Challenging distributional models with a conceptual network of philosophical terms
暂无分享,去创建一个
Antske Fokkens | Jelke Bloem | Pia Sommerauer | Wei Zhou | Yvette Oortwijn | Francois Meyer | Antske Fokkens | Wei Zhou | Yvette Oortwijn | Jelke Bloem | Pia Sommerauer | Francois Meyer
[1] M. de Rijke,et al. Ad Hoc Monitoring of Vocabulary Shifts over Time , 2015, CIKM.
[2] Anders Søgaard. Data point selection for cross-language adaptation of dependency parsers , 2011, ACL.
[3] Antske Fokkens,et al. Evaluating the Consistency of Word Embeddings from Small Data , 2019, RANLP.
[4] Erik Velldal,et al. Diachronic word embeddings and semantic shifts: a survey , 2018, COLING.
[5] Michael N. Jones,et al. Comparing Predictive and Co-occurrence Based Models of Lexical Semantics Trained on Child-directed Speech , 2016, CogSci.
[6] Amir Bakarov,et al. A Survey of Word Embeddings Evaluation Methods , 2018, ArXiv.
[7] Shimei Pan,et al. Incorporating Domain Knowledge in Learning Word Embedding , 2019, 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI).
[8] Mario Giulianelli,et al. Analysing Lexical Semantic Change with Contextualised Word Representations , 2020, ACL.
[9] Stefan Schlobach,et al. Phil@Scale: Computational Methods within Philosophy , 2013, DHLU.
[10] Marijn Koolen,et al. Digital begriffsgeschichte: Tracing semantic change using word embeddings , 2020 .
[11] Udo Hahn,et al. Bad Company—Neighborhoods in Neural Embedding Spaces Considered Harmful , 2016, COLING.
[12] Antske Fokkens,et al. Conceptual Change and Distributional Semantic Models: an Exploratory Study on Pitfalls and Possibilities , 2019, LChange@ACL.
[13] Omer Levy,et al. Improving Distributional Similarity with Lessons Learned from Word Embeddings , 2015, TACL.
[14] Udo Hahn,et al. An Assessment of Experimental Protocols for Tracing Changes in Word Semantics Relative to Accuracy and Reliability , 2016, LaTeCH@ACL.
[15] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[16] Dmitry I. Ilvovsky,et al. Creation and Evaluation of Datasets for Distributional Semantics Tasks in the Digital Humanities Domain , 2019, ArXiv.
[17] Katrin Erk,et al. Deep Neural Models of Semantic Shift , 2018, NAACL-HLT.
[18] Barbara McGillivray,et al. Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings , 2019, EMNLP.
[19] Anna Gladkova,et al. Intrinsic Evaluations of Word Embeddings: What Can We Do Better? , 2016, RepEval@ACL.
[20] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[21] Slav Petrov,et al. Temporal Analysis of Language through Neural Language Models , 2014, LTCSS@ACL.
[22] Lars Borin,et al. Survey of Computational Approaches to Diachronic Conceptual Change , 2018, ArXiv.
[23] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[24] Jure Leskovec,et al. Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change , 2016, EMNLP.
[25] A. Betti,et al. Modelling the History of Ideas , 2014 .
[26] Alexandre Allauzen,et al. Empirical Study of Diachronic Word Embeddings for Scarce Data , 2019, RANLP.
[27] George A. Miller,et al. WordNet: A Lexical Database for English , 1995, HLT.
[28] Steven Skiena,et al. Statistically Significant Detection of Linguistic Change , 2014, WWW.
[29] Sean C. Murphy,et al. Evaluation of Semantic Change of Harm-Related Concepts in Psychology , 2019, LChange@ACL.
[30] Daphna Weinshall,et al. Outta Control: Laws of Semantic Change and Inherent Biases in Word Representation Models , 2017, EMNLP.
[31] Bettina Speckmann,et al. A philosophical perspective on visualization for digital humanities , 2018 .
[32] Christopher D. Manning,et al. Better Word Representations with Recursive Neural Networks for Morphology , 2013, CoNLL.
[33] Marco Baroni,et al. High-risk learning: acquiring new word vectors from tiny data , 2017, EMNLP.
[34] Jure Leskovec,et al. Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change , 2016, ACL.
[35] Petr Sojka,et al. Software Framework for Topic Modelling with Large Corpora , 2010 .
[36] Alessandro Lenci,et al. The Effects of Data Size and Frequency Range on Distributional Semantic Models , 2016, EMNLP.
[37] Jian Huang,et al. Analyzing Multiple Medical Corpora Using Word Embedding , 2016, 2016 IEEE International Conference on Healthcare Informatics (ICHI).
[38] Thomas Risse,et al. On the Uses of Word Sense Change for Research in the Digital Humanities , 2017, TPDL.
[39] Christian Biemann,et al. An automatic approach to identify word sense changes in text media across timescales , 2015, Natural Language Engineering.
[40] Aurélie Herbelot,et al. Distributional techniques for philosophical enquiry , 2012, LaTeCH@EACL.
[41] Willard Van Orman Quine,et al. Word and Object , 1960 .
[42] Antske Fokkens,et al. A larger-scale evaluation resource of terms and their shift direction for diachronic lexical semantics , 2019, NODALIDA.
[43] Angeliki Lazaridou,et al. Multimodal Word Meaning Induction From Minimal Exposure to Natural Text. , 2017, Cognitive science.
[44] Arianna Betti,et al. Expert Concept-Modeling Ground Truth Construction for Word Embeddings Evaluation in Concept-Focused Domains , 2020, COLING.