Thread Reconstruction in Conversational Data using Neural Coherence Models

Discussion forums are an important source of information. They are often used to answer specific questions a user might have and to discover more about a topic of interest. Discussions in these forums may evolve in intricate ways, making it difficult for users to follow the flow of ideas. We propose a novel approach for automatically identifying the underlying thread structure of a forum discussion. Our approach is based on a neural model that computes coherence scores of possible reconstructions and then selects the highest scoring, i.e., the most coherent one. Preliminary experiments demonstrate promising results outperforming a number of strong baseline methods.Comment: Neu-IR: Workshop on Neural Information Retrieval 201

Using Technology to Develop Preservice Teachers\u27 Reflective Thinking

Developing high-level reflection skills proves troublesome for some preservice teachers. To examine the potential of an online environment for increasing productive reflection, students in three sequential undergraduate education classes responded to regular online prompts. We coded student comments for productive and unproductive reflection, knowledge integration, and analysis of the four aspects of teaching (learners and learning, subject matter knowledge, assessment and instruction ) as described by Davis, Bain, & Harrington (2001). We adapted a scoring approach recommended by Davis & Linn, (2000); Davis (2003) to analyze what aspects of teaching preservice teachers included, emphasized, and integrated when they reflected on their own beliefs about teaching. Discussion examines the utility of online environments for producing productive preservice teacher reflection

Comparing the use of edited and unedited text in parser self-training

We compare the use of edited text in the form of newswire and unedited text in the form of discussion forum posts as sources for training material in a self-training experiment involving the Brown reranking parser and a test set of sentences from an online sports discussion forum. We find that grammars induced from the two automatically parsed corpora achieve similar Parseval f-scores, with the grammars induced from the discussion forum material being slightly superior. An error analysis reveals that the two types of grammars do behave differently

Mental distress detection and triage in forum posts: the LT3 CLPsych 2016 shared task system

This paper describes the contribution of LT3 for the CLPsych 2016 Shared Task on automatic triage of mental health forum posts. Our systems use multiclass Support Vector Machines (SVM), cascaded binary SVMs and ensembles with a rich feature set. The best systems obtain macro-averaged F-scores of 40% on the full task and 80% on the green versus alarming distinction. Multiclass SVMs with all features score best in terms of F-score, whereas feature filtering with bi-normal separation and classifier ensembling are found to improve recall of alarming posts