A Generalized Syllogistic Inference System based on Inclusion and Exclusion Relations

We introduce a simple inference system based on two primitive relations between terms, namely, inclusion and exclusion relations. We present a normalization theorem, and then provide a characterization of the structure of normal proofs. Based on this, inferences in a syllogistic fragment of natural language are reconstructed within our system. We also show that our system can be embedded into a fragment of propositional minimal logic.     

Language Shift and the Inclusion of Indigenous Populations in Large-Scale Assessment Programs

Indicators of academic achievement for bilingual students can be inaccurate due to linguistic heterogeneity. For indigenous populations, language shift (the gradual replacement of one language by another) is a factor that can increase this heterogeneity and poses an additional challenge for valid testing. We investigated whether and how indigenous populations can be validly included in a large-scale assessment program. We gave Mexican preschool Mayan students aged 5 to 6 years the same set of mathematics items in three versions: (1) original in Spanish, (2) Mayan translation, and (3) content equivalent, developed from scratch in Mayan. Also, we collected information on the students’ and the teachers’ use of Spanish and Mayan, and on the communities’ support of the two languages. Students performed poorly on the three versions. Generalizability theory-based analyses revealed considerable performance inconsistency across items and language versions and low generalizability and dependability coefficients. This performance instability appears to stem from a dwindling support of the Mayan language in Mayan schools and communities. Fair, valid assessment of indigenous populations in either their languages or their countries’ dominant languages appears to be difficult to accomplish with current testing models and policies.     

The Faith of the Outsider: Exclusion and Inclusion in the Biblical Story – Frank Anthony Spina

Book reviewed:
The Faith of the Outsider: Exclusion and Inclusion in the Biblical Story , Frank Anthony Spina, Eerdmans Publishing Company 2005 (0-8028-2864-7), x + 206 pp., pb £9.99/$16.00     

自然语言处理中的哲学问题

分析了哲学上的理性主义和经验主义及其在自然语言处理的研究中的影响,对比了自然语言处理中基于规则的理性主义方法和基于统计的经验主义方法的优点和缺点,主张把这两种方法结合起来以推动自然处理研究的发展。本文还分析了哲学中知识本体的概念发展到知识本体工程的过程,说明了哲学对于自然语言处理的深刻影响。     

Explanatory Exclusion and Causal Exclusion

Given Kim’s principle of explanatory exclusion (EE), it follows that in addition to the problem of mental causation, dualism faces a problem of mental explanation. However, the plausibility of EE rests upon the acceptance of a further principle concerning the individuation of explanation (EI). The two methods of defending EI—either by combining an internal account of the individuation of explanation with a semantical account of properties or by accepting an external account of the individuation of explanation—are both metaphysically implausible. This is not, however, to reject the problem of mental explanation, for EE can be replaced with a far weaker principle, which does not require the acceptance of EI, but which generates a similar problem for dualism.

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Exclusion Learning and Emergent Symbolic Category Formation in Individuals With Severe Language Impairments and Intellectual Disabilities

The Psychological Record - We evaluated formation of simple symbolic categories from initial learning of specific dictated word-picture relations through emergence of untaught or derived relations....     

Traditional Logic,Modern Logic and Natural Language

In a recent paper Johan van Benthem reviews earlier work done by himself and colleagues on ‘natural logic’. His paper makes a number of challenging comments on the relationships between traditional logic, modern logic and natural logic. I respond to his challenge, by drawing what I think are the most significant lines dividing traditional logic from modern. The leading difference is in the way logic is expected to be used for checking arguments. For traditionals the checking is local, i.e. separately for each inference step. Between inference steps, several kinds of paraphrasing are allowed. Today we formalise globally: we choose a symbolisation that works for the entire argument, and thus we eliminate intuitive steps and changes of viewpoint during the argument. Frege and Peano recast the logical rules so as to make this possible. I comment also on the traditional assumption that logical processing takes place at the top syntactic level, and I question Johan’s view that natural logic is ‘natural’.     

Language and Operations in Children's Class Inclusion Reasoning: The Operational Semantic Theory of Reasoning

In this article we critique two prominent theories of reasoning—mental logic and mental models—and argue that reasoning does not consist of either applying logical rules or constructing mental models. Instead, we propose anoperational semantictheory of reasoning, according to which reasoning is based on children's operational understanding of key terms in a given problem. We then go on to consider an important recent developmental theory of reasoning, fuzzy-trace theory. In order to illustrate the view of reasoning proposed here we report a study of class inclusion. Dramatic differences in class inclusion performance were found as the result of linguistic context; performance was significantly higher when an explicit request for a subclass comparison preceded the class inclusion question as compared to a standard condition when the class inclusion question alone was asked. This was the case, however, only when the prior subclass comparison question referred to the same dimensions as the class inclusion question and not when irrelevant subclasses were referred to. Children's performance was also better when they sorted the materials into the supraordinate class as compared to the subclasses, but not when the experimenter sorted them for the child. These effects due to the operational and linguistic manipulations are discussed in terms of a generaloperational semantictheory of reasoning.     

语言——自然选择的一种适应

该文认为人类的语言能力是一种生物学意义上的适应,是自然选择的产物。人类的语言与自然界中其它复杂的生物学适应相类似,也具有特定的机能。语言的特定机能在于编码叙事性信息并与他人分享,这与人类认知活动中因果推理所具有的高度社会性特点相一致。两个新的研究领域,进化博弈理论和分子进化选择理论,都证实了语言是一种受到自然选择的进化适应     

Incremental Bayesian Category Learning From Natural Language

Models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper, we focus on categories acquired from natural language stimuli, that is, words (e.g., chair is a member of the furniture category). We present a Bayesian model that, unlike previous work, learns both categories and their features in a single process. We model category induction as two interrelated subproblems: (a) the acquisition of features that discriminate among categories, and (b) the grouping of concepts into categories based on those features. Our model learns categories incrementally using particle filters, a sequential Monte Carlo method commonly used for approximate probabilistic inference that sequentially integrates newly observed data and can be viewed as a plausible mechanism for human learning. Experimental results show that our incremental learner obtains meaningful categories which yield a closer fit to behavioral data compared to related models while at the same time acquiring features which characterize the learned categories. (An earlier version of this work was published in Frermann and Lapata 2014 .)     

自然语言处理预测抑郁症的技术陷阱与道德风险

抑郁症患者倾向于在网络社交平台上发布带有抑郁信号的推文。基于这些文字信息，借助自然语言处理进行分析，提取归纳用户的语言特征，可以预测潜在用户的抑郁症状况。由于隐私信息的敏感、相关技术的不成熟等原因，出现了诸如信息获取与隐私侵犯、算法偏见与信息误判、信息权利与信息利益、责任界定与权限模糊等现实问题，成为进一步发展的掣肘。进行算法技术升级、完善法律法规、加强行业伦理约束等是避免道德风险的重要措施。     

Fast Mapping and Exclusion (Emergent Matching) in Developmental Language,Behavior Analysis,and Animal Cognition Research

The Psychological Record - Researchers from behavior analytic, developmental, and comparative perspectives have all investigated the conditions under which new arbitrary (symbolic)...     

Analyzing Machine-Learned Representations: A Natural Language Case Study

As modern deep networks become more complex, and get closer to human-like capabilities in certain domains, the question arises as to how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations of sentences in one such artificial system for natural language processing. We first present a diagnostic test dataset to examine the degree of abstract composable structure represented. Analyzing performance on these diagnostic tests indicates a lack of systematicity in representations and decision rules, and reveals a set of heuristic strategies. We then investigate the effect of training distribution on learning these heuristic strategies, and we study changes in these representations with various augmentations to the training set. Our results reveal parallels to the analogous representations in people. We find that these systems can learn abstract rules and generalize them to new contexts under certain circumstances—similar to human zero-shot reasoning. However, we also note some shortcomings in this generalization behavior—similar to human judgment errors like belief bias. Studying these parallels suggests new ways to understand psychological phenomena in humans as well as informs best strategies for building artificial intelligence with human-like language understanding.