A significant portion of the world’s text is tagged by readers on social bookmarking websites. Credit attribution is an inherent problem in these corpora because most pages have multiple tags, but the tags do not always apply with equal specificity across the whole document. Solving the credit attribution problem requires associating each word in a document with the most appropriate tags and vice versa. This paper introduces Labeled LDA, a topic model that constrains Latent Dirichlet Allocation by defining (...) a one-to-one correspondence between LDA’s latent topics and user tags. This allows Labeled LDA to directly learn word-tag correspondences. We demonstrate Labeled LDA’s improved expressiveness over traditional LDA with visualizations of a corpus of tagged web pages from del.icio.us. Labeled LDA outperforms SVMs by more than 3 to 1 when extracting tag-specific document snippets. As a multi-label text classifier, our model is competitive with a discriminative baseline on a variety of datasets. (shrink)

As new energy vehicle is the future of automobile development, it is of great significance to dig deeper into the technical topics and development trends of new energy vehicles for accurately understanding the technical trends of the new energy vehicle industry, grasping development opportunities, and scientifically formulating strategic plans. This paper takes the patent texts in the field of new energy vehicles from 2000 to 2020 in the patent database of CNKI as the data source, identifies 25 technical topics implied (...) in the patent texts by using the LDA topic model, analyzes the evolution trend of the 25 technical topics in terms of importance and popularity, and predicts the popularity and development trend of each technical topic in new energy vehicles from 2021 to 2025 by constructing the ARIMA model. The popularity and development trend of each technology topic of new energy vehicles in China from 2021 to 2025 are predicted by constructing ARIMA model. Drawing on quantitative evidence, the study found that there are top five technical topics in terms of importance in this field, namely, Topic 8, Topic 5, Topic 14, Topic 9, and Topic 15. From 2014 to 2020, the importances of Topic 5, Topic 8, Topic 6, Topic 9, and Topic 15 are gradually rising. In terms of popularity of technical topics, from 2014 to 2020, the first to fifth topics are Topic 20, Topic 8, Topic 3, Topic 15, and Topic 10. Based on the prediction of ARIMA model, it is found that the popularity of these five technical topics is steadily increasing from 2021 to 2025, among which the popularity of Topic 20 will increase from 63.58 to 113.07, the largest increase in popularity among all technical topics. The paper provides implications for countries dedicated to developing the new energy vehicle industry. (shrink)

Based on Network Agenda Setting Model, this study collected 42,516 media reports from Party Media, commercial media, and We Media of China during the COVID-19 pandemic. We trained LDA models for topic clustering through unsupervised machine learning. Questionnaires and social network analysis methods were then applied to examine the correlation between media network agendas and public network agendas in terms of explicit and implicit topics. The study found that the media reports could be classified into 14 topics by (...) the LDA topic modeling, and the three types of media presented homogeneity in the topics of their reports, yet had their own characteristics; there was a significant correlation between the media network agenda and the public network agenda, and the We Media reports had the most prominent effect on the public network agenda; the correlation between the media agenda and the implicit public agenda was higher than that of the explicit public agenda. Overall, findings showed a significant correlation between network agendas among different media. (shrink)

With the emergence of online communities, more and more people are participating in online technology communities to meet personalized learning needs. This study aims to investigate whether and how male and female users behave differently in online technology communities. Using text data from Python Technology Community, through LDA (Latent Dirichlet Allocation) model, sentiment analysis and regression analysis, this paper reveals the different topics of male and female users in the online technology community, their sentimental tendencies and activity under different (...) topics, and their correlation and their mutual influence. The results show that: (1) Male users tend to provide information help, while female users prefer to participate in the topic of making friends and advertising. (2) When communicating in the technology community, male and female users mostly express positive emotions, but female users express positive emotions more frequently. (3) Different emotional tendencies of male and female users under different topics have different effects on their activity in the community. The activity of female users is more susceptible to emotional orientation. (shrink)

Topic model is a well proven tool to investigate the semantic content of textual corpora. Yet corpora sometimes include texts in several languages, making it impossible to apply language-specific computational approaches over their entire content. This is the problem we encountered when setting to analyze a philosophy of science corpus spanning over eight decades and including original articles in Dutch, German and French, on top of a large majority of articles in English. To circumvent this multilingual problem, we (...) use machine-translation tools to bulk translate non-English documents into English. Though largely imperfect, especially syntactically, these translations nevertheless provide correctly translated terms and preserve the semantic proximity of documents with respect to one another. To assess the quality of this translation step, we develop a “semantic topology preservation test” that relies on estimating the extent to which document-to-document distances have been preserved during translation. We then conduct an LDA topic-model analysis over the entire corpus of translated and English original texts, and compare it to a topic-model done over the English original texts only. We thereby identify the specific contribution of the translated texts. These studies reveal a more complete picture of main topics that can found in the philosophy of science literature, especially during the early days of the discipline when numerous articles were published in languages other than English. (shrink)

This manuscript constructs an intelligent sentiment analysis and marketing model for bed and breakfast consumption based on a behavioral psychology perspective. Based on the LDA theme model, the theme features and keywords of the reviews covering user feedback are explored from the text data, and the theme framework of user sentiment perception is constructed by combining previous literature on user perception in the B&B market, and the themes of user online reviews are summarized in four dimensions: practical, sensory, (...) cognitive, and emotional components of user experience. In this manuscript, GooSeeker software was selected for data crawling and ROST CM developed by Wuhan University was used for text processing. To improve the accuracy of text classification and improve the missing data, the online comment text is divided into sentences by symbols, and the text is divided into words based on sentences, and the spatial vector model and the text feature word weighting method of TF-IDF are used for vector representation, and the polynomial Bayesian classifier is called to identify the topics of sentences. The classical Theory of Planned Behavior was used to analyze the influencing factors of the willingness to consume experiential B&B tourism, and countermeasure suggestions for the development of B&B tourism were proposed based on the research findings In the empirical testing stage, a questionnaire on the willingness to consume experiential B&B tourism was designed, and web research was chosen to collect the data. SPSS20.0 was used to conduct reliability analysis, factor analysis, correlation analysis, and regression analysis on the data, and AMOS statistics were used to establish a structural equation model to verify the influence path of willingness to consume experiential B&B tourism. Finally, the moderating path of willingness to consume experiential B&B tourism was verified by using multi-group analysis. (shrink)

In this study, multidimensional feature extraction is performed on the U-language recordings of the test takers, and these features are evaluated separately, with five categories of features: pronunciation, fluency, vocabulary, grammar, and semantics. A deep neural network model is constructed to model the feature values to obtain the final score. Based on the previous research, this study uses a deep neural network training model instead of linear regression to improve the correlation between model score and expert (...) score. The method of using word frequency for semantic scoring is replaced by the LDA topic model for semantic analysis, which eliminates the need for experts to manually label keywords before scoring and truly automates the critique. Also, this paper introduces text cleaning after speech recognition and deep learning-based speech noise reduction technology in the scoring model, which improves the accuracy of speech recognition and the overall accuracy of the scoring model. Also, innovative applications and improvements are made to key technologies, and the latest technical solutions are integrated and improved. A new open oral grading model is proposed and implemented, and innovations are made in the method of speech feature extraction to improve the dimensionality of open oral grading. (shrink)

We compared two digital humanities methods in the analysis of a contested scientific term. “Epigenetics” is as enigmatic as it is popular. Some authors argue that its meaning has diluted over time as this term has come to describe a widening range of entities and mechanisms (Haig, International Journal of Epidemiology 41:13–16, 2012). Others propose both a Waddingtonian “broad sense” and a mechanistic “narrow sense” definition to capture its various scientific uses (Stotz and Griffiths, History and Philosophy of the Life (...) Sciences 38:22, 2016). We evaluated these proposals by first replicating a recent analysis by (Linquist and Fullerton, Theoretical Medicine and Bioethics 42:137–154, 2021). We analyzed the 1100 most frequently cited abstracts on epigenetics across four disciplines: proximal biology, biomedicine, general biology, and evolution. Each abstract was coded for its heritability commitments (if any) and functional interpretation. A second study applied LDA topic modelling to the same corpus, thus providing a useful methodological comparison. The two methods converged on a discipline-relative ambiguity. Within such disciplines as biomedicine or molecular biology that focus on proximate mechanisms, “epigenetic(s)” refers to a range of molecular structures while specifying nothing in particular about their heritability. This proximal conception was primarily associated with the functions of gene regulation and disease. In contrast, a second relatively uncommon sense of “epigenetic(s)” is restricted to a small proportion of evolutionary abstracts. It refers to many of the same molecular structures, but regards them as trans-generationally inherited and associated with adaptive phenotypic plasticity. This finding underscores the benefit of digital tools in complementing traditional conceptual analysis. Philosophers should be cautious not to conflate the relatively uncommon evolutionary sense of epigenetics with the more widely used proximal conception. (shrink)

The wanton dissemination of network pseudohealth information has brought great harm to people’s health, life, and property. It is important to detect and identify network pseudohealth information. Based on this, this paper defines the concepts of pseudohealth information, data block, and data block integration, designs an architecture that combines the latent Dirichlet allocation algorithm and data block update integration, and proposes the combination algorithm model. In addition, crawler technology is used to crawl the pseudohealth information transmitted on the Sina (...) Weibo platform during the “epidemic situation” from February to March 2020 for the simulation test on the experimental case dataset. The research results show that the LDA model can deeply mine the semantic information of network pseudohealth information, obtain the features of document-topic distribution, and classify and train topic features as input variables; the dataset partitioning method can effectively block data according to the text attributes and class labels of network pseudohealth information and can accurately classify and integrate the block data through the data block reintegration method; and considering that the combination model has certain limitations on the detection of network pseudohealth information, the support vector machine model can extract the granularity content of data blocks in pseudohealth information in real time, thus greatly improving the recognition performance of the combination model. (shrink)

Against the background that the top-level semantic framework of Chinese traditional culture is not comprehensive and unified, this study aims to preserve and disseminate cultural heritage information about Chinese traditional culture through the development of a domain ontology which is constructed from ancient books. A combination of top-down and bottom-up approaches was used to construct the ontology for Chinese traditional culture. An investigation of historians’ needs, and LDA topic clustering model were conducted, understanding the specific needs of historians, (...) collecting the topic, concepts and relationships. CIDOC CRM was reused to construct the basic framework of CTCO. Ontology structure and function were adopted to evaluate the effectiveness of CTCO. Evaluation results show that the ontology meets all the quality criteria of OntoMetrics, and the experts agreed on content representation. CTCO contributes to the organization of traditional Chinese culture and the construction of related databases. The study also forms a common path and puts forward proposals for the construction of domain ontology, which has great social relevance. (shrink)

Sallie McFague’s theological models construct a tensive relationship between conceptual structures and symbolic, metaphorical language to interpret the defining and elusive aspects of theological phenomena and loci. Computational models of language can extend and formalize the conceptual structures of theological models to develop computer-augmented interpretations of theological texts. Previously unclear is whether computational models can retain the tensive symbolism essential for theological investigation. I demonstrate affirmatively by constructing a computational topic model of the moral theology of Thomas Aquinas (...) from Summa Theologica (Second Part, in English translation) useful for interpreting not only the Thomistic text but also recent papal encyclicals. (shrink)

In this paper, we describe a model that learns semantic representations from the distributional statistics of language. This model, however, goes beyond the common bag‐of‐words paradigm, and infers semantic representations by taking into account the inherent sequential nature of linguistic data. The model we describe, which we refer to as a Hidden Markov Topics model, is a natural extension of the current state of the art in Bayesian bag‐of‐words models, that is, the Topics model of (...) Griffiths, Steyvers, and Tenenbaum (2007), preserving its strengths while extending its scope to incorporate more fine‐grained linguistic information. (shrink)

Selected response items and constructed response items are often found in the same test. Conventional psychometric models for these two types of items typically focus on using the scores for correctness of the responses. Recent research suggests, however, that more information may be available from the CR items than just scores for correctness. In this study, we describe an approach in which a statistical topic model along with a diagnostic classification model was applied to a mixed item (...) format formative test of English and Language Arts. The DCM was used to estimate students’ mastery status of reading skills. These mastery statuses were then included in a topic model as covariates to predict students’ use of each of the latent topics in their written answers to a CR item. This approach enabled investigation of the effects of mastery status of reading skills on writing patterns. Results indicated that one of the skills, Integration of Knowledge and Ideas, helped detect and explain students’ writing patterns with respect to students’ use of individual topics. (shrink)

From the end of 2018 in China, the Big-data Driven Price Discrimination (BDPD) of online consumption raised public debate on social media. To study the consumers’ attitude about the BDPD, this study constructed a semantic recognition frame to deconstruct the Affection-Behavior-Cognition (ABC) consumer attitude theory using machine learning models inclusive of the Labeled Latent Dirichlet Allocation (LDA), Long Short-Term Memory (LSTM), and Snow Natural Language Processing (NLP), based on social media comments text dataset. Similar to the questionnaires published results, this (...) article verified that 61% of consumers expressed negative sentiment toward BDPD in general. Differently, on a finer scale, this study further measured the negative sentiments that differ significantly among different topics. The measurement results show that the topics “Regular Customers Priced High” (69%) and “Usage Intention” (67%) occupy the top two places of negative sentiment among consumers, and the topic “Precision Marketing” (42%) is at the bottom. Moreover, semantic recognition results that 49% of consumers’ comments involve multiple topics, indicating that consumers have a pretty clear cognition of the complex status of the BDPD. Importantly, this study found some topics that had not been focused on in previous studies, such as more than 8% of consumers calling for government and legal departments to regulate BDPD behavior, which indicates that quite enough consumers are losing confidence in the self-discipline of the platform enterprises. Another interesting result is that consumers who pursue solutions to the BDPD belong to two mutually exclusive groups: government protection and self-protection. The significance of this study is that it reminds the e-commerce platforms to pay attention to the potential harm for consumers’ psychology while bringing additional profits through the BDPD. Otherwise, the negative consumer attitudes may cause damage to brand image, business reputation, and the sustainable development of the platforms themselves. It also provides the government supervision departments an advanced analysis method reference for more effective administration to protect social fairness. (shrink)

We propose a new approach for tracing value change. Value change may lead to a mismatch between current value priorities in society and the values for which technologies were designed in the past, such as energy technologies based on fossil fuels, which were developed when sustainability was not considered a very important value. Better anticipating value change is essential to avoid a lack of social acceptance and moral acceptability of technologies. While value change can be studied historically and qualitatively, we (...) propose a more quantitative approach that uses large text corpora. It uses probabilistic topic models, which allow us to trace values that are latent. We demonstrate the approach for five types of value change in technology. Our approach is useful for testing hypotheses about value change, such as verifying whether value change has occurred and identifying patterns of value change. The approach can be used to trace value change for various technologies and text corpora, including scientific articles, newspaper articles, and policy documents. (shrink)

Topic modelling is a popular unsupervised method for text processing that provides interpretable document representation. One of the most high-level approaches is additively regularized topic models (ARTM). This method features better quality than other methods due to its flexibility and advanced regularization abilities. However, it is challenging to find an optimal learning strategy to create high-quality topics because a user needs to select the regularizers with their values and determine the order of application. Moreover, it may require many (...) real runs or model training which makes this task time consuming. At the current moment, there is a lack of research on parameter optimization for ARTM-based models. Our work proposes an approach that formalizes the learning strategy into a vector of parameters which can be solved with evolutionary approach. We also propose a surrogate-based modification which utilizes machine learning methods that makes the approach for parameters search time efficient. We investigate different optimization algorithms (evolutionary and Bayesian) and their modifications with surrogates in application to topic modelling optimization using the proposed learning strategy approach. An experimental study conducted on English and Russian datasets indicates that the proposed approaches are able to find high-quality parameter solutions for ARTM and substantially reduce the execution time of the search. (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)

How can the development of ideas in a scientific field be studied over time? We apply unsupervised topic modeling to the ACL Anthology to analyze historical trends in the field of Computational Linguistics from 1978 to 2006. We induce topic clusters using Latent Dirichlet Allocation, and examine the strength of each topic over time. Our methods find trends in the field including the rise of probabilistic methods starting in 1988, a steady increase in applications, and a sharp (...) decline of research in semantics and understanding between 1978 and 2001, possibly rising again after 2001. We also introduce a model of the diversity of ideas, topic entropy, using it to show that COLING is a more diverse conference than ACL, but that both conferences as well as EMNLP are becoming broader over time. Finally, we apply Jensen-Shannon divergence of topic distributions to show that all three conferences are converging in the topics they cover. (shrink)

A persuasion dialogue is a dialogue in which a conflict between agents with respect to their points of view arises at the beginning of the talk and the agents have the shared, global goal of resolving the conflict and at least one agent has the persuasive aim to convince the other party to accept an opposing point of view. I argue that the persuasive force of argument may have not only extreme values but also intermediate strength. That is, I wish (...) to introduce two additional types of the effects of persuasion in addition to successful and unsuccessful ones (cf. Van Eemeren and Houtlosser in Argumentation 14(3):293–305, 2000; Advances in pragma-dialectics. Sic Sat, Amsterdam, 2002; Walton in A pragmatic theory of fallacy. University of Alabama Press, Tuscaloosa, 1995; Walton and Krabbe in Commitment in dialogue: basic concepts of interpersonal reasoning. State University of New York Press, Albany, New York, 1995). I propose a model which provides for modified versions of the standpoint of an agent needed in order to bring about two possible outcomes of a persuasion dialogue. These two outcomes I label partially-successful and over-successful. I call the potential, not yet verbalised, standpoint of an agent here the original topic t. Based on some aspects of relevance theory (Sperber and Wilson in Relevance: communication and cognition. Blackwell, Oxford, 1986; Wilson and Sperber in The handbook of pragmatics. Blackwell Publishing, Malden, 2006), I explain that the modified version of the original topic t is an implicature created from the original topic t and from a specific mental topic which belongs to, what I call the beneficial cognitive model (hence BCM). I define BCMi,t as a set of topics which are within the area of agent i’s interest of persuasion with respect to t. (shrink)

Once a central topic of philosophy of science, scientific explanation attracted less attention in the last two decades. My aim in this paper is to argue for a newsort of approach towards scientific explanation. In a first step I propose a classification of different approaches through a set of dichotomic characteristics. Taken into account the tendencies in actual philosophy of science I see a local, dynamic and non-theory driven approach as a plausible one. Considering models as bearers of explanations (...) will provide a proper frame for such an approach. In the second part I make some suggestions for a working agenda that will further articulate a sketchy account of explanation through models proposed by Hartmann and Frigg. (shrink)

Medieval literature is argumentative, since it argues for an idealized vision of reality acceptable to a proposed audience. Its narrative mode is description, performed according to the principles of the art of topical invention, derived from Cicero's De Inventione. The topoi or loci are features (circumstantiae) of a person or thing that are common to it as a class, such as tempus or locus for things. When filled out, according to the point of view desired by the author, public, context, (...) etc., they become the attributes (argumenta) of a particular human being or action.According to the author, all descriptions should be interpreted by reference to such a technique of topical invention, a method which will allow new explanations of the texts. The examples of the locus amoenus in various Latin and French works show how traditional and conventional models were adapted and specialized, by various devices, to fit new formal or conceptual intentions and new contexts. The examples and models proposed to the student learning composition by Masters such as Matthew of Vendôme, were given not to be copied, but to be imitated through topical invention, that is adaptation to a particular intention, through specializing devices. (shrink)

This dissertation investigates some philosophical issues using computational models. Chapter 1 presents a Lewis-Skyrms signaling game that can exhibit a type of compositionality novel to the signaling game literature. The structure of the signaling game is motivated by an analogy to the alarm calls of putty-nosed monkeys (Cercopithecus nictitans). Putty-nosed monkeys display a compositional system of alarm calls with a semantics that is sensitive to the ordering of terms. This sensitivity to the ordering of terms has not been previously modeled (...) with a Lewis-Skyrms signaling game literature. Signaling games are valued for showing how communicative systems can arise with minimal learning tools. Simulation results show that basic (Roth-Erev) reinforcement learning is sufficient for the acquisition of a compositional signaling system sensitive to the ordering of terms. Chapter 2 investigates social epistemology in the context of an effect of cognitive biases called the illusory truth effect. The illusory truth effect is exhibited when repeated exposure to a statement increases an individual's credence in that statement. While most investigations of the illusory truth effect focus on individuals' belief formation, humans typically form beliefs within a social structure. This is particularly relevant because various social structures can give rise to repeated exposure to statements; e.g. a popular book might recurringly be discussed in one's social circle, or one or two foundational papers might always be cited by a particular lab. So, how does the illusory truth effect influence learning and belief formation in a group? This chapter uses network models to investigate this question. These models show that the illusory truth effect can be very detrimental to a group's belief formation. The effect causes networks to prematurely settle on a belief, in part, through repeated exposure to data points that are not independently generated. Previous research has indicated that the probability of such failures is near zero when networks are large or scientists are forced to explore unpopular and risky science. However, simulation results show that the harmful consequences of the illusory truth effect are robust even in large networks with mandatory exploration. Finally, Chapter 3 shows how a rudimentary type of abstraction can obtain in Lewis-Skyrms signaling games. Here, abstraction is understood as occurring when different particulars take on the same functional role. Some abstraction may be guided by innate biases, and the chapter develops an analogy of reasoning about strategic thinking in chess to highlight some epistemic concerns that are raised by the presentation of abstraction. Concretely, the signaling game models of this chapter are developed in the context of two tasks that are much simpler than strategizing in chess. The first task is a transitive inference task that has been substantially studied in both humans and non-human animals. General features of the models developed for the transitive inference task are then carried over to a model for the second task of learning rudimentary grammatical structures. This second task is based on studies of human infants and non-human primates' ability to learn ``nonsense grammars". Closing discussion highlights some strengths of the abstraction exhibited in the Lewis-Skyrms signaling game models. (shrink)

This paper focuses on the inferential configuration of arguments, generally referred to as argument scheme. After outlining our approach, denominated Argumentum Model of Topics (AMT, see Rigotti and Greco Morasso 2006, 2009; Rigotti 2006, 2008, 2009), we compare it to other modern and contemporary approaches, to eventually illustrate some advantages offered by it. In spite of the evident connection with the tradition of topics, emerging also from AMT’s denomination, its involvement in the contemporary dialogue on argument schemes should not (...) be overlooked. The model builds in particular on the theoretical and methodological perspective of pragma-dialectics in its extended version, reconciling dialectic and rhetoric; nevertheless, it also takes into account numerous other contributions to the study of argument schemes. Aiming at a representation of argument schemes able to monitor the inferential cohesion and completeness of arguments, AMT focuses on two components of argument scheme that could be distinguished, readapting pragma-dialectical terms, as procedural and material respectively. The procedural component is based on the semantic-ontological structure, which generates the inferential connection from which the logical form of the argument is derived. The material component integrates into the argument scheme the implicit and explicit premises bound to the contextual common ground (Rigotti 2006). In this paper, the comparison of the AMT to other approaches focuses on the inferential configuration of arguments and not on the typologies of argument schemes and on the principles they are based on, which the authors intend to tackle in a further paper. (shrink)

The traditional way of presenting mathematical knowledge is logical deduction, which implies a monolithic structure with topics in a strict hierarchical relationship. Despite many recent developments and methodical inventions in mathematics education, many curricula are still close in spirit to this hierarchical structure. However, this organisation of mathematical ideas may not be the most conducive way for learning mathematics. In this paper, we suggest that flattening curricula by developing self-contained micro topics and by providing multiple entry points to knowledge by (...) making the dependency graph of notions and subfields as sparse as possible could improve the effectiveness of teaching mathematics. We argue that a less strictly hierarchical schedule in mathematics education can decrease mathematics anxiety and can prevent students from ‘losing the thread’ somewhere in the process. This proposal implies a radical re-evaluation of standard teaching methods. As such, it parallels philosophical deconstruction. We provide two examples of how the micro topics can be implemented and consider some possible criticisms of the method. A full-scale and instantaneous change in curricula is neither feasible nor desirable. Here, we aim to change the prevalent attitude of educators by starting a conversation about the flat curriculum alternative. (shrink)

There is a conceptual crisis in the biomedical sciences that is particularly salient in psychopathology research. Underlying the crisis is a controversy that pertains to the current medical model of disease that largely draws from causal-mechanistic explanations. The bedrock of this model is the analysis of biological part-dysfunctions that aims at unequivocally defining a pathological condition and demarcating it from its neighboring entities. This endeavor has led to a quest for physiological, biochemical, and genetic signatures. Yet, so far (...) there is little evidence for reliable biomarkers for any mental disorder. The contemporary biomedical paradigm largely ignores historical, dynamic, and system-level aspects—a view that has contributed much to the conceptual disjunction of the patient as a person from his/her disease. Notwithstanding the impressive progress in the biomedical sciences, increasingly more critics question whether the constituting framework is sufficient to convey a comprehensive understanding of illnesses, especially mental illnesses. Thus, the medical model urgently requires an update. But rather than revamping it by methodological advancements, it will be necessary to critically review its philosophical roots. The most problematic issues that require reworking are: the preponderance of the biostatistical theory; the undue decoupling of physiological from evolutionary explanations of function; the clinging to the Modern Synthesis ; and the neglect of dynamic and system-level properties. The proposed overhaul requires the heeding of historical explanations that draw from the Extended Evolutionary Synthesis as well as systems biology approaches for tackling multilevel and dynamic phenomena of complex systems. (shrink)

Statistical topic models provide a general data - driven framework for automated discovery of high-level knowledge from large collections of text documents. Although topic models can potentially discover a broad range of themes in a data set, the interpretability of the learned topics is not always ideal. Human-defined concepts, however, tend to be semantically richer due to careful selection of words that define the concepts, but they may not span the themes in a data set exhaustively. In this (...) study, we review a new probabilistic framework for combining a hierarchy of human-defined semantic concepts with a statistical topic model to seek the best of both worlds. Results indicate that this combination leads to systematic improvements in generalization performance as well as enabling new techniques for inferring and visualizing the content of a document. (shrink)

Conspiracy theories play a troubling role in political discourse. Online forums provide a valuable window into everyday conspiracy theorizing, and can give a clue to the motivations and interests of those who post in such forums. Yet this online activity can be difficult to quantify and study. We describe a unique approach to studying online conspiracy theorists which used non-negative matrix factorization to create a topic model of authors' contributions to the main conspiracy forum on Reddit. This subreddit (...) provides a large corpus of comments which spans many years and numerous authors. We show that within the forum, there are multiple sub-populations distinguishable by their loadings on different topics in the model. Further, we argue, these differences are interpretable as differences in background beliefs and motivations. The diversity of the distinct subgroups places constraints on theories of what generates conspiracy theorizing. We argue that traditional “monological” believers are only the tip of an iceberg of commenters. Neither simple irrationality nor common preoccupations can account for the observed diversity. Instead, we suggest, those who endorse conspiracies seem to be primarily brought together by epistemological concerns, and that these central concerns link an otherwise heterogenous group of individuals. (shrink)

Topics in Cognitive Science, Volume 14, Issue 2, Page 388-399, April 2022.

In this paper we look at the manual analysis of arguments and how this compares to the current state of automatic argument analysis. These considerations are used to develop a new approach combining a machine learning algorithm to extract propositions from text, with a topic model to determine argument structure. The results of this method are compared to a manual analysis.

Conducting emotion analysis and generating users' feedback from social media platforms may help understand their emotional responses to video products, such as a documentary on the lockdown of Wuhan during COVID-19. The results of emotion analysis could be used to make further user recommendations for marketing purposes. In our study, we try to understand how users respond to a documentary through YouTube comments. We chose “The lockdown: One month in Wuhan” YouTube documentary, and applied emotion analysis as well as a (...) machine learning approach to the comments. We first cleaned the data and then introduced an emotion analysis based on the statistical characteristics and lexicon combination. After that, we applied the Latent Dirichlet Allocation topic modeling approach to further generate main topics with keywords from the comments and visualized the distribution by visualizing the topics. The result shows trust, joy, and anticipation are the most prominent emotions dominating the comments. The major three themes, which account for 70% of all comments, are discussing stories about fighting against the virus, medical workers being heroes, and medical workers being respected. Further discussion has been conducted on the changing of different sentiments over time for the ongoing health crisis. This study proves that emotion analysis and LDA topic modeling could be used to generate explanations of users' opinions and feelings about video products, which could support user recommendations in marketing. (shrink)

In this brief commentary of Kamila Debowska-Kozlowska’s insightful analysis of persuasive outcomes (Processing topics from the Beneficial Cognitive Model in partially and over-successful persuasion dialogues. Argumentation, 2014), I articulate some suggestions for future development of her ideas. My main claim is that, while instances of partially and over-successful persuasion are indeed worthy of further theoretical inquiry, the topical analysis proposed by Debowska-Kozlowska may benefit from integration with other approaches.

As supposed in the last Editorial (HYLE, 5-1, p. 78), our special topic ‘Models in Chemistry’ has attracted new attention to the philosophy of chemistry. Only during the past couple of month, the number of visitors of the HYLE website has nearly doubled to some 1,600 per month. There is nothing comparable in the whole field of philosophy of science, as there is no other science having such a lot to catch up on philosophical work. At the same time, (...) this means a great challenge to meet the expectation of such a large and heterogeneous readership. (shrink)

Molecular models are typical topics of chemical research depending on the technical standards of observation, computation, and representation. Mathematically, molecular structures have been represented by means of graph theory, topology, differential equations, and numerical procedures. With the increasing capabilities of computer networks, computational models and computer-assisted visualization become an essential part of chemical research. Object-oriented programming languages create a virtual reality of chemical structures opening new avenues of exploration and collaboration in chemistry. From an epistemic point of view, virtual reality (...) is a new computer-assisted tool of human imagination and recognition. (shrink)

This monograph offers a critical introduction to current theories of how scientific models represent their target systems. Representation is important because it allows scientists to study a model to discover features of reality. The authors provide a map of the conceptual landscape surrounding the issue of scientific representation, arguing that it consists of multiple intertwined problems. They provide an encyclopaedic overview of existing attempts to answer these questions, and they assess their strengths and weaknesses. The book also presents a (...) comprehensive statement of their alternative proposal, the DEKI account of representation, which they have developed over the last few years. They show how the account works in the case of material as well as non-material models; how it accommodates the use of mathematics in scientific modelling; and how it sheds light on the relation between representation in science and art. The issue of representation has generated a sizeable literature, which has been growing fast in particular over the last decade. This makes it hard for novices to get a handle on the topic because so far there is no book-length introduction that would guide them through the discussion. Likewise, researchers may require a comprehensive review that they can refer to for critical evaluations. This book meets the needs of both groups. (shrink)

Abstract While agreeing that dynamical models play a major role in cognitive science, we reject Stepp, Chemero, and Turvey's contention that they constitute an alternative to mechanistic explanations. We review several problems dynamical models face as putative explanations when they are not grounded in mechanisms. Further, we argue that the opposition of dynamical models and mechanisms is a false one and that those dynamical models that characterize the operations of mechanisms overcome these problems. By briefly considering examples involving the generation (...) of action potentials and circadian rhythms, we show how decomposing a mechanism and modeling its dynamics are complementary endeavors. (shrink)

Communication within groups of agents has been lately the focus of research in dynamic epistemic logic. This paper studies a recently introduced form of partial (more precisely, topic-based) communication. This type of communication allows for modelling scenarios of multi-agent collaboration and negotiation, and it is particularly well-suited for situations in which sharing all information is not feasible/advisable. The paper can be divided into two parts. In the first part, we present results on invariance and complexity of model checking. (...) Moreover, we compare partial communication with the public announcement and arrow update settings in terms of both language-expressivity and update-expressivity. Regarding the former, the three settings are equivalent, their languages being equally expressive. Regarding the latter, all three modes of communication are incomparable in terms of update-expressivity. In the second part, we shift our attention to strategic topic-based communication. We do so by extending the language with a modality that quantifies over the topics the agents can ‘talk about’, thus allowing a form of arbitrary partial communication. For this new framework, we provide a complete axiomatisation, showing also that the new language’s model checking problem is PSPACE-complete. Finally, we argue that, in terms of expressivity, this new language of arbitrary partial communication is incomparable to that of arbitrary public announcements and also to that of arbitrary arrow updates. (shrink)