Semantic similarity and the comparison of word meanings.

Schaeffer, Benson; Wallace, R. Jay

doi:10.1037/h0028287

Cited by 72 publications

(31 citation statements)

References 4 publications

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“…This replicates the finding of Schaeffer and Wallace (1970) and, more importantly, demonstrates that the proximity measures of categories obtained in Experiment I can predict latencies in a task using exemplars of those categories.…”

Section: Resultssupporting

confidence: 74%

“…Recall that in this task, subjects are presented with a pair of words and are required to determine if both words belong to the same category or not. Using this paradigm, Schaeffer and Wallace (1970) demonstrated that the time required to respond "different" increased with the semantic similarity of the two items in the test pair. Here it was assumed that two items were similar (e.g., robin, dog) when their direct superordinates (e.g., birds and mammals) were themselves dominated by the same superordinate (e.g., animal); otherwise the items were considered dissimilar.…”

Section: Experiments IIImentioning

confidence: 99%

“…Yet the similarity relations between various categories can be of critical concern in semantic memory research. To illustrate, consider the same-different task introduced by Schaeffer and Wallace (1970). A subject is presented a word pair and is instructed to respond "same" if both words are drawn from the identical category (e.g., hawk, dove), and "different" otherwise (e.g., hawk, salmon).…”

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confidence: 99%

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Cross-category structure in semantic memory

et al. 1975

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Section: Resultssupporting

confidence: 74%

Section: Experiments IIImentioning

confidence: 99%

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confidence: 99%

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Cross-category structure in semantic memory

et al. 1975

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“…The proposed interplay between STM and LTM might be initially to access categorical information from LTM (categorical search), with the resulting contents searched in STM. Thus, the category processing constant estimated in the present experiment (about 40 msec/category) might be composed of a number of substages, e.g., search and comparison, where the search locates the categories and the comparison involves a matching of associates between the test item and the category being processed (Schaeffer & Wallace, 1969, 1970. The best match would result in that category being transferred into STM, where its contents would be checked for a match with the test item.…”

Section: Theoretical Modifications Of Memory Search Potential Contribmentioning

confidence: 99%

Organization and long-term memory search

Homa

1973

Memory & Cognition

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A recognition RT paradigm was used to assess a number of plausible search strategies in LTM for categorized lists. List length was determined by factorially combining two, three, or five categories with two, three, or five words per category, and test items could be one of four types: (1) positive, (2) a repeated positive, (3) a related negative, or (4) an unrelated negative. It was found that RT increased linearly with category size for both positive and related negatives (about 30-40 msec/item); whereas the increase was much smaller for the unrelated negatives, especially for three and five categories (about 9 rnsec/item). With an increase in the number of categories, RT increased at the rate of about 40 msec/category for all three test items. A theory of high-speed scanning for categorized material was proposed in which a serial and exhaustive search-of the categories is rust undertaken. If a positive category match is found, a serial and exhaustive search within the contents of the positive category is initiated; if no category match is found, the search is simply terminated. Some evidence was presented that categories recently probed may provide for a short-circuiting of the category search.Over 100 years ago, Donders (1868) suggested a method for studying the characteristics underlying simple mental processes, e.g., time required to detect a stimulus, time required to discriminate between two or more stimuli, etc. Donders's method contained two main assumptions: (1) Mental events require "real" time, i.e., their execution is neither instantaneous nor is the event itself too brief for measurement, and (2) complex mental operations are composed of simpler substages which occur serially and whose time components are additive. After some initially promising results, the second assumption was criticized by Kulpe (1896), leading to a diminution of interest in the Donderian additive model.Recently, however, the additive model was revitalized by Sternberg (1966Sternberg ( , 1969 in his study of retrieval processes from short-term memory (STM). Ss were required to make one response (e.g., pull a lever) whenever a stimulus from a predefined set was presented and to make a different response otherwise (e.g., pull a different lever). The size of the positive set was varied from one to six items on any particular trial. Sternberg interpreted the results as indicating that retrieval of information from STM was serial and exhaustive; the search was serial because reaction time (RT) increased linearly with the size of the positive set and exhaustive because of the equivalence of the slopes for the positive and negative stimuli. From the slope constant, it was hypothesized that each item of the positive set was successively compared to the test stimulus at a rate of about 40 msec per item.

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“…They have been used to discover the structure of semantic memory (Meyer, 1970;Schaeffer & Wallace, 1970) and to analyze cognitive processes. The most prominent feature models are Tversky's contrast and ratio models (1987,1977,1978,1982).…”

Section: Feature Modelsmentioning

confidence: 99%

A Hybrid Semantic Similarity Measure for Spatial Information Retrieval

Schwering

Kuhn

2009

Spatial Cognition & Computation

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Semantic similarity is central to many cognitive processes and plays an important role in the way humans process and reason about information. In particular, the retrieval of knowledge from memory hinges crucially on similarity. Likewise, information retrieval systems use similarity to detect relevant information for a given query. Current information retrieval systems apply mainly syntactic techniques to determine similarity. Although such syntactic similarity measures have performed strongly with resources containing large amounts of text, they cannot appropriately cope with syntactic and semantic heterogeneity and ambiguity, if the semantics of the terms is not explicitly available. Therefore, they are rather rigid and inflexible as they cannot adapt to the user's requirements and conceptualization of the domain. Furthermore, geographic features are distinguished via their geometric and thematic data. It is often not possible to capture the complex semantics of geographic features by a single name or a textual description. Therefore spatial data is different from text documents typically found in enterprise databases or on the Web. Retrieval of spatial information requires new, intelligent retrieval mechanisms that satisfy its specific requirements. A semantics-based solution can more easily adapt to user needs and therefore increases the flexibility and usability of spatial data and retrieval methods. This paper investigates the suitability of various approaches -originally developed to explain human similarity judgement -in the context of spatial information retrieval. We propose a new, hybrid approach for semantic similarity measurement, which can represent the complex semantics of spatial data. It allows for retrieving relevant data by determining the similarity between the query and the semantic descriptions of geographic feature types within the database. The hybrid similarity measure combines the geometric structure of conceptual spaces with the relational structure of semantic nets to one, cognitively plausible knowledge representation with an inherent similarity measure.

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Semantic similarity and the comparison of word meanings.

Cited by 72 publications

References 4 publications

Cross-category structure in semantic memory

Cross-category structure in semantic memory

Organization and long-term memory search

A Hybrid Semantic Similarity Measure for Spatial Information Retrieval

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