Linguistic Markers of Influence in Informal Interactions

There has been a long standing interest in understanding `Social Influence' both in Social Sciences and in Computational Linguistics. In this paper, we present a novel approach to study and measure interpersonal influence in daily interactions. Motivated by the basic principles of influence, we attempt to identify indicative linguistic features of the posts in an online knitting community. We present the scheme used to operationalize and label the posts with indicator features. Experiments with the identified features show an improvement in the classification accuracy of influence by 3.15%. Our results illustrate the important correlation between the characteristics of the language and its potential to influence others.Comment: 10 pages, Accepted in NLP+CSS workshop for ACL (Association for Computational Linguistics) 201

A Thermodynamic Interpretation of Time for Superstring Rolling Tachyons

Rolling tachyon backgrounds, arising from open strings on unstable branes in bosonic string theory, can be related to a simple statistical mechanical model - Coulomb gas of point charges in two dimensions confined to a circle, the Dyson gas. In this letter we describe a statistical system that is dual to non-BPS branes in superstring theory. We argue that even though the concept of time is absent in the statistical dual sitting at equilibrium, the notion of time can emerge at the large number of particles $N \to \infty$ limit.Comment: 6 pages, 3 figures, v2: reference added, v3: minor clarification, version to appear in journa

Expediting Support for Social Learning with Behavior Modeling

An important research problem for Educational Data Mining is to expedite the cycle of data leading to the analysis of student learning processes and the improvement of support for those processes. For this goal in the context of social interaction in learning, we propose a three-part pipeline that includes data infrastructure, learning process analysis with behavior modeling, and intervention for support. We also describe an application of the pipeline to data from a social learning platform to investigate appropriate goal-setting behavior as a qualification of role models. Students following appropriate goal setters persisted longer in the course, showed increased engagement in hands-on course activities, and were more likely to review previously covered materials as they continued through the course. To foster this beneficial social interaction among students, we propose a social recommender system and show potential for assisting students in interacting with qualified goal setters as role models. We discuss how this generalizable pipeline can be adapted for other support needs in online learning settings.Comment: in The 9th International Conference on Educational Data Mining, 201

Robust Interactive Dialogue Interpretation

Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 B.2 Portions of the Interlingua Representation . . . . . . . . . . . . . . . . . . . 236 B.3 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 viii List of Tables 4.1 The Three Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 ix List of Figures 1.1 Parse Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.2 Combination Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3 Repair Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.1 Combination Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.1 Sample Partial Parse . . . . . . . . . . . . . . . . . . . . . . . . . .

Automatically Learning to Use Discourse Information For Disambiguation

this paper we discuss how we apply predictions from our plan-based discourse processor discussed in (Rose et al., 1995) to the problem of disambiguation. The work we report here has been done in the context of the Enthusiast Spanish to English translation system. The Enthusiast system is part of the JANUS Speech-to-Speech translation system (Woszcyna et al., 1993; Woszcyna et al., 1994; Suhm et al., 1994; Levin et al., 1995). We discuss and evaluate two different methods for combining context based predictions with non context based predictions, namely a genetic programming approach and a neural network approach. We demonstrate the advantage of incorporating context-based predictions over the purely non context-based approach discussed in (Lavie, 1995). The results presented here show a significant improvement over our previous results reported in (Levin et al., 1995). Introduction In this paper we discuss how we apply predictions from our plan-based discourse processor discussed in (Ros e et al., 1995) to the problem of disambiguation. The work reported here had been carried out in the context of the Enthusiast Spanish to English translation system (Woszcyna et al., 1993; Woszcyna et al., 1994; Suhm et al., 1994; Levin et al., 1995). The Enthusiast System is part of the JANUS Speech-to-Speech translation system Ambiguity is a major problem in a large scale machine translation system such as Enthusiast. This is because the parsing grammar must be large in order to cover the wide range of constructions which speakers use. Additionally, the flexibility of the GLR* skipping parser (Lavie, 1995) we use magnifies the problem. In this paper we Computational Linguistics Program, Philosophy, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh PA, 15213, [email protected]