build based on ac9b6d6

dev/index.html

@@ -324,4 +324,4 @@
 @show acc_build_val</code></pre><p>Output:</p><pre><code class="language-output">acc_learn_train = 0.9983
 acc_learn_val = 0.6866
 acc_build_train = 1.0
-acc_build_val = 0.3284</code></pre><p>Alternatively, we  have a wrapper function incorporating all above functionalities. With this function, you can quickly explore datasets with different parameter settings. Please find more in the <a href="@ref">Test Combo Introduction</a>.</p><h2 id="Supports"><a class="docs-heading-anchor" href="#Supports">Supports</a><a id="Supports-1"></a><a class="docs-heading-anchor-permalink" href="#Supports" title="Permalink"></a></h2><p>There are two types of supports in outputs. An utterance level and a set of supports for each cue. The former support is also called &quot;synthesis-by-analysis&quot; support. This support is calculated by predicted S vector and original S vector and it is used to select the best paths. Cue level supports are slices of Yt matrices from each timestep. Those supports are used to determine whether a cue is eligible for constructing paths.</p><h2 id="Acknowledgments"><a class="docs-heading-anchor" href="#Acknowledgments">Acknowledgments</a><a id="Acknowledgments-1"></a><a class="docs-heading-anchor-permalink" href="#Acknowledgments" title="Permalink"></a></h2><p>This project was supported by the ERC advanced grant WIDE-742545 and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC number 2064/1 - Project number 390727645.</p><h2 id="Acknowledgments-2"><a class="docs-heading-anchor" href="#Acknowledgments-2">Acknowledgments</a><a class="docs-heading-anchor-permalink" href="#Acknowledgments-2" title="Permalink"></a></h2><p>This project was supported by the ERC advanced grant WIDE-742545 and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC number 2064/1 - Project number 390727645.</p><h2 id="Citation"><a class="docs-heading-anchor" href="#Citation">Citation</a><a id="Citation-1"></a><a class="docs-heading-anchor-permalink" href="#Citation" title="Permalink"></a></h2><p>If you find this package helpful, please cite it as follows:</p><p>Luo, X., Heitmeier, M., Chuang, Y. Y., Baayen, R. H. JudiLing: an implementation of the Discriminative Lexicon Model in Julia. Eberhard Karls Universität Tübingen, Seminar für Sprachwissenschaft.</p><p>The following studies have made use of several algorithms now implemented in JudiLing instead of WpmWithLdl:</p><ul><li><p>Baayen, R. H., Chuang, Y. Y., Shafaei-Bajestan, E., and Blevins, J. P. (2019). The discriminative lexicon: A unified computational model for the lexicon and lexical processing in comprehension and production grounded not in (de)composition but in linear discriminative learning. Complexity, 2019, 1-39.</p></li><li><p>Baayen, R. H., Chuang, Y. Y., and Blevins, J. P. (2018). Inflectional morphology with linear mappings. The Mental Lexicon, 13 (2), 232-270.</p></li><li><p>Chuang, Y.-Y., Lõo, K., Blevins, J. P., and Baayen, R. H. (2020). Estonian case inflection made simple. A case study in Word and Paradigm morphology with Linear Discriminative Learning. In Körtvélyessy, L., and Štekauer, P. (Eds.) Complex Words: Advances in Morphology, 1-19.</p></li><li><p>Chuang, Y-Y., Bell, M. J., Banke, I., and Baayen, R. H. (2020). Bilingual and multilingual mental lexicon: a modeling study with Linear Discriminative Learning. Language Learning, 1-55.</p></li><li><p>Heitmeier, M., Chuang, Y-Y., Baayen, R. H. (2021). Modeling morphology with Linear Discriminative Learning: considerations and design choices. Frontiers in Psychology, 12, 4929.</p></li><li><p>Denistia, K., and Baayen, R. H. (2022). The morphology of Indonesian: Data and quantitative modeling. In Shei, C., and Li, S. (Eds.) The Routledge Handbook of Asian Linguistics, (pp. 605-634). Routledge, London.</p></li><li><p>Heitmeier, M., Chuang, Y.-Y., and Baayen, R. H. (2023). How trial-to-trial learning shapes mappings in the mental lexicon: Modelling lexical decision with linear discriminative learning. Cognitive Psychology, 1-30.</p></li><li><p>Chuang, Y. Y., Kang, M., Luo, X. F. and Baayen, R. H. (2023). Vector Space Morphology with Linear Discriminative Learning. In Crepaldi, D. (Ed.) Linguistic morphology in the mind and brain.</p></li><li><p>Heitmeier, M., Chuang, Y. Y., Axen, S. D., &amp; Baayen, R. H. (2024). Frequency effects in linear discriminative learning. Frontiers in Human Neuroscience, 17, 1242720.</p></li><li><p>Plag, I., Heitmeier, M. &amp; Domahs, F. (to appear). German nominal number interpretation in an impaired mental lexicon: A naive discriminative learning perspective. The Mental Lexicon.</p></li></ul></article><nav class="docs-footer"><a class="docs-footer-nextpage" href="man/input/">Loading data »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Thursday 13 June 2024 13:27">Thursday 13 June 2024</span>. Using Julia version 1.10.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+acc_build_val = 0.3284</code></pre><p>Alternatively, we  have a wrapper function incorporating all above functionalities. With this function, you can quickly explore datasets with different parameter settings. Please find more in the <a href="@ref">Test Combo Introduction</a>.</p><h2 id="Supports"><a class="docs-heading-anchor" href="#Supports">Supports</a><a id="Supports-1"></a><a class="docs-heading-anchor-permalink" href="#Supports" title="Permalink"></a></h2><p>There are two types of supports in outputs. An utterance level and a set of supports for each cue. The former support is also called &quot;synthesis-by-analysis&quot; support. This support is calculated by predicted S vector and original S vector and it is used to select the best paths. Cue level supports are slices of Yt matrices from each timestep. Those supports are used to determine whether a cue is eligible for constructing paths.</p><h2 id="Acknowledgments"><a class="docs-heading-anchor" href="#Acknowledgments">Acknowledgments</a><a id="Acknowledgments-1"></a><a class="docs-heading-anchor-permalink" href="#Acknowledgments" title="Permalink"></a></h2><p>This project was supported by the ERC advanced grant WIDE-742545 and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC number 2064/1 - Project number 390727645.</p><h2 id="Acknowledgments-2"><a class="docs-heading-anchor" href="#Acknowledgments-2">Acknowledgments</a><a class="docs-heading-anchor-permalink" href="#Acknowledgments-2" title="Permalink"></a></h2><p>This project was supported by the ERC advanced grant WIDE-742545 and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC number 2064/1 - Project number 390727645.</p><h2 id="Citation"><a class="docs-heading-anchor" href="#Citation">Citation</a><a id="Citation-1"></a><a class="docs-heading-anchor-permalink" href="#Citation" title="Permalink"></a></h2><p>If you find this package helpful, please cite it as follows:</p><p>Luo, X., Heitmeier, M., Chuang, Y. Y., Baayen, R. H. JudiLing: an implementation of the Discriminative Lexicon Model in Julia. Eberhard Karls Universität Tübingen, Seminar für Sprachwissenschaft.</p><p>The following studies have made use of several algorithms now implemented in JudiLing instead of WpmWithLdl:</p><ul><li><p>Baayen, R. H., Chuang, Y. Y., Shafaei-Bajestan, E., and Blevins, J. P. (2019). The discriminative lexicon: A unified computational model for the lexicon and lexical processing in comprehension and production grounded not in (de)composition but in linear discriminative learning. Complexity, 2019, 1-39.</p></li><li><p>Baayen, R. H., Chuang, Y. Y., and Blevins, J. P. (2018). Inflectional morphology with linear mappings. The Mental Lexicon, 13 (2), 232-270.</p></li><li><p>Chuang, Y.-Y., Lõo, K., Blevins, J. P., and Baayen, R. H. (2020). Estonian case inflection made simple. A case study in Word and Paradigm morphology with Linear Discriminative Learning. In Körtvélyessy, L., and Štekauer, P. (Eds.) Complex Words: Advances in Morphology, 1-19.</p></li><li><p>Chuang, Y-Y., Bell, M. J., Banke, I., and Baayen, R. H. (2020). Bilingual and multilingual mental lexicon: a modeling study with Linear Discriminative Learning. Language Learning, 1-55.</p></li><li><p>Heitmeier, M., Chuang, Y-Y., Baayen, R. H. (2021). Modeling morphology with Linear Discriminative Learning: considerations and design choices. Frontiers in Psychology, 12, 4929.</p></li><li><p>Denistia, K., and Baayen, R. H. (2022). The morphology of Indonesian: Data and quantitative modeling. In Shei, C., and Li, S. (Eds.) The Routledge Handbook of Asian Linguistics, (pp. 605-634). Routledge, London.</p></li><li><p>Heitmeier, M., Chuang, Y.-Y., and Baayen, R. H. (2023). How trial-to-trial learning shapes mappings in the mental lexicon: Modelling lexical decision with linear discriminative learning. Cognitive Psychology, 1-30.</p></li><li><p>Chuang, Y. Y., Kang, M., Luo, X. F. and Baayen, R. H. (2023). Vector Space Morphology with Linear Discriminative Learning. In Crepaldi, D. (Ed.) Linguistic morphology in the mind and brain.</p></li><li><p>Heitmeier, M., Chuang, Y. Y., Axen, S. D., &amp; Baayen, R. H. (2024). Frequency effects in linear discriminative learning. Frontiers in Human Neuroscience, 17, 1242720.</p></li><li><p>Plag, I., Heitmeier, M. &amp; Domahs, F. (to appear). German nominal number interpretation in an impaired mental lexicon: A naive discriminative learning perspective. The Mental Lexicon.</p></li></ul></article><nav class="docs-footer"><a class="docs-footer-nextpage" href="man/input/">Loading data »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> on <span class="colophon-date" title="Thursday 13 June 2024 14:04">Thursday 13 June 2024</span>. Using Julia version 1.10.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>