The effect of pre-exposure on family resemblance categorization for stimuli of varying levels of perceptual difficulty

This study investigated the effect that pre-exposure to a set of stimuli has on the prevalence of family resemblance categorization. 64 participants were tested to examine the effect that pre-exposure type (same-stimuli vs unrelated-stimuli) and the perceptual difficulty of the stimuli (perceptually similar vs perceptually different) has on categorization strategy. There was a significant effect of perceptual difficulty, indicating that perceptually different stimuli evoked a higher level of family resemblance sorting than perceptually similar stimuli. There was no significant main effect of pre-exposure type; however, there was a significant interaction between pre-exposure type and level of perceptual difficulty. Post-hoc tests revealed that this interaction was the result of an increase in family resemblance sorting for the perceptually different stimuli under relevant preexposure but no such effect for perceptually similar stimuli. The theoretical implications of these findings are discussed

Pragmatics and word meaning

In this paper, we explore the interaction between lexical semantics and pragmatics. We argue that linguistic processing is informationally encapsulated and utilizes relatively simple ‘taxonomic’ lexical semantic knowledge. On this basis, defeasible lexical generalisations deliver defeasible parts of logical form. In contrast, pragmatic inference is open-ended and involves arbitrary real-world knowledge. Two axioms specify when pragmatic defaults override lexical ones. We demonstrate that modelling this interaction allows us to achieve a more refined interpretation of words in a discourse context than either the lexicon or pragmatics could do on their own.</jats:p

Leveraging a semantically annotated corpus to disambiguate prepositional phrase attachment

Accurate parse ranking requires semantic information, since a sentence may have many candidate parses involving common syntactic constructions. In this paper, we propose a probabilistic frame- work for incorporating distributional semantic information into a maximum entropy parser. Further- more, to better deal with sparse data, we use a modified version of Latent Dirichlet Allocation to smooth the probability estimates. This LDA model generates pairs of lemmas, representing the two arguments of a semantic relation, and can be trained, in an unsupervised manner, on a corpus anno- tated with semantic dependencies. To evaluate our framework in isolation from the rest of a parser, we consider the special case of prepositional phrase attachment ambiguity. The results show that our semantically-motivated feature is effective in this case, and moreover, the LDA smoothing both produces semantically interpretable topics, and also improves performance over raw co-occurrence frequencies, demonstrating that it can successfully generalise patterns in the training data.This is the final version of the article. It first appeared from Association for Computational Linguistics via http://www.aclweb.org/anthology/W15-0101

Semantic transfer in Verbmobil

This paper is a detailed discussion of semantic transfer in the context of the Verbmobil Machine Translation project. The use of semantic transfer as a translation mechanism is introduced and justified by comparison with alternative approaches. Some criteria for evaluation of transfer frameworks are discussed and a comparison is made of three different approaches to the representation of translation rules or equivalences. This is followed by a discussion of control of application of transfer rules and interaction with a domain description and inference component

Functional Distributional Semantics

Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena. We propose a novel probabilistic framework which draws on both formal semantics and recent advances in machine learning. In particular, we separate predicates from the entities they refer to, allowing us to perform Bayesian inference based on logical forms. We describe an implementation of this framework using a combination of Restricted Boltzmann Machines and feedforward neural networks. Finally, we demonstrate the feasibility of this approach by training it on a parsed corpus and evaluating it on established similarity datasets

What is the evidence of the impact of microfinance on the well-being of poor people?

The concept of microcredit was first introduced in Bangladesh by Nobel Peace Prize winner Muhammad Yunus. Professor Yunus started Grameen Bank (GB) more than 30 years ago with the aim of reducing poverty by providing small loans to the country’s rural poor (Yunus 1999). Microcredit has evolved over the years and does not only provide credit to the poor, but also now spans a myriad of other services including savings, insurance, remittances and non-financial services such as financial literacy training and skills development programmes; microcredit is now referred to as microfinance (Armendáriz de Aghion and Morduch 2005, 2010). A key feature of microfinance has been the targeting of women on the grounds that, compared to men, they perform better as clients of microfinance institutions and that their participation has more desirable development outcomes (Pitt and Khandker 1998). Despite the apparent success and popularity of microfinance, no clear evidence yet exists that microfinance programmes have positive impacts (Armendáriz de Aghion and Morduch 2005, 2010; and many others). There have been four major reviews examining impacts of microfinance (Sebstad and Chen, 1996; Gaile and Foster 1996, Goldberg 2005, Odell 2010, see also Orso 2011). These reviews concluded that, while anecdotes and other inspiring stories (such as Todd 1996) purported to show that microfinance can make a real difference in the lives of those served, rigorous quantitative evidence on the nature, magnitude and balance of microfinance impact is still scarce and inconclusive (Armendáriz de Aghion and Morduch 2005, 2010). Overall, it is widely acknowledged that no well-known study robustly shows any strong impacts of microfinance (Armendáriz de Aghion and Morduch 2005, p199-230). Because of the growth of the microfinance industry and the attention the sector has received from policy makers, donors and private investors in recent years, existing microfinance impact evaluations need to be re-investigated; the robustness of claims that microfinance successfully alleviates poverty and empowers women must be scrutinised more carefully. Hence, this review revisits the evidence of microfinance evaluations focusing on the technical challenges of conducting rigorous microfinance impact evaluations

Approaches to evidence synthesis in international development : a research agenda

Abstract: This paper discusses the spectrum of synthesis methods available to generate, explore and text theory, their value to the field of international development and innovations required to make better use of the primary research available. It argues for clearer distinctions between syntheses produced as public goods, and those tailored to specific circumstances; and strengthening knowledge systems through greater use of maps to navigate existing and missing evidence, harmonised outcomes and measures, and advances in automation technologies. Improved methods and guidance are required for synthesising formative research and investigating contextual factors. Engaging stakeholders and working across academic disciplines support the production of policy‐relevant syntheses and inspire methods development

Open-Source Machine Translation with DELPH-IN

The Deep Linguistic Processing with HPSG Initiative (DELH-IN) provides the infrastructure needed to produce open-source semantic transfer-based machine translation systems. We have made available a prototype Japanese-English machine translation system built from existing resources include parsers, generators, bidirectional grammars and a transfer engine