On the Readability of Kernel-based Deep Learning Models in Semantic Role Labeling Tasks over Multiple Languages

[1]  Michael Johnson,et al.  Compositionality , 2020, The Wiley Blackwell Companion to Semantics.

[2]  Zachary Chase Lipton.  The mythos of model interpretability , 2016, ACM Queue.

[3]  Michael Franke,et al.  Not unreasonable: Carving vague dimensions with contraries and contradictions , 2018, CogSci.

[4]  Emmanuele Chersoni,et al.  Logical Metonymy in a Distributional Model of Sentence Comprehension , 2017, *SEMEVAL.

[5]  Holger Schwenk,et al.  Supervised Learning of Universal Sentence Representations from Natural Language Inference Data , 2017, EMNLP.

[6]  Douwe Kiela,et al.  Learning to Negate Adjectives with Bilinear Models , 2017, EACL.

[7]  Vu,et al.  Building a Dictionary of Affixal Negations , 2016 .

[8]  Stephen Clark,et al.  RELPRON: A Relative Clause Evaluation Data Set for Compositional Distributional Semantics , 2016, CL.

[9]  Raffaella Bernardi,et al.  There Is No Logical Negation Here, But There Are Alternatives: Modeling Conversational Negation with Distributional Semantics , 2016, Computational Linguistics.

[10]  Nicholas Asher,et al.  Integrating Type Theory and Distributional Semantics: A Case Study on Adjective–Noun Compositions , 2016, CL.

[11]  Gemma Boleda,et al.  Formal Distributional Semantics: Introduction to the Special Issue , 2016, CL.

[12]  Chu-Ren Huang,et al.  Representing Verbs with Rich Contexts: an Evaluation on Verb Similarity , 2016, EMNLP.

[13]  J. Binder.  In defense of abstract conceptual representations , 2016, Psychonomic Bulletin & Review.

[14]  Ngoc Thang Vu,et al.  Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction , 2016, ACL.

[15]  Tim Oates,et al.  A Gold Standard for Scalar Adjectives , 2016, LREC.

[16]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[17]  Gina R Kuperberg,et al.  What do we mean by prediction in language comprehension? , 2016, Language, cognition and neuroscience.

[18]  Yoav Goldberg,et al.  A Primer on Neural Network Models for Natural Language Processing , 2015, J. Artif. Intell. Res..

[19]  David Abend.  Semantic Relations And The Lexicon Antonymy Synonymy And Other Paradigms , 2016 .

[20]  Peter Hagoort,et al.  The neurobiology of language beyond single-word processing , 2019, Science.

[21]  Bart Geurts,et al.  Scalar Diversity , 2016, J. Semant..

[22]  Angeliki Lazaridou,et al.  A Multitask Objective to Inject Lexical Contrast into Distributional Semantics , 2015, ACL.

[23]  Sanja Fidler,et al.  Skip-Thought Vectors , 2015, NIPS.

[24]  Omer Levy,et al.  Improving Distributional Similarity with Lessons Learned from Word Embeddings , 2015, TACL.

[25]  Deniz Yuret,et al.  Probabilistic Modeling of Joint-context in Distributional Similarity , 2014, CoNLL.

[26]  Georgiana Dinu,et al.  Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors , 2014, ACL.

[27]  Mihai Surdeanu,et al.  The Stanford CoreNLP Natural Language Processing Toolkit , 2014, ACL.

[28]  Dimitri Kartsaklis,et al.  A Study of Entanglement in a Categorical Framework of Natural Language , 2014, QPL.

[29]  Katrin Erk,et al.  A Formal Approach to Linking Logical Form and Vector-Space Lexical Semantics , 2014 .

[30]  Marco Baroni,et al.  Frege in Space: A Program of Compositional Distributional Semantics , 2014 .

[31]  Elia Bruni,et al.  Multimodal Distributional Semantics , 2014, J. Artif. Intell. Res..

[32]  Raffaella Bernardi,et al.  Distributional Semantics: A Montagovian View , 2014, Categories and Types in Logic, Language, and Physics.

[33]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[34]  Marco Baroni,et al.  Composition in Distributional Semantics , 2013, Lang. Linguistics Compass.

[35]  Marie-Catherine de Marneffe,et al.  Deriving Adjectival Scales from Continuous Space Word Representations , 2013, EMNLP.

[36]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.

[37]  Graeme Hirst,et al.  Computing Lexical Contrast , 2013, CL.

[38]  Peter Hagoort,et al.  MUC (Memory, Unification, Control) and beyond , 2013, Front. Psychol..

[39]  Phil Blunsom,et al.  “Not not bad” is not “bad”: A distributional account of negation , 2013, CVSM@ACL.

[40]  Sara Berlanda.  Constructional Intensifying Adjectives in Italian , 2013, MWE@NAACL-HLT.

[41]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[42]  Katrin Erk,et al.  Vector Space Models of Word Meaning and Phrase Meaning: A Survey , 2012, Lang. Linguistics Compass.

[43]  Mirella Lapata,et al.  A Comparison of Vector-based Representations for Semantic Composition , 2012, EMNLP.

[44]  Andrew Y. Ng,et al.  Semantic Compositionality through Recursive Matrix-Vector Spaces , 2012, EMNLP.

[45]  Roser Morante,et al.  Modality and Negation: An Introduction to the Special Issue , 2012, CL.

[46]  Shrikant Joshi.  Affixal Negation – Direct, Indirect and their Subtypes , 2012 .

[47]  Dietrich Klakow,et al.  A survey on the role of negation in sentiment analysis , 2010, NeSp-NLP@ACL.

[48]  Patrick Pantel,et al.  From Frequency to Meaning: Vector Space Models of Semantics , 2010, J. Artif. Intell. Res..

[49]  Stephen Clark,et al.  Mathematical Foundations for a Compositional Distributional Model of Meaning , 2010, ArXiv.

[50]  Ken McRae,et al.  People Use their Knowledge of Common Events to Understand Language, and Do So as Quickly as Possible , 2009, Lang. Linguistics Compass.

[51]  Mary Hare,et al.  Activating event knowledge , 2009, Cognition.

[52]  Martin Hackl,et al.  On the grammar and processing of proportional quantifiers: most versus more than half , 2009 .

[53]  Silvia Bernardini,et al.  The WaCky wide web: a collection of very large linguistically processed web-crawled corpora , 2009, Lang. Resour. Evaluation.

[54]  Katrin Erk,et al.  A Structured Vector Space Model for Word Meaning in Context , 2008, EMNLP.

[55]  Mirella Lapata,et al.  Vector-based Models of Semantic Composition , 2008, ACL.

[56]  Tamar Fraenkel,et al.  The meaning of negated adjectives , 2008 .

[57]  Alessandro Lenci,et al.  Distributional semantics in linguistic and cognitive research , 2008 .

[58]  Mirella Lapata,et al.  Dependency-Based Construction of Semantic Space Models , 2007, CL.

[59]  Peter Hagoort,et al.  Beyond the sentence given , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[60]  M. Kritka.  Negated Antonyms: Creating and Filling the Gap , 2007 .

[61]  R. Giora.  Anything negatives can do affirmatives can do just as well, except for some metaphors , 2006 .

[62]  Carita Paradis,et al.  Antonymy and negation—The boundedness hypothesis , 2006 .

[63]  Siobhan Chapman.  Logic and Conversation , 2005 .

[64]  Stefan Evert,et al.  The Statistics of Word Cooccur-rences: Word Pairs and Collocations , 2004 .

[65]  S. Peters,et al.  Word Vectors and Quantum Logic Experiments with negation and disjunction , 2003 .

[66]  Michael Collins,et al.  New Ranking Algorithms for Parsing and Tagging: Kernels over Discrete Structures, and the Voted Perceptron , 2002, ACL.

[67]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[68]  Ray Jackendoff,et al.  The Architecture of the Language Faculty , 1996 .

[69]  E. Clark.  Conventionality and contrast: Pragmatic principles with lexical consequences. , 1992 .

[70]  Geoffrey Leech,et al.  100 Million Words of English:The British National Corpus (BNC) , 1992 .

[71]  Alexis Kalokerinos.  A natural history of negation , 1991 .

[72]  Paul Kiparsky,et al.  Word-formation and the lexicon , 1982 .

[73]  Herbert H. Clark,et al.  Semantics and comprehension , 1976 .

[74]  Laurence R. Horn,et al.  On the semantic properties of logical operators in english' reproduced by the indiana university lin , 1972 .

[75]  Otto Jespersen,et al.  The Philosophy of Grammar , 1924 .