Seeing clearly: Frame Semantic, Psycholinguistic, and Cross ...

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CHAPTER 4. PSYCHOLINGUISTIC EXPERIMENTS 140 If they are shown the same set of sentences again in a random order and are also provided with an a priori set of categories, { to what extent will their categorizations be similar to each other? { to what extent will their categorizations be similar to those of the experimenters for the same sentences? We chose actual sentences from a corpus as the best approximation to natural uses of see which could conveniently be used as experimental stimuli. Method Subjects The subjects were 9 undergraduates at the University of California at Berkeley, who received credit toward their introductory psychology courses for their participating in the experiments. All subjects were native speakers of American English with little or no training in Linguistics. Materials The stimuli for Experiment 1 consisted of two blocks of 100 sentences selected at random from the Brown corpus, together with 44 constructed example sentences. (All the constructed sentences, along with a random sample of 50 corpus sentences are listed in Appendix A.) For the sorting task, each block of 100 sentences was printed on 3x5 inch cards, forming two sets, Set 1 and Set 2. A set of cards was also prepared for the 44 constructed example sentences. For the classi cation task, a set of Perl programs and web forms were written to display the material. Nineteen senses were used in the classi cation task; the de nitions and examples are given in the Appendix on page 256, along with the names we used for them at that time. The same pool of corpus and constructed sentences were used in both tasks. Procedure Each subject participated in two tasks, Sorting and Classi cation. One hour was allocated for each task.
CHAPTER 4. PSYCHOLINGUISTIC EXPERIMENTS 141 Task 1: Sorting In this task, subjects were given cards with one sentence containing see and asked to read each sentence one at a time and place it in a pile with sentences containing the same sense of see. They were instructed to ignore grammatical factors such as tense and voice, and to use as many piles as they wanted. Subjects were also asked to write a brief de nition or characterization of each group. Each subject sorted 100 corpus examples, followed by the 44 constructed examples, followed (if time permitted) by the remaining 100 corpus examples. To control for the in uence of the particular examples in the rst part of the sorting, the rst 100 sentences given to each subject were either all from Set 1, all from Set 2, or half from each set. Task 2: Classi cation Each subject sat at a computer on which the materials were displayed using HTML and a web browser. First the subjects read the directions and saw the list of de nitions and examples. Then the sentences were presented one at a time at the top of the screen, with the list of senses displayed below. Subjects chose the sense which best matched the use in the sentence by clicking next to it and then clicked again to go on to the next sentence. Both the response and the response latency were recorded, but the task was not paced, and subjects could refer back to the de nitions and examples whenever they wanted, either on screen or on a printed handout. All subjects completed the same 99 sentences (1 block of trials) within one hour. Statistical Measures of Agreement Three di erent measures of agreement were used in analyzing the results of the experiments reported here, simple agreement, kappa, and omega. Simple proportion of agreement with a \gold standard" is obviously the easiest to interpret, but it has serious limitations. It does not re ect agreements among raters that do not match the standard. For example, suppose in an experiment on perception of dialects, recordings of three speakers, A, B, and C, are played to a total of 12 subjects (called \raters"), who must decide which speakers are from New York, which from Chicago and which from Philadelphia. Several possible outcomes of such an experiment are shown in the four parts of Table 4.1.
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Seeing clearly: Frame Semantic, Psy
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Seeing clearly: Frame Semantic, Psy
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Next three psycholinguistic experim
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BasicSenses........................
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Acknowledgements Careful citations
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acquisition. Aside from needing to
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Chapter 1 Introduction Xue er bu s,
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CHAPTER 1. INTRODUCTION 3 In lingui
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CHAPTER 1. INTRODUCTION 5 Although
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CHAPTER 1. INTRODUCTION 7 part on t
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CHAPTER 1. INTRODUCTION 9 is not cl
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CHAPTER 1. INTRODUCTION 11 1980:10-
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CHAPTER 1. INTRODUCTION 13 L.U. Lex
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CHAPTER 1. INTRODUCTION 15 spondenc
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CHAPTER 1. INTRODUCTION 19 shows th
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CHAPTER 1. INTRODUCTION 21 Regular
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CHAPTER 1. INTRODUCTION 23 base for
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CHAPTER 1. INTRODUCTION 25 d. The i
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CHAPTER 1. INTRODUCTION 27 (12) a.
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CHAPTER 1. INTRODUCTION 29 linguist
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CHAPTER 1. INTRODUCTION 31 Introspe
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CHAPTER 1. INTRODUCTION 33 on the o
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CHAPTER 1. INTRODUCTION 35 they con
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CHAPTER 1. INTRODUCTION 37 meaning
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Chapter 2 A Frame Semantic Analysis
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CHAPTER 2. A FRAME SEMANTIC ANALYSI
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CHAPTER 6. FUTURE RESEARCH DIRECTIO
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BIBLIOGRAPHY 239 Bierwisch, Manfred
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BIBLIOGRAPHY 241 ||, & B.T.S. Atkin
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BIBLIOGRAPHY 243 ||. 1990. The inva
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BIBLIOGRAPHY 245 Osgood, C.E. 1970.
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BIBLIOGRAPHY 247 Wasow, Thomas. 198
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Appendix A Additional Corpus Exampl
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APPENDIX A. ADDITIONAL CORPUS EXAMP
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APPENDIX A. ADDITIONAL CORPUS EXAMP
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APPENDIX B. SUMMARY OF MORPHOLOGY A
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APPENDIX C. EXPERIMENT 1 257 Recogn
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APPENDIX C. EXPERIMENT 1 259 to lea
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APPENDIX C. EXPERIMENT 1 261 At the
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Appendix D Stimuli for Experiments
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APPENDIX D. STIMULI FOR EXPERIMENTS
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Index activation, 13, 136, 137, 231
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INDEX 277 description, 82 see proce
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INDEX 279 173{178, 186, 190{192, 20
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