Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

Cue Phrase Classification Using Machine Learning

Cue phrases may be used in a discourse sense to explicitly signal discourse structure, but also in a sentential sense to convey semantic rather than structural information. Correctly classifying cue phrases as discourse or sentential is critical in natural language processing systems that exploit discourse structure, e.g., for performing tasks such as anaphora resolution and plan recognition. This paper explores the use of machine learning for classifying cue phrases as discourse or sentential. Two machine learning programs (Cgrendel and C4.5) are used to induce classification models from sets of pre-classified cue phrases and their features in text and speech. Machine learning is shown to be an effective technique for not only automating the generation of classification models, but also for improving upon previous results. When compared to manually derived classification models already in the literature, the learned models often perform with higher accuracy and contain new linguistic insights into the data. In addition, the ability to automatically construct classification models makes it easier to comparatively analyze the utility of alternative feature representations of the data. Finally, the ease of retraining makes the learning approach more scalable and flexible than manual methods.Comment: 42 pages, uses jair.sty, theapa.bst, theapa.st

Formulating representative features with respect to document genre classification

Genre classification (e.g. whether a document is a scientific article or magazine article) is closely bound to the physical and conceptual structure of document as well as the level of depth involved in the text. Hence, it provides a means of ranking documents retrieved by search tools according to metrics other than topical similarity. Moreover, the structural information derived from genre classification can be used to locate target information within the text. In previous studies, the detection of genre classes has been attempted by using some normalised frequency of terms or combinations of terms in the document (here, we are using term as a reference to words, phrases, syntactic units, sentences and paragraphs, as well as other patterns derived from deeper linguistic or semantic analysis). These approaches largely neglect how the term is distributed throughout the document. Here, we report the results of automated experiments based on distributive statistics of words in order to present evidence that term distribution pattern is a better indicator of genre class than term frequency.

Searching for Ground Truth: a stepping stone in automating genre classification

This paper examines genre classification of documents and its role in enabling the effective automated management of digital documents by digital libraries and other repositories. We have previously presented genre classification as a valuable step toward achieving automated extraction of descriptive metadata for digital material. Here, we present results from experiments using human labellers, conducted to assist in genre characterisation and the prediction of obstacles which need to be overcome by an automated system, and to contribute to the process of creating a solid testbed corpus for extending automated genre classification and testing metadata extraction tools across genres. We also describe the performance of two classifiers based on image and stylistic modeling features in labelling the data resulting from the agreement of three human labellers across fifteen genre classes.

A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts

Active learning and the Irish treebank

We report on our ongoing work in developing the Irish Dependency Treebank, describe the results of two Inter annotator Agreement (IAA) studies, demonstrate improvements in annotation consistency which have a knock-on effect on parsing accuracy, and present the final set of dependency labels. We then go on to investigate the extent to which active learning can play a role in treebank and parser development by comparing an active learning bootstrapping approach to a passive approach in which sentences are chosen at random for manual revision. We show that active learning outperforms passive learning, but when annotation effort is taken into account, it is not clear how much of an advantage the active learning approach has. Finally, we present results which suggest that adding automatic parses to the training data along with manually revised parses in an active learning setup does not greatly affect parsing accuracy