1,934 research outputs found

    The Effects of Age-of-Acquisition on Ambiguity Resolution: Evidence from Eye Movements

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    Words that are rated as acquired earlier in life receive shorter fixation durations than later acquired words, even when word frequency is adequately controlled (Juhasz & Rayner, 2003; 2006). Some theories posit that age-of-acquisition (AoA) affects the semantic representation of words (e.g., Steyvers & Tenenbaum, 2005), while others suggest that AoA should have an influence at multiple levels in the mental lexicon (e.g. Ellis & Lambon Ralph, 2000). In past studies, early and late AoA words have differed from each other in orthography, phonology, and meaning, making it difficult to localize the influence of AoA. Two experiments are reported which examined the locus of AoA effects in reading. Both experiments used balanced ambiguous words which have two equally-frequent meanings acquired at different times (e.g. pot, tick). In Experiment 1, sentence context supporting either the early- or late-acquired meaning was presented prior to the ambiguous word; in Experiment 2, disambiguating context was presented after the ambiguous word. When prior context disambiguated the ambiguous word, meaning AoA influenced the processing of the target word. However, when disambiguating sentence context followed the ambiguous word, meaning frequency was the more important variable and no effect of meaning AoA was observed. These results, when combined with the past results of Juhasz and Rayner (2003; 2006) suggest that AoA influences access to multiple levels of representation in the mental lexicon. The results also have implications for theories of lexical ambiguity resolution, as they suggest that variables other than meaning frequency and context can influence resolution of noun-noun ambiguities

    Determining Intent: A Quantitative and Qualitative Linguistic Analysis of Holographic and Professional Wills

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    My dissertation focuses on the language of wills. More specifically, I am interested in how the language of holographic wills (i.e., handwritten wills) differs from the language of professional wills.My research question is Do linguistic differences between professional wills and holographic wills have the potential to affect the interpretation of the wills, subsequently influencing the outcome of the probate process? In order to address this question, I conduct a quantitative and qualitative contrastive corpus analysis of holographic and professionally-prepared wills. My hypothesis is that the discourse of holographic wills will tend to be more narrative-like, reflecting personal experiences and emotions. By contrast, the language of professional wills is more formal and rule-driven than the language of holographic wills. By using computational analysis tools such as the Gramulator, my dissertation identifies specific language differencesbetween these two text types that support my hypothesis. These differences are assessed through a variety of statistical methods. Additionally, I perform a qualitative assessment of three case studies on individual wills using discourse analysis approaches to provide insight into why and how the meaning of the text may be determined. Although both types of discourse have their differences, their main goal is the same: to convey the testator\u27s intent. The purpose of my dissertation is to facilitate this goal by demonstrating to the legal community how non-professionals write their wills so that when a controversy over a holographic will arises, the legal community can apply the methods and techniques presented here and determine the testator\u27s intent, since by law, this is what is required

    The Effects of Age-of-Acquisition on Ambiguity Resolution: Evidence from Eye Movements

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    Words that are rated as acquired earlier in life receive shorter fixation durations than later acquired words, even when word frequency is adequately controlled (Juhasz & Rayner, 2003; 2006). Some theories posit that age-of-acquisition (AoA) affects the semantic representation of words (e.g., Steyvers & Tenenbaum, 2005), while others suggest that AoA should have an influence at multiple levels in the mental lexicon (e.g. Ellis & Lambon Ralph, 2000). In past studies, early and late AoA words have differed from each other in orthography, phonology, and meaning, making it difficult to localize the influence of AoA. Two experiments are reported which examined the locus of AoA effects in reading. Both experiments used balanced ambiguous words which have two equally-frequent meanings acquired at different times (e.g. pot, tick). In Experiment 1, sentence context supporting either the early- or late-acquired meaning was presented prior to the ambiguous word; in Experiment 2, disambiguating context was presented after the ambiguous word. When prior context disambiguated the ambiguous word, meaning AoA influenced the processing of the target word. However, when disambiguating sentence context followed the ambiguous word, meaning frequency was the more important variable and no effect of meaning AoA was observed. These results, when combined with the past results of Juhasz and Rayner (2003; 2006) suggest that AoA influences access to multiple levels of representation in the mental lexicon. The results also have implications for theories of lexical ambiguity resolution, as they suggest that variables other than meaning frequency and context can influence resolution of noun-noun ambiguities

    Moving beyond Kucera and Francis: a critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English

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    Word frequency is the most important variable in research on word processing and memory. Yet, the main criterion for selecting word frequency norms has been the availability of the measure, rather than its quality. As a result, much research is still based on the old Kucera and Francis frequency norms. By using the lexical decision times of recently published megastudies, we show how bad this measure is and what must be done to improve it. In particular, we investigated the size of the corpus, the language register on which the corpus is based, and the definition of the frequency measure. We observed that corpus size is of practical importance for small sizes (depending on the frequency of the word), but not for sizes above 16-30 million words. As for the language register, we found that frequencies based on television and film subtitles are better than frequencies based on written sources, certainly for the monosyllabic and bisyllabic words used in psycholinguistic research. Finally, we found that lemma frequencies are not superior to word form frequencies in English and that a measure of contextual diversity is better than a measure based on raw frequency of occurrence. Part of the superiority of the latter is due to the words that are frequently used as names. Assembling a new frequency norm on the basis of these considerations turned out to predict word processing times much better than did the existing norms (including Kucera & Francis and Celex). The new SUBTL frequency norms from the SUBTLEXUS corpus are freely available for research purposes from http://brm.psychonomic-journals.org/content/supplemental, as well as from the University of Ghent and Lexique Web sites

    Symbolic and Cognitive Theory in Biology

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    In previous work, I have looked in detail at the capacity and the limits of the linguistics model as applied to gene expression. The recent use of a primitive applied linguistic model in Apple's SIRI system allows further analysis. In particular, the failings of this system resemble those of the HGP; the model used also helps point out the shortcomings of the concept of the "gene". This is particularly urgent as we are entering an era of applied biology in the absence of theory, and indeed an era with a near-epidemic of retracted papers. There are a few workarounds proposed. One is to add to the nascent field of biosemiotics a more explicit concern with syntax. At the time of writing, Apple is being sued for false advertising of its iphone 4s, with the associated claim that apple had solved many of the problems of natural language processing by computer (nlpbc). The system was bought by Apple from a company called SIRI, and in turn was based on the notion, trumpeted by the prior art in a company called Dejima, that nlpbc could be done by keywords alone. Yet the hype resembles nothing so much as the misrepresentation of the Human Genome Project (HGP) fed to the media in the glory days at the beginning of this millennium, and it says a lot for the status of scientists in society that they have avoided Apple's fate. In this paper, a short review of several current themes in theoretical and applied biology is first proposed. Then the tensions implicit in the notion that the "gene" is simultaneously to be identified as a unit of inheritance and spatially located over spatially well-defined nucleotides is explored and the notion is found to be incoherent. An expanded notion of inheritance is proposed in the context of a focus on inheritance as necessarily involving species, population and organism over time. While it is premature to talk about a paradigm shift, it is certainly arguable that biology urgently needs a sophisticated theory of how symbols work substantially more sophisticated than that implicit in the HGP; Biosemiotics affords a framework in which this might be tried. Indeed, as this paper concludes, there may yet be room for a "Bionoetics", a perspective in which biological explanation can be extended to include cognition in all its forms. Finally, a working sketch of a modeling environment written in LISP, one that shows promise in reflecting the complexities discussed in the paper, is included

    Experience-based language acquisition: a computational model of human language acquisition

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    Almost from the very beginning of the digital age, people have sought better ways to communicate with computers. This research investigates how computers might be enabled to understand natural language in a more humanlike way. Based, in part, on cognitive development in infants, we introduce an open computational framework for visual perception and grounded language acquisition called Experience-Based Language Acquisition (EBLA). EBLA can “watch” a series of short videos and acquire a simple language of nouns and verbs corresponding to the objects and object-object relations in those videos. Upon acquiring this protolanguage, EBLA can perform basic scene analysis to generate descriptions of novel videos. The general architecture of EBLA is comprised of three stages: vision processing, entity extraction, and lexical resolution. In the vision processing stage, EBLA processes the individual frames in short videos, using a variation of the mean shift analysis image segmentation algorithm to identify and store information about significant objects. In the entity extraction stage, EBLA abstracts information about the significant objects in each video and the relationships among those objects into internal representations called entities. Finally, in the lexical acquisition stage, EBLA extracts the individual lexemes (words) from simple descriptions of each video and attempts to generate entity-lexeme mappings using an inference technique called cross-situational learning. EBLA is not primed with a base lexicon, so it faces the task of bootstrapping its lexicon from scratch. The performance of EBLA has been evaluated based on acquisition speed and accuracy of scene descriptions. For a test set of simple animations, EBLA had average acquisition success rates as high as 100% and average description success rates as high as 96.7%. For a larger set of real videos, EBLA had average acquisition success rates as high as 95.8% and average description success rates as high as 65.3%. The lower description success rate for the videos is attributed to the wide variance in entities across the videos. While there have been several systems capable of learning object or event labels for videos, EBLA is the first known system to acquire both nouns and verbs using a grounded computer vision system
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