In this paper, the influence of intonation to recognize dialogue acts from speech is assessed. Assessment is based on an empirical approach: manually tagged data from a spoken-dialogue and video corpus are used in a CARTstyle machine learning algorithm to produce a predictive model. Our approach involves two general stages: the tagging task, and the development of machine learning experiments. ...

Part-of-speech (POS) tagging is the process of assigning each word of an input text into an appropriate morphological class. Automatic recognition of parts-of-speech is very important for high level NLP applications, since it would be usually infeasible to perform this task manually in practical systems. One approach to POS tagging uses morphological disambiguation which selects the most suitab...

There has been an increasing interest in utilizing a wide variety of knowledge sources in order to perform automatic tagging of speech events, such as sentence boundaries and dialogue acts. In addition to the word spoken, the prosodic content of the speech has been proved quite valuable in a variety of spoken language processing tasks such as sentence segmentation and tagging, disfluency detect...

This paper presents the results of main part-of-speech tagging of Turkish sentences using Conditional Random Fields (CRFs). Although CRFs are applied to many different languages for part-of-speech (POS) tagging, Turkish poses interesting challenges to be modeled with them. The challenges include issues related to the statistical model of the problem as well as issues related to computational co...

Unsupervised part-of-speech (POS) tagging has recently been shown to greatly benefit from Bayesian approaches where HMM parameters are integrated out, leading to significant increases in tagging accuracy. These improvements in unsupervised methods are important especially in specialized social media domains such as Twitter where little training data is available. Here, we take the Bayesian appr...

For one aspect of grammatical annotation, part-of-speech tagging, we investigate experimentally whether the ceiling on accuracy stems from limits to the precision of tag definition or limits to analysts’ ability to apply precise definitions, and we examine how analysts’ performance is affected by alternative types of semi-automatic support. We find that, even for analysts very well-versed in a ...

We describe how part-of-speech information delivered by a tagger (the mpro tool) has been integrated into the alep (Advanced Language Engineering Platform) system. For this we extended an approach described within the ls-gram project, which consisted in de ning the Text Handling component of alep in such a way that so-called \messy details" are handled within this subsystem, hence keeping the (...

Background: An ongoing assessment of the literature is difficult with the rapidly increasing volume of research publications and limited effective information extraction tools which identify entity relationships from text. A recent study reported development of Muscorian, a generic text processing tool for extracting proteinprotein interactions from text that achieved comparable performance to ...

Accurate part-of-speech (POS) tagging of natural language text data can add power to automated information retrieval and extraction. Brill's transformation-based learning (TBL) approach to automated POS tagging was introduced in 1992, combining virtues of rule-based and stochastic methods. Brill's innovative idea was to use machine learning techniques to search through all of rule space for the...

How to deal with part of speech (POS) tagging is a very important problem when we build a syntactic parsing system. We could preprocess the text with a POS tagger before perform parsing in a pipelined approach. Alternatively, we could perform POS tagging and parsing simultaneously in an integrated approach. Few, if any, comparisons have been made on such architecture issues for Chinese parsing....