting-edge methods for data-mining and visualization. Big data is (...) a useful investigatory tool for moral psychology, and it fits well with the Ramsification method the first author advances in a series of recent papers. The guiding insight of these papers is that we can infer the meaning and structure of concepts from patterns of assertions and inferential associations in natural language. (shrink)

This Special Issue on Connectionist Models of Human Language Processing provides an opportunity for an appraisal both of specific connectionist models and of the status and utility of connectionist models of language in general. This introduction provides the background for the papers in the Special Issue. The development of connectionist models of language is traced, from their intellectual origins, to the state of current research. Key themes that arise throughout different areas of connectionist psycholinguistics are highlighted, (...) and recent developments in speech processing, morphology, sentence processing, language production, and reading are described. We argue that connectionist psycholinguistics has already had a significant impact on the psychology of language, and that connectionist models are likely to have an important influence on future research. (shrink)

Imagine a telephone conversation between a presidential aide and a wealthy supporter, shortly after news breaks that the president plans to veto a bill that the supporter strongly favors. The nervous aide opens with “I'm calling to let you know that the president regrets his, uh, his decision…” The supporter's hopes rise at the intimation that the president changed his mind. But when the aide continues: “… did not meet with your apparel, I mean, your approval,” the crestfallen (and, perhaps, (...) former) supporter realizes that the president is sorry merely about the impact of his decision, not the decision itself. The aide's utterance seems to mean one thing at first, but something entirely different in the end. Unintentional verbal about‐faces of this kind are common in everyday language use – the outspoken owner of an American baseball team recently said that she regretted her remarks offended many people – yet listeners rarely notice the miscues, the wealthy supporter notwithstanding. This is one of the chief motivations behind the psycholinguistic study of language use. (shrink)

Computer-based logic proofs are a form of unnatural language in which the process and structure of proof generation can be observed in considerable detail. We have been studying how students respond to multimodal logic teaching, and performance measures have already indicated that students' pre-existing cognitive styles have a significant impact on teaching outcome. Furthermore, a large corpus of proofs has been gathered via automatic logging of proof development. This paper applies a series of techniques, including corpus statistical methods, to (...) the proof logs. The results indicate that students' cognitive styles influence the structure of their logical discourse, via their differing methods of handling abstract information in diagrams, and transferring information between modalities. (shrink)

Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of (...) theoretical developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty of the stimulus, and suggesting fruitful links with probabilistic theories of categorization and ambiguity resolution in perception. (shrink)

In the property listing task (PLT), participants are asked to list properties for a concept (e.g., for the concept dog, “barks,” and “is a pet” may be produced). In conceptual property norming (CPNs) studies, participants are asked to list properties for large sets of concepts. Here, we use a mathematical model of the property listing process to explore two longstanding issues: characterizing the difference between concrete and abstract concepts, and characterizing semantic knowledge in the blind versus sighted population. When we (...) apply our mathematical model to a large CPN reporting properties listed by sighted and blind participants, the model uncovers significant differences between concrete and abstract concepts. Though we also find that blind individuals show many of the same processing differences between abstract and concrete concepts found in sighted individuals, our model shows that those differences are noticeably less pronounced than in sighted individuals. We discuss our results vis‐a‐vis theories attempting to characterize abstract concepts. (shrink)

Compositionality has been a central concept in linguistics and philosophy for decades, and it is increasingly prominent in many other areas of cognitive science. Its status, however, remains contentious. Here, I reassess the nature and scope of the principle of compositionality (Partee, 1995) from the perspective of psycholinguistics and cognitive neuroscience. First, I review classic arguments for compositionality and conclude that they fail to establish compositionality as a property of human language. Next, I state a new competence argument, acknowledging (...) the fact that any competent user of a language L can assign to most expressions in L at least one meaning which is a functiononlyof the meanings of the expression's parts and of its syntactic structure. I then discuss selected results from cognitive neuroscience, indicating that the human brain possesses the processing capacities presupposed by the competence argument. Finally, I outline a language processing architecture consistent with the neuroscience results, where semantic representations may be generated by a syntax‐driven streamandby an “asyntactic” processing stream, jointly or independently. Compositionality is viewed as a constraint on computation in the former stream only. (shrink)

Espino, Santamaria, and Garcia-Madruga (2000) report three results on the time taken to respond to a probe word occurring as end term in the premises of a syllogistic argument. They argue that these results can only be predicted by the theory of mental models. It is argued that two of these results, on differential reaction times to end-terms occurring in different premises and in different figures, are consistent with Chater and Oaksford's (1999) probability heuristics model (PHM). It is argued that (...) the third finding, on different reaction times between figures, does not address the issue of processing difficulty where PHM predicts no differences between figures. It is concluded that Espino et al.'s results do not discriminate between theories of syllogistic reasoning as effectively as they propose. (shrink)

Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using probing classifiers, a popular analysis (...) technique in NLP (and deep learning more broadly). The project of operationalising a philosophically-informed notion of representation should be of interest to both philosophers of science and NLP practitioners. It affords philosophers a novel testing-ground for claims about the nature of representation, and helps NLPers organise the large literature on probing experiments, suggesting novel avenues for empirical research. (shrink)

According to embodied theories of language (ETLs), word meaning relies on sensorimotor brain areas, generally dedicated to acting and perceiving in the real world. More specifically, words denoting actions are postulated to make use of neural motor areas, while words denoting visual properties draw on the resources of visual brain areas. Therefore, there is a direct correspondence between word meaning and the experience a listener has had with a word's referent on the brain level. Behavioral and neuroimaging studies have (...) provided evidence in favor of ETLs; however, recent studies have also shown that sensorimotor information is recruited in a flexible manner during language comprehension (e.g., Raposo et al. ; Van Dam et al., ), leaving open the question as to what level of language processing sensorimotor activations contribute. In this study, we investigated the time course of modality‐specific contributions (i.e., the contribution of action information) as to word processing by manipulating both (a) the linguistic and (b) the action context in which target words were presented. Our results demonstrate that processes reflecting sensorimotor information play a role early in word processing (i.e., within 200 ms of word presentation), but that they are sensitive to the linguistic context in which a word is presented. In other words, when sensorimotor information is activated, it is activated quickly; however, specific words do not reliably activate a consistent sensorimotor pattern. (shrink)

According to embodied theories of language (ETLs), word meaning relies on sensorimotor brain areas, generally dedicated to acting and perceiving in the real world. More specifically, words denoting actions are postulated to make use of neural motor areas, while words denoting visual properties draw on the resources of visual brain areas. Therefore, there is a direct correspondence between word meaning and the experience a listener has had with a word's referent on the brain level. Behavioral and neuroimaging studies have (...) provided evidence in favor of ETLs; however, recent studies have also shown that sensorimotor information is recruited in a flexible manner during language comprehension (e.g., Raposo et al. ; Van Dam et al., ), leaving open the question as to what level of language processing sensorimotor activations contribute. In this study, we investigated the time course of modality-specific contributions (i.e., the contribution of action information) as to word processing by manipulating both (a) the linguistic and (b) the action context in which target words were presented. Our results demonstrate that processes reflecting sensorimotor information play a role early in word processing (i.e., within 200 ms of word presentation), but that they are sensitive to the linguistic context in which a word is presented. In other words, when sensorimotor information is activated, it is activated quickly; however, specific words do not reliably activate a consistent sensorimotor pattern. (shrink)

Corpus-based word frequencies are one of the most important predictors in language processing tasks. Frequencies based on conversational corpora are shown to better capture the variance in lexical decision tasks compared to traditional corpora. In this study, we show that frequencies computed from social media are currently the best frequency-based estimators of lexical decision reaction times. The results are robust and are still substantial when we control for corpus size.

The first of its kind to thoroughly cover language technology at all levels and with all modern technologies this book takes an empirical approach to the ...

First published in 1982. Routledge is an imprint of Taylor & Francis, an informa company.

Semantic noise, the effect ensuing from the denotative and thus functional variability exhibited by different terms in different contexts, is a common concern in natural language processing (NLP). While unarguably problematic in specific applications (e.g., certain translation tasks), the main argument of this paper is that failing to observe this linguistic matter of fact as a generative effect rather than as an obstacle, leads to actual obstacles in instances where language model outputs are presented as neutral. Given (...) that a common and long-standing challenge in NLP is the interpretation of ambiguous – i.e., semantically noisy – cases, this article focuses on an exemplar ambiguity-resolution task in NLP: the problem of anaphora in Winograd schemas. The main question considered is: to what extent is the standard approach to disambiguation in NLP subject to a stagnant “image of language”? And, can a transdisciplinary, dynamic approach combining linguistics and philosophy elucidate new perspectives on these possible conceptual shortcomings? In order to answer these questions we explore the term and concept of noise, particularly in its presentation as semantic noise. Owing to its definitional plurality, and sometimes even desirable unspecificity, the term noise is thus used as proof of concept for semantic generativity being an inherent characteristic in linguistic representation, and its concept is used to interrogate assumptions admitted in the resolution of Winograd schemas. The argument is speculative and theoretical in method, and the result is an analysis which provides an account of the fundamentally dialogical and necessarily open-ended effects of semantic noise in natural language. (shrink)

This paper provides an epistemological and methodological analysis of the recent practice of using neural language models to simulate brain language processing. It is argued that, on the one hand, this practice can be understood as an instance of the traditional simulative method in artificial intelligence, following a mechanistic understanding of the mind; on the other hand, that it modifies the simulative method significantly. Firstly, neural language models are introduced; a study case showing how neural (...) class='Hi'>language models are being applied in cognitive neuroscience for simulative purposes is then presented; after recalling the main epistemological features of the simulative method in artificial intelligence, it is finally highlighted how the epistemic opacity of neural language models is tackled by using the brain itself to simulate the neural language model and to test hypotheses about it, in what is called here a co-simulation. (shrink)

Our understanding of dual-process models of cognition may benefit from a consideration of language processing, as language comprehension involves fast and slow processes analogous to those used for reasoning. More specifically, De Neys's criticisms of the exclusivity assumption and the fast-to-slow switch mechanism are consistent with findings from the literature on the construction and revision of linguistic interpretations.

Given the discovery of a distributed language and motor functional network, surprisingly few studies have explored whether language processing is related to motor skill training. To address this issue, the present study used functional magnetic resonance imaging to compare whole-brain activation between nonexperts and experts in table tennis, an open skill sport in which players make rapid decisions in response to an ever-changing environment. Whole-brain activation was assessed in 30 expert table tennis players with more than 7 (...) years’ experience and 35 age-matched nonexpert college students while they performed both a size and a semantic judgment task of words presented on a monitor. Compared with nonexperts, expert table tennis players showed greater activation in the left middle occipital gyrus and right precuneus while judging the size of the words versus during baseline fixation. They also showed greater activation in the left lingual gyrus during the semantic judgment task versus during baseline fixation. Our findings indicate that the visual regions engaged in language processing are associated with open motor skill training. (shrink)

I defend a claim, central to much work in psycholinguistics, that constructing mental representations of syntactic structures is a necessary step in language comprehension. Call such representations “mental phrase markers”. Several theorists in psycholinguistics, AI, and philosophy have cast doubt on the usefulness of positing MPMs. I examine their proposals and argue that they face major empirical and conceptual difficulties. My conclusions tell against the broader skepticism that persists in philosophy—e.g., in the embodied cognition literature —about the usefulness of (...) positing mental representations in psychological models. Using my discussion of sentence parsing as a case study, I propose several conditions on an appropriate appeal to mental representations. Finally, I point out that the familiar arguments from productivity, systematicity, and inferential coherence suffice to establish that MPMs themselves have a c.. (shrink)

Evidence that an animal is capable of some degree of symbolic, human language processing supports the argument that the animal's consciousness is to some degree human-like. In this paper, we reinterpret the findings of Savage- Rumbaugh et al. using the twin tools of Deacon's referential hierarchy and Systemic Functional Linguistics, with a view to providing further corroborative evidence for a Bonobo ape's symbolic processing abilities, and as a result to open a window into the consciousness of at (...) least one non-human primate. (shrink)