Knowledge of Dialog Acts (DAs) is important for the automatic understanding and summarization of meetings. Current approaches rely on a lot of hand labeled data to train automatic taggers. One approach that has been successful in reducing the amount of training data in other areas of NLP is active learning. We ask if active learning with lexical cues can help for this task and this domain. To better address this question, we explore active learning for two different types of DA models – hidden Markov models (HMMs) and maximum entropy (maxent). 1
© 2014 IEEE. In this paper, a spoken command and control interface that acquires spoken language thr...
For many natural language applications it is desirable to be able to automatically tag utterances a...
The key idea behind active learning is that a machine learning algorithm can achieve greater accurac...
Knowledge of Dialog Acts (DAs) is important for the automatic understanding and summarization of mee...
In the construction of a part-of-speech an-notated corpus, we are constrained by a fixed budget. A f...
Active learning is a useful technique that allows for a considerably reduction of the amount of data...
Active learning is a supervised machine learning technique in which the learner is in control of the...
Active learning techniques were employed for classification of dialogue acts over two dialogue corpo...
Supervised machine learning methods are increasingly employed in political science. Such models requ...
Many applications of spoken-language systems can benefit from having access to annotations of prosod...
Detecting discourse patterns such as dialog acts (DAs) is an important factor for processing spoken ...
Statistical machine learning has become an integral technology for solving many informatics applicat...
Traditional supervised machine learning algorithms are expected to have access to a large corpus of ...
In natural language acquisition, it is difficult to gather the annotated data needed for supervised ...
Active learning reduces annotation costs for supervised learning by concentrating labelling efforts ...
© 2014 IEEE. In this paper, a spoken command and control interface that acquires spoken language thr...
For many natural language applications it is desirable to be able to automatically tag utterances a...
The key idea behind active learning is that a machine learning algorithm can achieve greater accurac...
Knowledge of Dialog Acts (DAs) is important for the automatic understanding and summarization of mee...
In the construction of a part-of-speech an-notated corpus, we are constrained by a fixed budget. A f...
Active learning is a useful technique that allows for a considerably reduction of the amount of data...
Active learning is a supervised machine learning technique in which the learner is in control of the...
Active learning techniques were employed for classification of dialogue acts over two dialogue corpo...
Supervised machine learning methods are increasingly employed in political science. Such models requ...
Many applications of spoken-language systems can benefit from having access to annotations of prosod...
Detecting discourse patterns such as dialog acts (DAs) is an important factor for processing spoken ...
Statistical machine learning has become an integral technology for solving many informatics applicat...
Traditional supervised machine learning algorithms are expected to have access to a large corpus of ...
In natural language acquisition, it is difficult to gather the annotated data needed for supervised ...
Active learning reduces annotation costs for supervised learning by concentrating labelling efforts ...
© 2014 IEEE. In this paper, a spoken command and control interface that acquires spoken language thr...
For many natural language applications it is desirable to be able to automatically tag utterances a...
The key idea behind active learning is that a machine learning algorithm can achieve greater accurac...
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