Posting Act Tagging Using Transformation-Based Learning
نویسندگان
چکیده
In this article we present the application of transformation-based learning (TBL) [1] to the task of assigning tags to postings in online chat conversations. We define a list of posting tags that have proven useful in chat-conversation analysis. We describe the templates used for posting act tagging in the context of template selection. We extend traditional approaches used in part-of-speech tagging and dialogue act tagging by incorporating regular expressions into our templates. We close with a presentation of results that compare favorably with the application of TBL in dialogue act tagging.
منابع مشابه
Development of a Machine Learnable Discourse Tagging Tool
We have developed a discourse level tagging tool for spoken dialogue corpus using machine learning methods. As discourse level information, we focused on dialogue act, relevance and discourse segment. In dialogue act tagging, we have implemented a transformation-based learning procedure and resulted in 70% accuracy in open test. In relevance and discourse segment tagging, we have implemented a ...
متن کاملA semantic tagging tool for spoken dialogue corpus
In this paper, we report our semantic tagging tool for spoken dialogue corpus. This tagging tool can acquire analysis rules using Transformation-based Learning (TBL) from small scale training corpus. It can learn dialogue act tagging rules and semantic frame tagging rules. The precisions are 72% in dialogue act tagging and 58% of semantic frame tagging in open test.
متن کاملAn Investigation of Transformation-Based Learning in Discourse
This paper presents results from the first attempt to apply Transformation-Based Learning to a discourse-level Natural Language Processing task. To address two limitations of the standard algorithm, we developed a Monte Carlo version of TransformationBased Learning to make the method tractable for a wider range of problems without degradation in accuracy, and we devised a committee method for a...
متن کاملTheory and Algorithms for Information Extraction and Classification in Textual Data Mining
Regular expressions can be used as patterns to extract features from semi-structured and narrative text [8]. For example, in police reports a suspect’s height might be recorded as “{CD} feet {CD} inches tall”, where {CD} is the part of speech tag for a numeric value. The result in [1] shows us that regular expressions could have higher performance than explicit expressions in some applications ...
متن کاملDialogue Act Tagging with Transformation-Based Learning
For the task of recognizing dialogue acts, we are applying the Transformation-Based Learning (TBL) machine learning algorithm. To circumvent a sparse data problem, we extract values of well-motivated features of utterances, such as speaker direction, punctuation marks, and a new feature, called dialogue act cues, which we find to be more effective than cue phrases and word n-grams in practice. ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005