47,200 research outputs found
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
Speaker segmentation and clustering
This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved
Non-Compositional Term Dependence for Information Retrieval
Modelling term dependence in IR aims to identify co-occurring terms that are
too heavily dependent on each other to be treated as a bag of words, and to
adapt the indexing and ranking accordingly. Dependent terms are predominantly
identified using lexical frequency statistics, assuming that (a) if terms
co-occur often enough in some corpus, they are semantically dependent; (b) the
more often they co-occur, the more semantically dependent they are. This
assumption is not always correct: the frequency of co-occurring terms can be
separate from the strength of their semantic dependence. E.g. "red tape" might
be overall less frequent than "tape measure" in some corpus, but this does not
mean that "red"+"tape" are less dependent than "tape"+"measure". This is
especially the case for non-compositional phrases, i.e. phrases whose meaning
cannot be composed from the individual meanings of their terms (such as the
phrase "red tape" meaning bureaucracy). Motivated by this lack of distinction
between the frequency and strength of term dependence in IR, we present a
principled approach for handling term dependence in queries, using both lexical
frequency and semantic evidence. We focus on non-compositional phrases,
extending a recent unsupervised model for their detection [21] to IR. Our
approach, integrated into ranking using Markov Random Fields [31], yields
effectiveness gains over competitive TREC baselines, showing that there is
still room for improvement in the very well-studied area of term dependence in
IR
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