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Professor Mark Johnson

BSc USyd, MA UCSD, PhD Stanford.

Associate Investigator

Contact Details

Office : E6A 316
Phone : +61 2 9850 9525
Fax : +61 2 9850 9551
email : mjohnson@science.mq.edu.au
Homepage : http://web.science.mq.edu.au/~mjohnson/

External Address

Department of Computing
Macquarie University NSW 2109

Profile

Mark Johnson is a Professor of Language Science (CORE) in the Department of Computing at Macquarie University. He was awarded a BSc (Hons) in 1979 from the University of Sydney, an MA in 1984 from the University of California, San Diego and a PhD in 1987 from Stanford University. He held a postdoctoral fellowship at MIT from 1987 until 1988, and has been a visiting researcher at the University of Stuttgart, the Xerox Research Centre in Grenoble, CSAIL at MIT and the Natural Language group at Microsoft Research. He has worked on a wide range of topics in computational linguistics, but his main research area is parsing and its applications to text and speech processing. He was President of the Association for Computational Linguistics in 2003, and was a professor from 1989 until 2009 in the Departments of Cognitive and Linguistic Sciences and Computer Science at Brown University.

Professor Johnson's research area is computational linguistics, i.e., explicit computational models of language acquisition, comprehension and production. His recent work has focused on probabilistic models for syntactic parsing (identifying the way words combine to form phrases and sentences) and semantic interpretation, and on Bayesian models of the acquisition of phonology, morphology and the lexicon.

Recent External Appointments

  • Editorial board member, Computer Speech and Language. (2009 - 2012)
  • Editorial advisor, Computational Linguistics. (2009 - 2012)

Recent Grants Awarded

  • ARC Discovery Project Grant [DP160102156] (2016 - 2018) "Improved syntactic parsing and semantic analysis for natural language processing." Johnson, M., & Steedman, M. ($388,000)
  • Macquarie University Research Infrastructure Block Grant Scheme (2012) “Upgrade of child magnetoencephalography (MEG) system.” Johnson, B.W., Crain, S., Thornton, R., Demuth, K., Brock, J., Sowman, P., Zhou, P., & Johnson, M. ($100,000)
  • ARC Discovery Project Grant [DP110102506] (2011 - 2013) "Computational models of synergies in human language acquisition." Johnson, M., Demuth, K., & Frank, M. ($368,000)
  • ARC Discovery Project Grant [DP110102593] (2011 - 2013) "Incremental syntactic parsing and coreference resolution." Johnson, M.E., & Steedman, M. ($395,000)
  • National Science Foundation (NSF) Partnerships for International Research and Education (PIRE) International Training Program in Computational Linguistics (2009 - 2014) "Collaborative investigation of meaning representation in language processing." Johnson, M. & Charniak, E. ($1,064,990)

Media Engagement

Selected Publications

Books

  • Johnson, M., Khudanpur, S.P., Ostendorf, M. and Rosenfeld, R. (Eds.). (2004). Mathematical Foundations of Speech and Language Processing New York, USA: Springer.
  • Johnson, M. (1988). Attribute-value Logic and the Theory of Grammar CSLI Lecture Notes Series: Chicago University Press.

Periodicals

  • Johnson, M. (2017). Marr's levels and the minimalist program. Psychonomic Bulletin & Review, 24(1), 171-174. doi:10.3758/s13423-016-1062-1
  • Surian, D., Nguyen, D.Q., Kennedy, G., Johnson, M., Coiera, E., & Dunn, A.G. (2016). Characterizing twitter discussions about HPV vaccines using topic modelling and community detection. Journal of Medical Internet Research, 18(8), e232. doi:10.2196/jmir.6045
  • Börschinger, B., & Johnson, M. (2014). Exploring the role of stress in Bayesian word segmentation using Adaptor Grammars. Transactions of the Association for Computational Linguistics, 2(Feb), 93-104.
  • Goldwater, S,. Griffiths, T.L., & Johnson, M. (2011). Producing power-law distributions and damping word frequencies with two-stage language models. Journal of Machine Learning Research, 12, 2335-2382.
  • Johnson, M. (2011). How relevant is linguistics to computational linguistics? Linguistic Issues in Language Technology, 12.
  • Johnson, M. & Demuth, K. (2010). Unsupervised phonemic Chinese word segmentation using Adaptor Grammars. Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010).
  • Johnson, M. & Demuth, K. (2010). Unsupervised phonemic Chinese word segmentation using Adaptor Grammars. Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010).
  • Johnson, M., Demuth, K., Frank, M., & Jones, B.K. (2010). Synergies in learning words and their referents. Advances in Neural Information Processing Systems, 23.
  • Goldwater, S., Griffiths, T., & Johnson, M. (2009). A Bayesian Framework for Word Segmentation: Exploring the Effects of Context. Cognition, 112(1), 21-54.

Published Conference Proceedings

  • Vu, T., Nguyen, D.Q., Johnson, M., Song, D., & Willis, A. (2017). Search personalization with embeddings. In J. Jose & et al. (Eds.), Advances in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Proceedings (pp. 598-604). Aberdeen, UK: Springer.
  • Ambati, B.R., Deoskar, T., Johnson, M., & Steedman, M. (2015). An incremental algorithm for transition-based CCG parsing. In M. Post & A. Lopez (Eds.), 2015 Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2015 (pp. 53-63). Denver, USA: Association for Computational Linguistics (ACL).
  • Honnibal, M., & Johnson, M. (2015). An improved non-monotonic transition system for dependency parsing, In L. Marquez, C. Callison-Burch & J. Su (Eds.), Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 (pp. 1373-1378). Lisbon, Portugal: Association for Computational Linguistics (ACL).
  • Johnson, M., Christophe, A., Dupoux, E., & Demuth, K. (2014). Modelling function words improves unsupervised word segmentation. In K. Toutanova & H. Wu (Eds.), Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (pp. 282-292). Stroudsburg: The Association for Computational Linguistics.
  • Synnaeve, G., Dautriche, I., Börschinger, B., Johnson, M., & Dupoux, E. (2014). Unsupervised word segmentation in context. In J. Hajic & J. Tsujii (Eds.), Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics. Dublin: ICCL.
  • Börschinger, B., Johnson, M., & Demuth, K. (2013). A joint model of word segmentation and phonological variation for English word-final /t/-deletion. In R. Navigli, J.-S. Chang & S. Faralli (Eds.), Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Volume 1: Long Papers (pp. 1508–1516). Madison: Omnipress, Inc.
  • Börschinger, B., & Johnson, M. (2012). Using rejuvenation to improve particle filtering for Bayesian word segmentation. In M. Li & M. White (Ed.), Proceedings of the 50th Annual General Meeting of the Association for Computational Linguistics (ACL), vol. 2: Short papers (pp. 85-89). Stroudsburg: Association for Computational Linguistics.
  • Börschinger, B., Demuth, K., & Johnson, M. (2012). Studying the effect of input size for Bayesian word segmentation on the Providence Corpus. In M. Kay & C. Boitet (Eds.), Proceedings of the 24th International Conference on Computational Linguistics (pp. 325-340). Mumbai: COLING 2012 Organizing Committee.
  • Johnson, M., Demuth, K., & Frank, M. (2012). Exploiting social information in grounded language learning via grammatical reductions. In M. Li & M. White (Eds.), Proceedings of the 50th Annual General Meeting of the Association for Computational Linguistics (ACL), vol. 1: Long papers (pp. 883-891). Stroudsburg: Association for Computational Linguistics.
  • JojoWong, S.M., Dras, M., & Johnson, M. (2012). Exploring adaptor grammars for native language identification. In N. Okazaki (Ed.), Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (pp. 699-709). Stroudsburg: Association for Computational Linguistics.
  • Jones, B., Johnson, M., & Goldwater, S. (2012). Semantic parsing with Bayesian tree transducers. In M. Li & M. White (Eds.), Proceedings of the 50th Annual General Meeting of the Association for Computational Linguistics (ACL), vol. 1: Long papers (pp. 488-496). Stroudsburg: Association for Computational Linguistics.
  • Meylan, S., Kurumada, C., Börschinger, B., Johnson, M., & Frank, M.C. (2012). Modeling online word segmentation performance in structured artificial languages. In N. Miyake, D. Peebles & R.P. Cooper (Eds.), Proceedings of the 34th Annual Meeting of the Cognitive Science Society (pp. 2002-2007). Austin: Cognitive Science Society.
  • Börschinger, B., & Johnson, M. (2011). A particle filter algorithm for Bayesian Wordsegmentation. In D. Molla & D. Martinez (Eds.), Proceedings of the Australasian Language Technology Association Workshop (pp. 10-18). Canberra: Australian National University.
  • Börschinger, B., Jones, B. K., & Johnson, M. (2011). Reducing grounded learning to grammatical inference. In P. Merlo (Eds.), Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 1416-1425). Stroudsburg, USA: Association for Computational Linguistics.
  • Johnson, M. (2011). Parsing in parallel on multiple cores and GPUs. In D. Molla & D. Martinez (Eds.), Proceedings of the Australasian Language Technology Association Workshop (pp. 29-37). Canberra: Australian National University.
  • JojoWong, S.M., Dras, M., & Johnson, M. (2011). Topic modeling for native language identification. In D. Molla & D. Martinez (Eds.), roceedings of the Australasian Language Technology Association Workshop (pp. 115-124). Canberra: Australian National University.
  • Jones, B., Johnson, M., & Goldwater, S. (2011). Formalizing semantic parsing with tree transducers. In D. Molla & D. Martinez (Eds.), Proceedings of the Australasian Language Technology Association Workshop (pp. 19-28). Canberra: Australian National University.
  • Parviz, M., Johnson, M., Johnson, B., & Brock, J. (2011). Using Language Models and Latent Semantic Analysis to Characterise the N400m Neural Response. In (Ed.), Proceedings of the Australasian Language Technology Association Workshop 2011 Canberra: Australasian Language Technology Association.
  • Yuen, I., Demuth, K., & Johnson, M. (2011). Prosodic structure in child speech planning and production. In W.-S. Lee, & E. Zee (Ed.), International Congress of Phonetic Sciences XVII (pp. 2248-2251). Hong Kong: City University of Hong Kong.
  • Zwarts, S., & Johnson, M. (2011). The impact of language models and loss functions on repair disfluency detection. In D. Lin (Ed.), Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 703-711). Stroudsburg, USA: Association for Computational Linguistics.

Conference Presentations, Colloquia, and other presentations

  • Johnson, M. (2015, October). Rational inferences and Bayesian inferences. Invited colloquium at the CCD Developing Mind Series: Rational Inferences Workshop, Macquarie University, Sydney.
  • Johnson, M. (2015, September). Synergies in word learning. Paper presented at the Workshop on Infant Speech Perception (WISP): Phonological and Lexical Development, Macquarie University, Sydney.
  • Johnson, M., Pate, J., Borschinger, B., & Demuth, K. (2014, November). Syllable weight and stress provide similar information for word segmentation. Poster session presented at the 39th Boston University Conference on Language Development, Boston, USA.
  • Synnaeve, G., Dautriche, I., Börschinger, B., Johnson, M., & Dupoux, E. (2014, August). Unsupervised word segmentation in context. Paper presented at the 25th International Conference on Computational Linguistics (COLING2014), Dublin, Ireland.
  • Börschinger, B., & Johnson, M. (2014, January). Exploring the role of stress in Bayesian word segmentation using Adaptor Grammars. Paper presented at the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, USA.
  • Johnson, M. (2013, July). Language acquisition as statistical inference. Keynote paper presented at the 19th International Congress of Linguists (19ICL), Geneva, Switzerland.
  • Börschinger, B., Demuth, K., & Johnson, M. (2012, December). Studying the effect of input size for Bayesian word segmentation on the providence corpus. Paper presented at the 24th International Conference on Computational Linguistics, Mumbai, India.
  • Johnson, M. (2012, October). Natural language processing and computational linguistics: From theory to applications. Colloquium at the Informatics Workshop, Macquarie University, Sydney.
  • Johnson, M. (2012, July). Learning words. Colloquium at the Center for Language and Speech Processing Workshop on Zero Resource Speech Technologies and Models of Early Language Acquisition, Johns Hopkins University, Baltimore, USA.
  • Johnson, M. (2012, July). Computational linguistics: Where do we go from here? Invited colloquium at the 50th Annual Meeting of the Association for Computational Linguistics (ACL), Jeju Island, South Korea.
  • Johnson, M. (2012, April). Grammars and topic models. Colloquium at the NICTA seminar, Australian National University.
  • Johnson, M., Demuth, K., & Frank, M. (2012, April). Exploiting social information in grounded language learning via grammatical reductions. Presentation given at the 50th Annual Meeting of the Association for Computational Linguistics (ACL), Jeju Island, South Korea.
  • Johnson, M. (2011, December). Parsing in parallel on multiple cores and GPUs. Presentation given at the Australasian Language Technology Association Workshop, Australian National University, Canberra.
  • Parviz, M., Johnson, M., Johnson, B.W., & Brock, J. (2011, December). Using language models and latent semantic analysis to characterize the N400m neural response. Paper presented at the Australasian Cognitive Neuroscience Conference - 21st Meeting of the Australasian Society for Psychophysiology, Macquarie University, Sydney.
  • Yuen, I., Demuth, K. & Johnson, M. (2011, August). Prosodic structure in child speech planning and production. Paper presented at the International Congress of Phonetic Sciences, Hong Kong.
  • Johnson, M. (2011, July). Dirichlet-Multinomials and Gibbs sampling. Colloquium at the Institute for Pure & Applied Mathematics, Graduate Summer School: Probabilistic Models of Cognition, University of California, Los Angeles, USA.
  • Johnson, M. (2011, June). Bayesian models of language acquisition, or where do the rules come from? Presentation given at the Department of Linguistics, Stanford University, USA.
  • Johnson, M. (2011, June). The impact of language models and loss functions on repair disfluency detection. Presentation given at the The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, USA.

Further Information

Contact Details

Telephone: +61 2 9850 4127
Email : ccd@mq.edu.au
Web : www.ccd.edu.au

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