We present SPARSAR, a system for the automatic analysis of poetry(and text) style. It makes use of NLP tools like tokenizers, sentence splitters, NER (Name Entity Recognition) tools, and a tagger. In addition, it adds syntactic and semantic structural analysis and prosodic modeling. We use a constituency parser to measure the structure of modifiers in NPs; and a dependency mapping to analyse the verbal complex and determine Polarity and Factuality. A phonological parser is used to account for OOVWs, in the process of grapheme to phoneme conversion of the poem. We also measure the prosody of the poem by associating mean durational values in msecs to each syllable from a database of syllable durations; to account for missing syllables we use a syllable parser. Eventually we produce six general indices that allow single poems as well as single poets to be compared. A fundamental component for the production of emotions is the one that performs affective and sentiment analysis. Lines associated to specific emotions are then marked to be pronounced with special care for the final module of the system, which is responsible for the production of expressive reading by a TTS system. Expressive reading is allowed by the possibility to interact with the TTS by means of specific markers and parameters.

An Expressive Poetry Reader

DELMONTE, Rodolfo;
2014-01-01

Abstract

We present SPARSAR, a system for the automatic analysis of poetry(and text) style. It makes use of NLP tools like tokenizers, sentence splitters, NER (Name Entity Recognition) tools, and a tagger. In addition, it adds syntactic and semantic structural analysis and prosodic modeling. We use a constituency parser to measure the structure of modifiers in NPs; and a dependency mapping to analyse the verbal complex and determine Polarity and Factuality. A phonological parser is used to account for OOVWs, in the process of grapheme to phoneme conversion of the poem. We also measure the prosody of the poem by associating mean durational values in msecs to each syllable from a database of syllable durations; to account for missing syllables we use a syllable parser. Eventually we produce six general indices that allow single poems as well as single poets to be compared. A fundamental component for the production of emotions is the one that performs affective and sentiment analysis. Lines associated to specific emotions are then marked to be pronounced with special care for the final module of the system, which is responsible for the production of expressive reading by a TTS system. Expressive reading is allowed by the possibility to interact with the TTS by means of specific markers and parameters.
Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/42974
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