7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      LADEC: The Large Database of English Compounds

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The Large Database of English Compounds (LADEC) consists of over 8,000 English words that can be parsed into two constituents that are free morphemes, making it the largest existing database specifically for use in research on compound words. Both monomorphemic (e.g., wheel) and multimorphemic (e.g., teacher) constituents were used. The items were selected from a range of sources, including CELEX, the English Lexicon Project, the British Lexicon Project, the British National Corpus, and Wordnet, and were hand-coded as compounds (e.g., snowball). Participants rated each compound in terms of how predictable its meaning is from its parts, as well as the extent to which each constituent retains its meaning in the compound. In addition, we obtained linguistic characteristics that might influence compound processing (e.g., frequency, family size, and bigram frequency). To show the usefulness of the database in investigating compound processing, we conducted a number of analyses that showed that compound processing is consistently affected by semantic transparency, as well as by many of the other variables included in LADEC. We also showed that the effects of the variables associated with the two constituents are not symmetric. In short, LADEC provides the opportunity for researchers to investigate a number of questions about compounds that have not been possible to investigate in the past, due to the lack of sufficiently large and robust datasets. In addition to directly allowing researchers to test hypotheses using the information included in LADEC, the database will contribute to future compound research by allowing better stimulus selection and matching.

          Related collections

          Most cited references58

          • Record: found
          • Abstract: not found
          • Article: not found

          Lexical storage and retrieval of prefixed words

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Explaining human performance in psycholinguistic tasks with models of semantic similarity based on prediction and counting: A review and empirical validation

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Emotion and language: valence and arousal affect word recognition.

              Emotion influences most aspects of cognition and behavior, but emotional factors are conspicuously absent from current models of word recognition. The influence of emotion on word recognition has mostly been reported in prior studies on the automatic vigilance for negative stimuli, but the precise nature of this relationship is unclear. Various models of automatic vigilance have claimed that the effect of valence on response times is categorical, an inverted U, or interactive with arousal. In the present study, we used a sample of 12,658 words and included many lexical and semantic control factors to determine the precise nature of the effects of arousal and valence on word recognition. Converging empirical patterns observed in word-level and trial-level data from lexical decision and naming indicate that valence and arousal exert independent monotonic effects: Negative words are recognized more slowly than positive words, and arousing words are recognized more slowly than calming words. Valence explained about 2% of the variance in word recognition latencies, whereas the effect of arousal was smaller. Valence and arousal do not interact, but both interact with word frequency, such that valence and arousal exert larger effects among low-frequency words than among high-frequency words. These results necessitate a new model of affective word processing whereby the degree of negativity monotonically and independently predicts the speed of responding. This research also demonstrates that incorporating emotional factors, especially valence, improves the performance of models of word recognition. PsycINFO Database Record (c) 2014 APA, all rights reserved.
                Bookmark

                Author and article information

                Contributors
                cgagne@ualberta.ca
                Journal
                Behav Res Methods
                Behav Res Methods
                Behavior Research Methods
                Springer US (New York )
                1554-351X
                1554-3528
                25 July 2019
                25 July 2019
                2019
                : 51
                : 5
                : 2152-2179
                Affiliations
                [1 ]GRID grid.17089.37, Department of Psychology, , University of Alberta, ; Edmonton, Alberta Canada
                [2 ]GRID grid.25073.33, ISNI 0000 0004 1936 8227, Department of Linguistics and Languages, , McMaster University, ; Hamilton, Ontario Canada
                Article
                1282
                10.3758/s13428-019-01282-6
                6797637
                31347038
                c3552966-f018-4907-9868-723a119eca84
                © The Author(s) 2019

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000155, Social Sciences and Humanities Research Council of Canada;
                Award ID: 435-2014-0003
                Categories
                Article
                Custom metadata
                © The Psychonomic Society, Inc. 2019

                Clinical Psychology & Psychiatry
                compound words,semantic transparency,psycholinguistics,morphology,bigram frequency,sentiment,family size

                Comments

                Comment on this article