What makes a word easy to learn? Early‐learned words are frequent and tend to name concrete referents. But words typically do not occur in isolation. Some words are predictable from their contexts; others are less so. Here, we investigate whether predictability relates to when children start producing different words (age of acquisition; AoA). We operationalized predictability in terms of a word's surprisal in child‐directed speech, computed using n‐gram and long‐short‐term‐memory (LSTM) language models. Predictability derived from LSTMs was generally a (...) better predictor than predictability derived from n‐gram models. Across five languages, average surprisal was positively correlated with the AoA of predicates and function words but not nouns. Controlling for concreteness and word frequency, more predictable predicates and function words were learned earlier. Differences in predictability between languages were associated with cross‐linguistic differences in AoA: the same word (when it was a predicate) was produced earlier in languages where the word was more predictable. (shrink)

Traditional flat-structured bigram and trigram models of phonotactics are useful because they capture a large number of facts about phonological processes. Additionally, these models predict that local interactions should be easier to learn than long-distance ones because long-distance dependencies are difficult to capture with these models. Long-distance phonotactic patterns have been observed by linguists in many languages, who have proposed different kinds of models, including feature-based bigram and trigram models, as well as precedence models. Contrary to flat-structured bigram and trigram (...) models, these alternatives capture unbounded dependencies because at an abstract level of representation, the relevant elements are locally dependent, even if they are not adjacent at the observable level. Using an artificial grammar learning paradigm, we provide additional support for these alternative models of phonotactics. Participants in two experiments were exposed to a long-distance consonant-harmony pattern in which the first consonant of a five-syllable word was [s] or [∫] (“sh”) and triggered a suffix that was either [-su] or [-∫u] depending on the sibilant quality of this first consonant. Participants learned this pattern, despite the large distance between the trigger and the target, suggesting that when participants learn long-distance phonological patterns, that pattern is learned without specific reference to distance. (shrink)

We examined how language supports the expression of temporality within sentence boundaries in English, which has a rich inventory of grammatical means to express temporality. Using a computational model that mimics how humans learn from exposure we explored what the use of different tense and aspect combinations reveals about the interaction between our experience of time and the cognitive demands that talking about time puts on the language user. Our model was trained on n-grams extracted from the BNC (...) to select the TA combination that fits the context best. It revealed the existence of two different sub-systems within the set of TA combinations, a “simplex” one that is supported lexically and is easy to learn, and a “complex” one that is supported contextually and is hard to learn. The finding that some TA combinations are essentially lexical in nature necessitates a rethink of tense and aspect as grammatical categories that form the axes of the temporal system. We argue that the system of temporal reference may be more fruitfully thought of as the result of learning a system that is steeped in experience and organised along a number of functional principles. (shrink)

Piantadosi, Tily, and Gibson analyzed a large‐scale web‐scraping corpus (the Google 1T dataset) and reported that word length is independently predicted from average information content (surprisal) calculated by a 2‐ to 4‐gram model (hereafter, longer‐span surprisal) across 11 Indo‐European languages, namely, Czech, Dutch, English, French, German, Italian, Polish, Spanish, Portuguese, Romanian, and Swedish. However, a recent article by Meylan and Griffiths suggested the importance of preprocessing for studies with large‐scale corpora and reanalyzed the same databases. After their preprocessing, (...) the results in Piantadosi et al. were not replicated in Czech, Romanian, and Swedish. Additionally, a German‐specific study by Koplenig, Kupietz, and Wolfer showed that the strict analysis did not replicate the result in Piantadosi et al. for that language with the preprocessing suggested by Meylan and Griffiths in a large‐scale but less noisy database. These three studies provide evidence from 11 Indo‐European languages and one Afro‐Asiatic language, Hebrew, as relevant in this debate. However, we do not have evidence from other linguistic groups. This study provides evidence about Japanese based on a strict preprocessing of Google's web‐scraping database. The results show that Japanese word length can be predicted independently by 2‐ to 4‐gram surprisal. (shrink)

Numerous studies have examined the acoustic correlates of sentential stress and its underlying linguistic functionality. However, the mechanism that connects stress cues to the listener's attentional processing has remained unclear. Also, the learnability versus innateness of stress perception has not been widely discussed. In this work, we introduce a novel perspective to the study of sentential stress and put forward the hypothesis that perceived sentence stress in speech is related to the unpredictability of prosodic features, thereby capturing the attention of (...) the listener. As predictability is based on the statistical structure of the speech input, the hypothesis also suggests that stress perception is a result of general statistical learning mechanisms. To study this idea, computational simulations are performed where temporal prosodic trajectories are modeled with an n-gram model. Probabilities of the feature trajectories are subsequently evaluated on a set of novel utterances and compared to human perception of stress. The results show that the low-probability regions of F0 and energy trajectories are strongly correlated with stress perception, giving support to the idea that attention and unpredictability of sensory stimulus are mutually connected. (shrink)

Cet article propose une réflexion à la fois théorique et méthodologique sur les objets de la phraséologie étendue, qui s’intéresse à des unités préfabriquées du discours au-delà des critères de figement. Plus précisément, nous tentons de clarifier le concept général de motif, ainsi que celui, plus spécifique, de routine discursive. Nous proposons ensuite de comparer deux approches méthodologiques différentes pour l’identification des routines en corpus : une méthode hiérarchique, basée sur le repérage d’arbres lexico-syntaxiques récurrents (ALR), et la méthode séquentielle (...) classique des segments répétés (SR) ou n-grams. Nous montrons, au travers d’une étude sur corpus, que la méthode des ALR présente un réel intérêt pour le repérage des routines et des collocations, mais que les SR semblent plus adaptés et plus simples à mettre en œuvre pour des locutions figées ou des constructions syntaxiques impliquant des lexèmes grammaticaux – le modèle syntaxique des ALR nécessitant une adaptation pour pouvoir identifier ces cas. (shrink)

We discuss two named-entity recognition models which use characters and character n-grams either exclusively or as an important part of their data representation. The first model is a character-level HMM with minimal context information, and the second model is a maximum-entropy conditional markov model with substantially richer context features. Our best model achieves an overall F1 of 86.07% on the English test data (92.31% on the development data). This number represents a 25% error reduction over the (...) same model without word-internal (substring) features. (shrink)

Language research has come to rely heavily on large‐scale, web‐based datasets. These datasets can present significant methodological challenges, requiring researchers to make a number of decisions about how they are collected, represented, and analyzed. These decisions often concern long‐standing challenges in corpus‐based language research, including determining what counts as a word, deciding which words should be analyzed, and matching sets of words across languages. We illustrate these challenges by revisiting “Word lengths are optimized for efficient communication” (Piantadosi, Tily, & Gibson, (...) 2011), which found that word lengths in 11 languages are more strongly correlated with their average predictability (or average information content) than their frequency. Using what we argue to be best practices for large‐scale corpus analyses, we find significantly attenuated support for this result and demonstrate that a stronger relationship obtains between word frequency and length for a majority of the languages in the sample. We consider the implications of the results for language research more broadly and provide several recommendations to researchers regarding best practices. (shrink)

The study aims to help characterize the sort of structures about which people can acquire unconscious knowledge. It is already well established that people can implicitly learn n-grams and also repetition patterns. We explore the acquisition of unconscious structural knowledge of symmetry. Chinese Tang poetry uses a specific sort of mirror symmetry, an inversion rule with respect to the tones of characters in successive lines of verse. We show, using artificial poetry to control both n-gram structure and repetition patterns, (...) that people can implicitly learn to discriminate inversions from non-inversions, presenting a challenge to existing models of implicit learning. (shrink)

Twitter is a virtual social network where people share their posts and opinions about the current situation, such as the coronavirus pandemic. It is considered the most significant streaming data source for machine learning research in terms of analysis, prediction, knowledge extraction, and opinions. Sentiment analysis is a text analysis method that has gained further significance due to social networks’ emergence. Therefore, this paper introduces a real-time system for sentiment prediction on Twitter streaming data for tweets about the coronavirus pandemic. (...) The proposed system aims to find the optimal machine learning model that obtains the best performance for coronavirus sentiment analysis prediction and then uses it in real-time. The proposed system has been developed into two components: developing an offline sentiment analysis and modeling an online prediction pipeline. The system has two components: the offline and the online components. For the offline component of the system, the historical tweets’ dataset was collected in duration 23/01/2020 and 01/06/2020 and filtered by #COVID-19 and #Coronavirus hashtags. Two feature extraction methods of textual data analysis were used, n-gram and TF-ID, to extract the dataset’s essential features, collected using coronavirus hashtags. Then, five regular machine learning algorithms were performed and compared: decision tree, logistic regression, k-nearest neighbors, random forest, and support vector machine to select the best model for the online prediction component. The online prediction pipeline was developed using Twitter Streaming API, Apache Kafka, and Apache Spark. The experimental results indicate that the RF model using the unigram feature extraction method has achieved the best performance, and it is used for sentiment prediction on Twitter streaming data for coronavirus. (shrink)

Key n-grams are useful in the analysis of legal discourse as they help bring recurrent key expressions to the fore and understand the patterning of legal language. This paper aims to generate, analyse and compare the key n-grams of two legal corpora: a corpus of European directives on distance consumer contracts and a UK national legislation corpus on the same subject-matter. The corpora are considered, alternatively, as both focus and reference corpora. In this way, keyness, i.e., the terminology that makes (...) each corpus unique, is revealed from both corpora. The paper findings mostly bring to the fore five different patterns: differences in the key n-grams due to institutional or country-related factors; legalese influences; typical n-grams of Eurolect; dichotomy in the terminology used (albeit applying the same legal principles), and polysemy (i.e., similar words with different applications in various genres). This analysis confirms the usefulness and insightfulness of key n-grams in understanding the impact of disciplinary conventions in legal language. (shrink)

The ongoing generation of expectations is fundamental to listeners’ experience of music, but research into types of statistical information that listeners extract from musical melodies has tended to emphasize transition probabilities and n‐grams, with limited consideration given to other types of statistical learning that may be relevant. Temporal associations between scale degrees represent a different type of information present in musical melodies that can be learned from musical corpora using expectation networks, a computationally simple method based on activation and decay. (...) Expectation networks infer the expectation of encountering one scale degree followed in the near (but not necessarily immediate) future by another given scale degree, with previous work suggesting that scale degree associations learned by expectation networks better predict listener ratings of pitch similarity than transition probabilities. The current work outlines how these learned scale degree associations can be combined to predict melodic continuations and tests the resulting predictions on a dataset of listener responses to a musical cloze task previously used to compare two other models of melodic expectation, a variable‐order Markov model (IDyOM) and Temperley's music‐theoretically motivated model. Under multinomial logistic regression, all three models explain significant unique variance in human melodic expectations, with coefficient estimates highest for expectation networks. These results suggest that generalized scale degree associations informed by both adjacent and nonadjacent relationships between melodic notes influence listeners’ melodic predictions above and beyond n‐gram context, highlighting the need to consider a broader range of statistical learning processes that may underlie listeners’ expectations for upcoming musical events. (shrink)

Before the end of the first year of life, infants begin to lose the ability to perceive distinctions between sounds that are not phonemic in their native language. It is typically assumed that this developmental change reflects the construction of language-specific phoneme categories, but how these categories are learned largely remains a mystery. Peperkamp, Le Calvez, Nadal, and Dupoux (2006) present an algorithm that can discover phonemes using the distributions of allophones as well as the phonetic properties of the allophones (...) and their contexts. We show that a third type of information source, the occurrence of pairs of minimally differing word forms in speech heard by the infant, is also useful for learning phonemic categories and is in fact more reliable than purely distributional information in data containing a large number of allophones. In our model, learners build an approximation of the lexicon consisting of the high-frequency n-grams present in their speech input, allowing them to take advantage of top-down lexical information without needing to learn words. This may explain how infants have already begun to exhibit sensitivity to phonemic categories before they have a large receptive lexicon. (shrink)

In this paper, we conduct an in-depth study of Japanese keyword extraction from news reports, train external computer document word sets from text preprocessing into word vectors using the Ship-gram model in the deep learning tool Word2Vec, and calculate the cosine distance between word vectors. In this paper, the sliding window in TextRank is designed to connect internal document information to improve the in-text semantic coherence. The main idea is to use not only the statistical and structural features (...) of words but also the semantic features of words extracted through word-embedding techniques, i.e., multifeature fusion, to obtain the importance weights of words themselves and the attraction weights between words and then iteratively calculate the final weight of each word through the graph model algorithm to determine the extracted keywords. To verify the performance of the algorithm, extensive simulation experimental studies were conducted on three different types of datasets. The experimental results show that the proposed keyword extraction algorithm can improve the performance by a maximum of 6.45% and 20.36% compared with the existing word frequency statistics and graph model methods, respectively; MF-Rank can achieve a maximum performance improvement of 1.76% compared with PW-TF. (shrink)

The article discusses the possible “physical” meaning of the distance between genetic sequences, based on comparing the set of all words of fixed length occurring in two genomic sequences. The considered distances suitable describe phylogenetic relationships and allow ranking by the genomes similarities in situations where it is practically impossible to provide by alignment methods. A simulation shows that the distances between the N-gram distributions change almost linearly, with genome lengths growing for relatively small artificial evolutionary modifications. In the (...) general case of comparing two genetic texts, a function for “calibrating” the distance between N-gram distributions is found. This fact makes it possible to interpret the considered distances by means of the number of elementary operators performed in an alignment process between the compared sequences. (shrink)

동서철학 비교연구서. 헤겔, 하이데거 등의 독일 정통철학의 핵심과 퇴계 율곡 등의 한국 전통철학의 정수 를 그 독자성과 세계성, 그리고 연결성의 측면에서 비교 고찰한 논문집.

This article critically evaluates Itzhak Gilboa, Andrew Postlewaite, Larry Samuelson, and David Schmeidler’s account of economic models. First, it gives a selective overview of their argument, highlighting their emphasis on similarity and oversight of the role of idealizations in economics. Second, it proposes a sketch of an account of models as arguments and argumentative devices. This account not only sheds light on Gilboa et al.’s approach, including its shortcomings, but also identifies key challenges in model-based inference, suggesting a fresh (...) perspective on the uses of models in economics for diverse objectives. (shrink)

The paper is devoted to a conceptual model of cell patterning, based on a generalized notion of the epigenetic code of a cell determining its state. We introduce the concept of signaling depending both upon the spatial distance between cells and the distance between their cell states (s-distance); signaling can repel cells in the space of cell states (s-space) or attract them. The influence of different types of repelling signaling on the evolution of cells is considered. Stabilizing signaling, namely (...) a signaling monotonically decreasing with s-distance, causes the restoring of cell states after perturbations; destabilizing signaling, i.e., the one in which the signaling monotonically increases with s-distance, causes the appearance of pairs of cells with alternating cell states (one close to the state conventionally called “head”, and another close to the “tail” state). Non-monotonic (in s-space) signaling splits the cells into groups. The model shows that different types of signaling may provide different types of cellular patterns. General principles for applying this model to complex cellular structures are discussed. (shrink)

Van der Henst argues that the theory of mental models lacks a pragmatic component. He fills the gap with the notion that reasoners draw the most relevant conclusions. We agree, but argue that theories need an element of “nondeterminism.” It is often impossible to predict either what will be most relevant or which particular conclusion an individual will draw.

Automated Software Testing (AST) using Model Checking is in this article epistemologically analysed in order to argue in favour of a model-based reasoning paradigm in computer science. Preliminarily, it is shown how both deductive and inductive reasoning are insufficient to determine whether a given piece of software is correct with respect to specified behavioural properties. Models algorithmically checked in Model Checking to select executions to be observed in Software Testing are acknowledged as analogical models which establish isomorphic (...) relations with the target system’s data set. Analogical models developed in AST are presented as abductive models providing hypothetical explanations to observed executions. The model assumption—algorithmic check—software testing process is understood as the abduction—deduction—induction process defining the selective abduction and turned to isolate a set of model-based hypotheses concerning the target system behaviours. A manipulative abduction process is finally recognized in the practice of adapting, abstracting and refining models that do not provide successful predictions. (shrink)

ABSTRACTEmotion regulation comprises attempts to influence when and how emotions are experienced and expressed. It has mostly been conceived of as proactive or reactive, but it may also be retroactive and involve memory. I term such past-oriented activity mnemonic emotion regulation and propose that it involves increasing or decreasing access to or altering the characteristics of a memory. People may increase access to a memory and make it more likely that it will be retrieved in the future, for example by (...) rehearsing a pleasant memory. They may decrease access to a memory and make it less likely that intrusions will be experienced in the future, for example by repeatedly trying to stop an unpleasant memory from being retrieved. Finally, people may alter the characteristics of a memory and change its content or context, for example by replacing a negative impression with a productive interpretation. I discuss how mnemonic emotion regulation may be i... (shrink)

I develop and test a multilevel trust-based model of ethical public leadership, which links ethical leadership, trust and leadership outcomes both within and across organizational levels. I examine how both ethical leadership and trust relate to employee well-being and satisfaction, group organizational citizenship behaviour and perceived organizational performance. The findings, based on data collected from an online quantitative survey conducted in three local councils of the north east of England, provide evidence in support of positive relationships between ethical leadership (...) and employees’ trust in leaders at multiple levels. This trust is in turn shown to influence employees’ attitudes, behaviours and cognitions. (shrink)

Qur'an courses for the 4-6 age group took their place among religious education activities with a pilot application in 2012-2013. Since then, the literature on the Qur'an course for the 4-6 age group continues to be enriched by various research. The book titled Evaluation of 4-6 Years Qur'an Course Teaching Program (Stufflebeam CIPP Model) was written to give feedback on the teaching program and fill the gap in the literature. With this study, for the first time, 4-6-year-old course teaching (...) programs were evaluated with the Stufflebeam CIPP model in context, input, process, and product dimensions. Although there are areas for improvement in the research, the study is important as it gives information about the model in question and accelerates program development studies. (shrink)

One of the five pillars of the Islam, zakat (alms) was made necessary after the hijra. Although Zakat was one of the most important resources of Islamic state treasury for many years, it was excluded from the state authority in the period of Omar bin Abdul-Aziz. In recent years, it has been seen that some Muslim-majority states have made significant attempts to manage zakat under the control of the state. For example; Indonesia, Pakistan, Bangladesh, Sudan, and Nigeria are very important (...) states in terms of zakat application in İslamic world and Malaysia, a Southeast Asian country, comes first among those. Especially after 1990, Malaysia activated a new zakat management. This study deals with the Malaysian zakat system in the context of the state administration of zakat. The aim of the study is to examine the Malaysian zakat system in a comprehensive way. In the study, the history of zakat system in Malaysia, the current zakat models applied in there and the areas to which zakat is distributed are investigated. As a qualitative research method, document analysis was employed in the study. In this context, many international studies were examined which are directly related to the subject, and twenty-five Malaysian websites belonging to the institutions which collect and distribute the funds were also scanned. It has been found that Malaysian zakat system consists of public model, semi-public model and private model and seen that the states are divided into three regions and different zakat models are applied in each region. (shrink)

L’article présente les premiers résultats de l’analyse de la plus récente version des n-grammes de Google italiens et français (GN, 2020), dans le cadre d’un projet qui vise à construire, à partir des données brutes, un ensemble de dictionnaires électroniques disponibles en libre accès qui faciliteraient l’utilisation des GN pour la recherche diachronique en morphologie lexicale. L’analyse de dizaines de millions de n-grammes révèle que les GN italiens et français contiennent respectivement 2,7 et 3,7 millions de formes de mots uniques (...) avec l’information sur leur fréquence dans les livres publiés, en fonction de l’année de publication du livre correspondant sur une période d’environ 500 ans. L’article décrit également la procédure d’extraction des données concernant les mots complexes dont les composants sont écrits séparément. (shrink)

Correct interpretation of the data from integral laboratory tests, including Thrombin Generation Test, requires biochemistry-based mathematical models of blood coagulation. The purpose of this study is to describe the experimental TGT data from healthy donors and hemophilia A and B patients. We derive a simplified ODE model and apply it to analyze the TGT data from healthy donors and HA/HB patients with in vitro added tissue factor pathway inhibitor antibody. This model allows the characterization of hemophilia patients in (...) the space of three most important model parameters. The proposed approach may provide a new quantitative tool for the analysis of experimental TGT. Also, it gives a reduced model of coagulation verified against clinical data to be used in future theoretical large-scale modeling of thrombosis in flow. (shrink)

We introduce Kripke models for propositional substructural logics with strong negation, and show the completeness theorems for these logics using an extended Ishihara's canonical model construction method. The framework presented can deal with a broad range of substructural logics with strong negation, including a modified version of Nelson's logic N$^-$, Wansing's logic COSPL, and extended versions of Visser's basic propositional logic, positive relevant logics, Corsi's logics and M\'endez's logics.

Webb's scheme for classifying behavioral models is applicable to a wide range of theories and simulations, nonrobotic as well as robotic. It is suggested that a meta-analysis of existing models, characterized according to the proposed scheme, could identify regions of the seven-dimensional modelling space that are particularly likely to lead to new insights in understanding behavior.

We give an explicit solution of a model Boltzmann kinetic equation describing a gas between two walls maintained at different temperatures. In the model, which is essentially one-dimensional, there is a probability for collisions to reverse the velocities of particles traveling in opposite directions. Particle number and speeds (but not momentum) are collision invariants. The solution, which depends on the stochastic collision kernels at the walls, has a linear density profile and the energy flux satisfies Fourier's law.

We present a dynamic programming model which is used to investigate hypothermia as an adaptive response by small passerine birds in winter. The model predicts that there is a threshold function of reserves during the night, below which it is optimal to enter hypothermia, and above which it is optimal to rest. This threshold function decreases during the night, with a particularly sharp drop at the end of the night, representing the time and energy costs associated with returning (...) to normal body temperature. The results of the model emphasise the trade-off between energy and predation, not just between foraging options, but also between foraging during the day and entering hypothermia at night. The value of being able to use hypothermia represents not just energy savings, but also reduced predation risk due to changes in the optimal foraging strategy. Conditions which give a high value of hypothermia are short photoperiod, variable food supply, low temperatures, poor and scarce food supplies. (shrink)

The goal of the present study was to elucidate what influences medical students’ attitudes and interests in medical ethics. At the end of their first, fifth and last terms, 409 medical students from all six medical schools in Sweden participated in an attitude survey. The questions focused on the students’ experience of good and poor role models, attitudes towards medical ethics in general and perceived effects of the teaching of medical ethics. Despite a low response rate at some schools, this (...) study indicates that increased interest in medical ethics was related to encountering good physician role models, and decreased interest, to encountering poor role models. Physicians involved in the education of medical students seem to teach medical ethics as role models even when ethics is not on the schedule. The low response rate prevents us from drawing definite conclusions, but the results could be used as hypotheses to be further scrutinised. (shrink)