Readings in Latent Semantic Analysis for Cognitive Science and Education

• LSA, Université du Colorado, USA
• Infomap, a LSA engine
  
• LSI software (GTP, GUP, ...)
• Fridolin Wild open-source LSA package for R
• NEW Gallito 2.0, from the Spanish Group of Interest in LSA
  
• Gensim, a Python Framework for Vector Space Modelling
  
• GnuTutor, open source learning environment based on Autotutor
  
• LSI (Latent Semantic Indexing), Telcordia
• Knowledge Technologies
• SummaryStreet, Université du Colorado, USA
• S-Space packages for building semantic spaces
  

• Bestgen, Y., Univ. Louvain-la-Neuve, Belgium
• Dennis, S., Univ. of Colorado, USA
  
• Dessus, Ph. LSE, Univ. Grenoble, France
• Dong, A., Univ. Sydney Australia
  
• Dumais, S. T., Microsoft, USA
• Escudero, I., University of Madrid, Spain
  
• Foltz, P., Univ. Nouveau-Mexique, USA
• Graesser, A., University of Memphis, USA
• Ishioka T., NCUEE, Japan
• Jorge-Botana, G., University of Madrid, Spain
• Kakkonen,T., University of Eastern Finland
  
• Kintsch, E., Univ. of Colorado, USA
• Kintsch, W., Univ. of Colorado, USA
• Landauer, T., Univ. of Colorado, USA
• Leon, J., University of Madrid, Spain
  
• Lemaire, B., LPNC, Univ. Grenoble, France
• Lenhard, W., University of Würzburg, Germany
  
• McNamara, D., Universiy of Memphis, USA
  
• Nakov, P., Univ. of California, USA
  
• Olmos, R., University of Madrid, Spain 
• Quesada, J., Max Planck Institute, Berlin, Germany
• Skoyles, J., London Univ., UK
• Sahlgren, M., SICS, Sweden
  
• Stahl, G., Boulder, USA
• Steyvers M., University of California, USA
  
• Wiemer-Hastings, P., DePaul University, USA
  

• Qu'est LSA / What is LSA ?
• Wikipedia article on LSA
• Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., Harshman, R. (1990). Indexing by Latent Semantic Analysis . Journal of the American Society for Information Science, 41-6, 391-407.
• Foltz, P. W. (1996). Latent Semantic Analysis for text-based research . Behavior Research Methods, Instruments and Computers, 28-2, 197-202.
• Garcia E. (2006). SVD and LSI Tutorials.
  
• Kontostathis A., Pottenger, W. (2006). A Framework for understanding Latent Semantic Indexing (LSI) Performance. Information Processing and Management 42, 56-73.
  
• Landauer, T. K., Foltz, P. W., Laham, D. (1998). An introduction to Latent Semantic Analysis. Discourse Processes, 25, 259-284.
• Landauer, T.K. (2002). Applications of Latent Semantic Analysis, 24th Annual Meeting of the Cognitive Science Society, August 9th 2002.
• Lemaire, B. & Denhière, G. (2003). Cognitive Models based on Latent Semantic Analysis, Tutorial given at the 5th International Conference on Cognitive Modeling (ICCM'2003), Bamberg, Germany, April 9.
• Lemaire B.,  Dessus P. (2003). Modèles cognitifs issus de l'Analyse de la sémantique latente. Cahiers Romans de Sciences Cognitives 1(1), 55-74
• Yu, C., Cuadrado, J., Ceglowski, M., Payne, J. (2002) Patterns in Unstructured Data, Discovery, Aggregation, and Visualization, A Presentation to the Andrew W. Mellon Foundation.

• Strang, G. MIT OpenCourseware on Linear Algebra (Lecture 29) at http://videolectures.net/mit1806s05_linear_algebra/
  
• Tod Will. Introduction to the Singular Value Decomposition at http://www.uwlax.edu/faculty/will/svd/index.html
• A iMetaSearch SVD and LSA tutorial at http://www.puffinwarellc.com/index.php/news-and-articles/articles/30-singular-value-decomposition-tutorial.html

• Tonta, Y., Darvish, H.R. (2010). Diffusion of Latent Semantic Analysis as a research tool: a social network analysis approach. Journal of Informetrics 4(2), 166-174.
  

SenseClusters

• Pedersen, T, Kulkarni, A. (2005) Identifying Similar Words and Contexts in Natural Language with SenseClusters In Proceedings of the Twentieth National Conference on Artificial Intelligence July, 2005, Pittsburgh, PA. (Intelligent Systems Demonstration)
• SenseClusters page
  

Hyperspace Analogue to Language (HAL)

• Burgess, C. (1998). From simple associations to the building blocks of language: Modeling meaning in memory with the HAL model . Behavior Research Methods, Instruments, & Computers, 30, 188-198.
• Burgess, C., Livesay, K, Lund, K. (1998). Explorations in Context Space: Words, sentences, discourse . Discourse Processes, 25, 211-257.
• Desai, R. (1998). A review of two statistical models of knowledge representation, acquisition, and memory. Bloomington : Université de l'Indiana, research report.
• Li, P., Burgess, C., & Lund, K. (2000). The acquisition of word meaning through global lexical co-occurrences . In E. Clark (ed.) Proceedings of the 30th Child Language Research Forum. Stanford, CA: Center for the Study of Language and Information, pp. 167-178. 
• Lund, K., Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence . Behavior research, methods, instruments and computers, 28-2, 203-208.

Pointwise Mutual Information - Information Retrieval (PMI-IR)

• Turney, Peter (2001) Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL . In De Raedt, Luc and Flach, Peter, Eds. Proceedings of the Twelfth  European Conference on Machine Learning (ECML-2001), 491-502, Freiburg, Germany.

Random Indexing

• Sahlgren, M. (2001). Vector-Based Semantic Analysis: Representing Word Meanings Based on Random Labels . Presented at the Semantic Knowledge Acquisition and Categorisation Workshop at ESSLLI '01, Helsinki, Finland.
• Sahlgren, M. (2002). Towards a Flexible Model of Word Meaning. Paper presented at the AAAI Spring Symposium 2002, March 25-27, Stanford University, Palo Alto,
  
• Kanerva, P., Kristofersson, J. & Holst, A.(2000) Random Indexing of Text Samples for Latent Semantic Analysis. Proceedings of the 22nd Annual Conference of the Cognitive Science Society, p.1036, 2000.

Word Association Space (WAS)

• Steyvers, M., Shiffrin R.M., & Nelson, D.L. (in press). Word Association Spaces for Predicting Semantic Similarity Effects in Episodic Memory. In A. Healy (Ed.), Cognitive Psychology and its Applications: Festschrift in Honor of Lyle Bourne, Walter Kintsch, and Thomas Landauer, Washington DC: American Psychological Association

Incremental Construction of an Associative Network (ICAN)


• Lemaire, B., Denhière, G. (2004). Incremental Construction of an Associative Network from a Corpus, in Proceedings of the 26th Annual Meeting of the Cognitive Science Society (CogSci'2004), 825-830.
  

• Vigliocco, G., Vinson, D.P, Lewis, W. & Garrett, M.F. (2004). Representing the meanings of object and action words: The Featural and Unitary Semantic Space hypothesis. Cognitive Psychology 48(4), 422-488.

• Cai, Z., McNamara, D.S., Louwerse, M., Hu, X., Rowe, M., Graesser, A.C. (2004). NLS: A Non-Latent Similarity Algorithm, in Proceedings of the 26th Annual Meeting of the Cognitive Science Society (CogSci'2004), 180-185.

• Ventura, M., Hu, X., Graesser, A., Louwerse, M., Olney, A. (2004). The Context Dependent Sentence Abstraction Model, in Proceedings of the 26th Annual Meeting of the Cognitive Science Society (CogSci'2004), 1387-1392.

• Glenberg, A. M., Robertson, D. A.,  (2000). Grounding Symbols and Computing Meaning : A supplement to Glenberg & Robertson . Journal of Memory and Language, 43, 379-401.
• Jhean-Larose, S., Leclercq, V., Diaz, J., Denhière, G., Bouchon-Meunier, B. (2010). Knowledge evaluation based on LSA, MCQs and free answer questions. Studia Informatica Universalis 8(1), 57-84.
  
• Kintsch, W. (1998). The representation of Knowledge in Minds and Machines . International Journal of Psychology, 33-6, 411-420.
• Rehder, B., Schreiner, M. E., Wolfe, M. B. W., Laham, D., Landauer, T. K., Kintsch, W. (1998). Using Latent Semantic Analysis to assess knowledge: some technical considerations . Discourse Processes, 25, 337-354.
• Shapiro, A.M., & McNamara, D.S. (2000). The use of latent semantic analysis as a tool for the quantitative assessment of understanding and knowledge. Journal of Educational Computing Research, 22, 1-36.
• Wolfe, M. B. W., Schreiner, M. E., Rehder, B., Laham, D., Foltz, P. W., Kintsch, W., Landauer, T. K. (1998). Learning from text: Matching readers and texts by Latent Semantic Analysis . Discourse Processes, 25, 309-336.

• Bestgen, Y., Cabiaux, A.F. (2002). L'analyse sémantique latente et l'identification des métaphores . TALN'2002.
• NEW Jorge-Botana, G., León, J.A.,Olmos, R. & Hassan-Montero, Y.(2010). Visualizing polysemy using LSA and the predication algorithm. Journal of the American Society for Information Science and Technology 61(8), 1706–1724.
  
• NEW Jorge-Botana, G., León, J.A.,Olmos, R., & Escudero, I. (2011) The representation of polysemy through vectors: some building blocks for constructing models and applications with LSA. International Journal of Continuing Engineering education and Long Learning 21(4).
• Kintsch, W. (1998). Comprehension, a paradigm for cognition . Cambridge : Cambridge UP.
• Landauer, T. K., Dumais, S. T. (1997). A solution to Plato's problem : the Latent Semantic Analysis theory of acquisition, induction and representation of knowledge . Psychological Review, 104, 211-240.
• Landauer (2002). On the computational basis of learning and cognition: Arguments from LSA . In N. Ross (Ed.), The psychology of learning and motivation, 41, 43-84.
• Lemaire, B. (1998). Models of High-dimensional Semantic Spaces . Proc. 4th Int. Workshop on Multistrategy Learning (MSL 98). Desenzano, Italie.
• Lemaire, B. & Bianco, M. (2003). Contextual Effects on Metaphor Comprehension: Experiment and Simulation . Proc. of the 5th International Conference on Cognitive Modeling (ICCM'2003), Bamberg, Germany.
• Lemaire, B., Mandin, S., Dessus, P. & Denhière, G. (2005). Computational cognitive models of summarization assessment skills, in Proceedings of the 27th Annual Meeting of the Cognitive Science Society (CogSci' 2005). Stresa, Italy, July 21-23, 1266-1271.
  
• Nakov P., S. Terzieva. (2002)  Latent Semantic Analysis for Notional Structures Investigation . EuroSPP (Annual Congress of the European Society for Philosophy and Psychology). Lyon, France, July 10-13.
• Terzieva S., Nakov P., Handjieva S. (2001) Investigating the degree of Adequacy of the Relations in the Concept Structure of Students using the Method of Latent Semantic Analysis . In Proceedings of CompSysTech'2001 - Bulgarian Computer Science Conference. Sofia, Bulgaria.
  
• Quesada, J.F, Kintsch, W. and Gomez, E. (2001) A Computational Theory of Complex Problem Solving Using the Vector Space Model (part I): Latent Semantic Analysis, Through the Path of Thousands of Ants. In Canas (Ed.) Cognitive resarch with Microworlds, p. ??
• Quesada, J.F, Kintsch, W. and Gomez, E. (2001) A Computational Theory of Complex Problem Solving Using the Vector Space Model (part II): Latent Semantic Analysis Applied to Empirical Results from Adaptation Experiments .In Canas (Ed.) Cognitive resarch with Microworlds.
• Quesada, J., Kintsch, W., and Gomez E. (2002) A theory of Complex Problem Solving using Latent Semantic Analysis . In W. D. Gray & C. D. Schunn (Eds.) Proceedings of the 24th Annual Conference of the Cognitive Science Society Fairfax, VA. Lawrence Erlbaum Associates, Mahwah, NJ.
• Redington, M., Chater, N. (1998). Connectionist and statistical approaches to language acquisition: A distributional perspective . Language and Cognitive Processes, 13-2/3, 29-91.

• Foltz, P. W., Kintsch, W., Landauer, T. K., (1998). The measurement of textual coherence with Latent Semantic Analysis . Discourse Processes, 25, 285-307.
• Tristan Miller (2003). Latent semantic analysis and the construction of coherent extracts. In Galia Angelova, Kalina Bontcheva, Ruslan Mitkov, Nicolas Nicolov, and Nikolai Nikolov, editors, Proceedings of the International Conference RANLP-2003 (Recent Advances in Natural Language Processing), pages 270–277, September 2003.

• NEW Jorge-Botana, G.,Olmos, R.,& Barroso, A. (in press) The Construction-Integration framework: A means to disminish bias in LSA-based Call Routing. International Journal of Speech Technology.
  
• Kintsch, W. (2000). Metaphor comprehension: A computational Theory . Psychonomic Bulletin & Review, 7-2, 257-266.
• Kintsch, W. (2001). Predication . Cognitive Science, 25-2, 173-202.
• Kintsch, W. (2002). On the notions of theme and topic in psychological process models of text comprehension, In M. Louwerse & W. van Peer (Eds) Thematics: Interdisciplinary Studies, Amsterdam: Benjamins, 151-170.
• Kintsch, W. and Bowles, A.R. (2002). Metaphor comprehension: what makes a metaphor difficult to understand?, Metaphor and Symbol, 17, 249-262.
• Lemaire B., Bianco M., Sylvestre E., Noveck I. (2001) Un modèle de compréhension de textes fondé sur l'analyse de la sémantique latente, In H. Paugam_Moisy, V. Nyckees, J. Caron-Pargue (Eds.) La cognition entre individu et société (actes du colloque de l'ARCo), Hermès, 309-320.
• Lemaire, B., Denhière, G., Bellissens, C., Jhean-Larose, S. (2006) A Computational Model for Simulating Text Comprehension. Behavior Research Methods 38(4), 628-637.
  
• McCauley, L. (unpublished). Using Latent Semantic Analysis to Aid Speech Recognition and Understanding.
• Rubistein A. (2003). Using LSA to detect Irony In Proceedings of Corpus Linguistics'2003 - Interdisciplinary Workshop on Corpus-Based Approaches to Figurative Language. UK, Lancaster, 27 March - 1 April 2003.

• Bellissens, C. & Denhière G. (2004) Retrieval Structure Construction during Reading: Experimentation and Simulation, in Proceedings of the 26th Annual Meeting of the Cognitive Science Society (CogSci'2004), 91-95.
• Denhière G., Lemaire, B. (2004)  A Computational Model of a Child Semantic Memory, in Proceedings of the 26th Annual Meeting of the Cognitive Science Society (CogSci'2004), 297-302.
• Howard, M.W. & Kahanna, M.J. (2002) When does Semantic Similarity Help Episodic Retrieval?, Journal of Memory and Language 46, 85-98.
• Howard, M.W. & Kahanna, M.J. (2007) Semantic Structure and Episodic Memory, In T.K. Landauer, D.S. McNamara, S. Dennis, W. Kintsch (Eds). Handbook of Latent Semantic Analysis, Mahwah: Lawrence Erlbaum Associates.
• Kintsch, W. (2010). Modeling Semantic Memory. Studia Informatica Universalis 8(1), 5-27.
  
• Kintsch, W., Patel, V.L., & Ericsson, K. A. (1999) The role of long-term working memory in text comprehension. Psychologia 42, 186-198

• Chanceaux, M., Guérin-Dugué, A., Lemaire, B., Baccino, T. (2008). Towards a model of information seeking by integrating visual, semantic and memory maps, In B. Caputo, M Vincze (Eds), Proceedings of the 4th International Cognitive Vision Workshop, Lecture Notes in Computer Science 5329, Berlin: Springer Verlag, 65-78.
• Huettig, F., Quinlan, P., McDonal, S., Altmann, G. (2005) Models of high-dimensional semantic space predict language-mediated eye movements in the visual world. Acta Psychologica 121, 65-80.
  

    Généralités / General purpose

• van Bruggen, J., Sloepp, P., van Rosmalen, P., Brouns, F., Vogten, H., Koper, R., Tattersall, C. (2004) Latent semantic analysis as a tool for learner positioning in learning networks for lifelong learning, British Journal of Educational Technology 35(6), 729-738.
  
• Chung, G. & O'Neil, G. (1997). Methodological Approaches to online scoring of essays. Los Angeles : Center for the Study of Evaluation, CRESST, Technical report 461.
• Dessus, P. (2000). Construction de connaissances par exposition à un cours avec LSA [Knowledge Building with LSA through Course exposition]. In Cognito, 18, 27-34.
• Dikli, S. (2006) An Overview of Automated Scoring of Essays. The Journal of Technology, Learning and Assessment 5(1).
• Haley, D.T., Thomas, P., de Roeck A., Petre, M. (2005). A research taxonomy for Latent Semantic Analysis-Based Educational Applications, Technical Report of the Open University, Departement of Computing, 2005/09.
• Harry Halpin, Johanna D. Moore and Judy Robertson (2004). Automatic Analysis of Plot for Story Rewriting, in Proceedings of Empirical Methods in Natural Language Processing (EMNLP 2004), Barcelona, Spain.
• NEW Jorge-Botana, G., León, J.A.,Olmos, R., & Escudero, I. (2010). Latent Semantic Analysis Parameters for Essay Evaluation using Small-Scale Corpora. Journal of Quantitative Linguistics 17(1), 1–29.
  
• Landauer, T. K., & Psotka, J. (2000). Simulating Text Understanding for Educational Applications with Latent Semantic Analysis: Introduction to LSA. Interactive Learning Environments, 8(2), pp. 73-86.
• Lemaire, B. (1999). Tutoring systems based on Latent Semantic Analysis. In S. Lajoie, M. Vivet (Eds) Artificial Intelligence in Education . Amsterdam : IOS Press, 527-534.
• Miller T. (2003) Essay assessment with latent semantic analysis. Journal of Educational Computing Research, 28, 2003.
• NEW Olmos, R., León, J.A., Jorge-Botana, G. & Escudero, I. (2011) Using Latent Semantic Analysis to grade brief summaries some proposals. International Journal of Continuing Engineering education and Long Learning 21 (2/3).
• NEW Olmos, R., León, J.A., Jorge-Botana, G. & Escudero, I. (2009). New algorithms assessing short summaries in expository texts using Latent Semantic Analysis. Behavior Research Methods. 41(3), 944-950.
  
• Wild, F., Stahl, C., Stermsek, G., Neumann G. (2005). Parameters driving effectiveness of automated essay scoring with LSA, In Danson, M. (Ed.) Proceedings of the 9th International Computer Assisted Assessment (CAA) Conference, 5-6 July 2005, Loughborough University, Leicestershire, UK.
• Zampa V. & Lemaire B. (2002). Latent Semantic Analysis for User Modelling, Journal of Intelligent Information Systems 18(1), 15-30.

• Dessus, P. & Lemaire. B. (1999). Apex, un système d'aide à la préparation d'examens [Apex, a system that helps to prepare exams] . Sciences et Techniques Éducatives, 6-2, 409-415
• Dessus, P, & Lemaire, B. (2002). Using production to assess learning: an ILE that Fosters Self-Regulated Learning. In S. A. Cerri, G. Gouardères, F. Paraguaçu (Eds.), Intelligent Tutoring Systems (ITS 2002) (pp. 772-781). Berlin : Springer, LNCS 2363. [Abstract ]
• Dessus, P., Lemaire, B. & Vernier, A. (2000). Free-text assessment in a Virtual Campus . In K. Zreik (Ed.), Proc. Third International Conference on Human System Learning (CAPS'3). Paris : Europia, 61-76.
• Lemaire, B &  Dessus, P. (2001). A System to Assess the Semantic Content of Student Essays. Journal of Educational Computing Research, 24-3, 305-320.

• Graesser, A. C., Person, N. K., & Harter, D. (2001). Teaching tactics and dialog in Autotutor . International Journal of Artificial Intelligence in Education 12, 257-279 .
• Wiemer-Hastings, P. & Graesser, A. (2000). Select-a-Kibitzer: A computer tool that gives meaningful feedback on student compositions . Interactive Learning Environments .
• Wiemer-Hastings, P., Wiemer-Hastings, K. & Graesser, A. C. (1999). Improving an intelligent tutor's comprehension of students with Latent Semantic Analysis . In S. P. Lajoie, M. Vivet (Eds), Artificial Intelligence in Education. Amsterdam : IOS Press.

• Foltz, P. W., Gilliam, S. and Kendall, S. (2000). Supporting content-based feedback in online writing evaluation with LSA . Interactive Learning Environments, 8(2), 111-129.
• Foltz, P.W., Laham, D., & Landauer, T. K. (1999). Automated Essay Scoring: Applications to Educational Technology, In Proceedings of the ED-MEDIA Conference, Seattle.
• Foltz, P. W., Laham, D., & Landauer, T. K. (1999). The  Intelligent Essay Assessor: Applications to Educational Technology, Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 1(2).
• Kintsch, E., Steinhart, D., Stahl, G., Matthews, C., Lamb, R., & the LSA Group (2000). Developing Summarization Skills through the Use of LSA-Based Feedback . Interactive Learning Environments, 8-2, 87-109.
• Strait, M.J., Haynes, J.A., & Foltz, P.W. (2000). Applications of Latent Semantic Analysis to Lessons Learned Systems . In Aha, D.W., & Weber, R. (Eds.), Intelligent Lessons Learned Systems: Papers  from the 2000 Workshop (Technical Report WS-00-03). Menlo Park, CA: AAAI Press.

• Bellissens, C., Jeuniaux, P., Duran, N.D., McNamara, D. (2010). A Text Relatedness and Dependency Computational Model. Studia Informatica Universalis 8(1), 85-127.
  

• Stahl, G., de Paula, R. & LSA Research Group (sous presse). Evolution of an LSA-based Interactive Environment for Learning to Write Summaries. Interactive Learning Environments .
• Steinhart, D. (2001) Summary Street: an intelligent tutoring system for improving student writing  through the use of latent semantic analysis Unpublished doctoral dissertation,  Institute of Cognitive Science, University of Colorado, Boulder.
• Wade-Stein, D. & Kintsch, E. (2003) Summary Street: Interactive Computer Support for Writing, Technical Report from the Institute for Cognitive Science, University of Colorado, USA.
  

• Blustein, J., Webber, R. E. (1995). Using LSI to evaluate the quality of hypertext links. ACM SIGIR-IR and Automatic Construction of Hypermedia : a research workshop .
• Dessus, P. (1999). Vérification sémantique de liens hypertextes avec LSA [Hypertext Links Semantic Verification with LSA] . In J.-P. Balpe, A. Lelu, S. Natkin, I. Saleh (Eds), Hypertextes, hypermédias et internet (H2PTM'99) . Paris : Hermès, 119-129.

• Dessus, P. (1999). Où va la recherche en éducation ? Analyse factorielle de résumés de communications aux Biennales de l'éducation et de la formation. Année de la Recherche en Sciences de l'Education, 6, 201-219.
• Dong, A. (2004). Quantifying coherent thinking in design: a computational linguistics approach. In J.S. Gero (Ed.) Design Computing and Cognition'04, (pp. 521-540), Kluwer.
  
• Franceschetti, D.R., Karnavat, A., Marineau, J., McCallie, G.L., Olde, B.A., Terry, B.L., & Graesser, A.C. (2001).  Development of physics test corpora for latent semantic analysis. Proceedings of the 23th Annual Meeting of the Cognitive Science Society (pp. 297-300).  Mahwah, NJ: Erlbaum.
• Olde, B.A., Franceschetti, D.R., Karnavat, A., Graeser A.C. and the Tutoring Research Group. (2002). The Right Stuff: Do You Need to Sanitize Your Corpus When Using Latent Semantic Analysis?. Proceedings of the 24th Annual Meeting of the Cognitive Science Society. Fairfax. 708-713.

• Campbell R.S., Pennebaker J.W. (to appear) The Secret Life of Pronouns: Linking Latent Semantic Analysis of Writing Samples to Physical Health, Psychological Science.
• NEW Skoyles, J. R. (2011). Autism, Context/Noncontext Information Processing, and Atypical Development. Autism Research and Treatment, vol. 2011, Article ID 681627, 14 pages.
• NEW Skoyles, J.R., http://www.human-existence.com/autism.html, a web page about the links between LSA and autism.
  

• Kanejiya, D., Kumar, A. and S. Prasad. (2003) Automatic Evaluation of Students' Answers using Syntactically Enhanced LSA
  In Proceedings of the Human Language Technology Conference (HLT-NAACL 2003) Workshop on Building Educational Applications using NLP, Edmonton, Canada.
  
• Landauer, T. K., Laham, D., Rehder, B., & Schreiner, M. E., (1997). How well can passage meaning be derived without using word order? A comparison of Latent Semantic Analysis and humans. In M. G. Shafto & P. Langley (Eds.), Proceedings of the 19th annual meeting of the Cognitive  Science Society (pp. 412-417). Mawhwah, NJ: Erlbaum.
• Serafin, R. & Di Eugenio, B. (2004). FLSA: Extending LSA with features for dialogue act classification. In Proceedings of the 42nd Annual Meeting of the Association for Comuputational Linguistics (ACL'2004). 21-26 July, 2004, Barcelona, Spain.
• Wiemer-Hastings, P. (2000) Adding syntactic information to LSA. Proceedings of the Twenty-second Annual Conference of the Cognitive Science Society (pp. 989-993). Mahwah, NJ: Lawrence Erlbaum Associates
• Wiemer-Hastings, P., & Zipitria, I. (2001). Rules for syntax, vectors for semantics, Proceedings of the Twenty-third Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.