Papers by Suresh Manandhar
Abstract: This article describes a method for classifying dialogue utterances and detecting the i... more Abstract: This article describes a method for classifying dialogue utterances and detecting the interlocutor's agreement or disagreement. This labelling can help improve dialogue management by providing additional information on the utterance's content without deep parsing. The proposed technique improves upon state of the art approaches by using a Support Vector Machine cascade. A combination of three binary support vector machines in a cascade is employed to filter out utterances that are easy to classify, thus reducing the ...
ABSTRACT Statistical data on phonemes, useful in continuous speech recognition system, are presen... more ABSTRACT Statistical data on phonemes, useful in continuous speech recognition system, are presented. This paper explains basics of a simple system for phonemes, diphones and triphones statistics estimation from a text corpus of Polish language. Obtained results are ...
Proceedings of the 23rd International Conference on Computational Linguistics Posters, 2010
Proceedings of the 5th International Workshop on Semantic Evaluation, Jul 15, 2010
This paper presents an unsupervised graph-based method for automatic word sense induction and dis... more This paper presents an unsupervised graph-based method for automatic word sense induction and disambiguation. The innovative part of our method is the assignment of either a word or a word pair to each vertex of the constructed graph. Word senses are induced by clustering the constructed graph. In the disambiguation stage, each induced cluster is scored according to the number of its vertices found in the context of the target word. Our system participated in SemEval-2010 word sense induction and disambiguation task.
This paper is a survey of methods and algorithms for unsupervised learning of morphology. We prov... more This paper is a survey of methods and algorithms for unsupervised learning of morphology. We provide a description of the methods and algorithms used for morphological segmentation from a computational linguistics point of view. We survey morphological segmentation methods covering methods based on MDL (minimum description length), MLE (maximum likelihood estimation), MAP (maximum a posteriori), parametric and non-parametric Bayesian approaches. A review of the evaluation schemes for unsupervised morphological segmentation is also provided along with a summary of evaluation results on the Morpho Challenge evaluations.
Computational learning of natural language is often attempted without using the knowledge availab... more Computational learning of natural language is often attempted without using the knowledge available from other research areas such as psychology and linguistics. This can lead to systems that solve problems that are neither theoretically or practically useful. In this paper we present a system CLL which aims to learn natural language syntax in a way that is both computationally effective and psychologically plausible. This theoretically plausible system can also perform the practically useful task of unsupervised learning of syntax. CLL has then been applied to a corpus of declarative sentences from the Penn Treebank on which it has been shown to perform comparatively well with respect to much less psychologically plausible systems, which are significantly more supervised and are applied to somewhat simpler problems.
Abstract: This article describes a method for classifying dialogue utterances and detecting the i... more Abstract: This article describes a method for classifying dialogue utterances and detecting the interlocutor's agreement or disagreement. This labelling can help improve dialogue management by providing additional information on the utterance's content without deep parsing. The proposed technique improves upon state of the art approaches by using a Support Vector Machine cascade. A combination of three binary support vector machines in a cascade is employed to filter out utterances that are easy to classify, thus reducing the ...
2008 Ieee International Conference on Multimedia and Expo, May 26, 2008
Segmenting the speech signals on the basis of time-frequency analysis is the most natural approac... more Segmenting the speech signals on the basis of time-frequency analysis is the most natural approach. Boundaries are located in places where energy of some frequency subband rapidly changes. Speech segmentation method which bases on discrete wavelet transform, the resulting power spectrum and its derivatives is presented. This information allows to locate the boundaries of phonemes. A statistical classification method was used to check which features are useful. The efficiency of segmentation was verified on a male speaker taken from a corpus of Polish language.
2014 Ieee Acm International Conference on Advances in Social Networks Analysis and Mining, Aug 1, 2014
2014 Ieee Acm International Conference on Advances in Social Networks Analysis and Mining, Aug 1, 2014
A method is presented for automatically extending WordNet with the telic relationships proposed i... more A method is presented for automatically extending WordNet with the telic relationships proposed in Pustejovsky's lexicon model. The method extracts telic relationships from WordNet glosses by first selecting a telic word through a pattern matcher aided by a part-of-speech tagger and then employing a word disambiguation module to select the specific meaning (synset) of the telic word. The method is shown to be fruitful, inferring a number of useful relationships.
... Alistair Willis Suresh Manandhar ... An associated scope structure, F is de ned so that terms... more ... Alistair Willis Suresh Manandhar ... An associated scope structure, F is de ned so that terms representing quanti ers in F map onto elements in F . A relation is de ned over the terms in F , such that a quanti er in F outscopes some other quanti er in F only if the relation holds ...
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Papers by Suresh Manandhar