Cue Phrase Classification Using Machine Learning

Cue phrases may be used in a discourse sense to explicitly signal discourse structure, but also in a sentential sense to convey semantic rather than structural information. Correctly classifying cue phrases as discourse or sentential is critical in natural language processing systems that exploit discourse structure, e.g., for performing tasks such as anaphora resolution and plan recognition. This paper explores the use of machine learning for classifying cue phrases as discourse or sentential. Two machine learning programs (Cgrendel and C4.5) are used to induce classification models from sets of pre-classified cue phrases and their features in text and speech. Machine learning is shown to be an effective technique for not only automating the generation of classification models, but also for improving upon previous results. When compared to manually derived classification models already in the literature, the learned models often perform with higher accuracy and contain new linguistic insights into the data. In addition, the ability to automatically construct classification models makes it easier to comparatively analyze the utility of alternative feature representations of the data. Finally, the ease of retraining makes the learning approach more scalable and flexible than manual methods.Comment: 42 pages, uses jair.sty, theapa.bst, theapa.st

Exploring complex vowels as phrase break correlates in a corpus of English speech with ProPOSEL, a prosody and POS English lexicon

Real-world knowledge of syntax is seen as integral to the machine learning task of phrase break prediction but there is a deficiency of a priori knowledge of prosody in both rule-based and data-driven classifiers. Speech recognition has established that pauses affect vowel duration in preceding words. Based on the observation that complex vowels occur at rhythmic junctures in poetry, we run significance tests on a sample of transcribed, contemporary British English speech and find a statistically significant correlation between complex vowels and phrase breaks. The experiment depends on automatic text annotation via ProPOSEL, a prosody and part-of-speech English lexicon. Copyright © 2009 ISCA

A prosodic constraint on wh-extraction from preverbal infinitival subjects

This paper introduces a series of mitigating circumstances improving the acceptability of wh-extraction from preverbal infinitival subjects in Rioplatense Spanish. It is argued that the factor behind these amelioration effects is encoded in prosodic structure, much in line with the hypothesis that certain island restrictions apply at PF. The linguistic principle accounting for the phenomenon is proposed to be a faithfulness constraint at the syntax- prosody interface stating that an extraction domain XP cannot be mapped as a prosodic word ω at PF. An alternative syntactic account based on freezing is shown to be unable to capture the relevant contrasts.This paper introduces a series of mitigating circumstances improving the acceptability of wh-extraction from preverbal infinitival subjects in Rioplatense Spanish. It is argued that the factor behind these amelioration effects is encoded in prosodic structure, much in line with the hypothesis that certain island restrictions apply at PF. The linguistic principle accounting for the phenomenon is proposed to be a faithfulness constraint at the syntax-prosody interface stating that an extraction domain XP cannot be mapped as a prosodic word ω at PF. An alternative syntactic account based on freezing is shown to be unable to capture the relevant contrasts

Chapter 2: The Original ToBI System and the Evolution of the ToBI Framework

In this chapter, the authors will try to identify the essential properties of a ToBI framework annotation system by describing the development and design of the original ToBI conventions. In this description, the authors will overview the general phonological theory and the specific theory of Mainstream American English intonation and prosody that the authors decided to incorporate in the original ToBI tags. The authors will also state the practical principles that led us to make the decisions that the authors did. The chapter is organised as follows. Section 2.2 briefly chronicles how the MAE_ToBI system came into being. Section 2.3 briefly describes the consensus account of English intonation and prosody on which the MAE_ToBI system is based. Section 2.4 catalogues the different components of a MAE_ToBI transcription and lists the salient rules which constrain the relationships between different components. This section also expands upon the theoretical foundations and practical consequences of adopting the general structure of multiple labelling tiers, and particularly the separation of the labels for tones from the labels for indexing prosodic boundary strength. Section 2.5 then describes some of the extensions of the basic ToBI tiers that have been adopted by some sites. This section also compares our decisions about the number of tiers and about inter-tier constraints with the analogous decisions for some of the other ToBI systems described in this book. Section 2.6 discusses the status of the symbolic labels relative to the continuous phonetic records that are also an obligatory component of the MAE_ToBI transcription. Section 2.7 then closes by listing several open research questions that the authors would like to see addressed by MAE_ToBI users and the larger ToBI community

Computational Approaches to the Syntax–Prosody Interface: Using Prosody to Improve Parsing

Prosody has strong ties with syntax, since prosody can be used to resolve some syntactic ambiguities. Syntactic ambiguities have been shown to negatively impact automatic syntactic parsing, hence there is reason to believe that prosodic information can help improve parsing. This dissertation considers a number of approaches that aim to computationally examine the relationship between prosody and syntax of natural languages, while also addressing the role of syntactic phrase length, with the ultimate goal of using prosody to improve parsing. Chapter 2 examines the effect of syntactic phrase length on prosody in double center embedded sentences in French. Data collected in a previous study were reanalyzed using native speaker judgment and automatic methods (forced alignment). Results demonstrate similar prosodic splitting behavior as in English in contradiction to the original study’s findings. Chapter 3 presents a number of studies examining whether syntactic ambiguity can yield different prosodic patterns, allowing humans and/or computers to resolve the ambiguity. In an experimental study, humans disambiguated sentences with prepositional phrase- (PP)-attachment ambiguity with 49% accuracy presented as text, and 63% presented as audio. Machine learning on the same data yielded an accuracy of 63-73%. A corpus study on the Switchboard corpus used both prosodic breaks and phrase lengths to predict the attachment, with an accuracy of 63.5% for PP-attachment sentences, and 71.2% for relative clause attachment. Chapter 4 aims to identify aspects of syntax that relate to prosody and use these in combination with prosodic cues to improve parsing. The aspects identified (dependency configurations) are based on dependency structure, reflecting the relative head location of two consecutive words, and are used as syntactic features in an ensemble system based on Recurrent Neural Networks, to score parse hypotheses and select the most likely parse for a given sentence. Using syntactic features alone, the system achieved an improvement of 1.1% absolute in Unlabelled Attachment Score (UAS) on the test set, above the best parser in the ensemble, while using syntactic features combined with prosodic features (pauses and normalized duration) led to a further improvement of 0.4% absolute. The results achieved demonstrate the relationship between syntax, syntactic phrase length, and prosody, and indicate the ability and future potential of prosody to resolve ambiguity and improve parsing

The Spanish intonation of speakers of a Basque pitch-accent dialect

In this paper the main aspects of the intonation of broad focus declaratives in Lekeitio Spanish are described and analyzed. In this variety, accents are realized as pitch rises rather than falls, similarly to Standard Peninsular Spanish and unlike in Lekeitio Basque, the other native language of these speakers. Accentual valleys are aligned before the onset of the stressed syllable, except in final position in the utterance. Accentual peaks are aligned before the offset of the accented syllable, with an earlier alignment in accents in the object phrase. At the end of the subject phrase, peaks display later alignment. The number of unstressed syllables intervening between accents seems to affect F0 valley and peak alignment for certain positions. For non-object positions, F0 valleys align earlier as more unstressed syllables intervene between accents, and for the final position in the subject, F0 peaks align later as more unstressed syllables intervene between accents.Aquest article descriu i analitza els principals aspectes de l'entonació de les oracions declaratives de l'espanyol parlat a Lekeitio. En aquesta varietat, els accents tonals es realitzen com a moviments ascendents en lloc de descendents: en això s'apropen a la varietat estàndard d'espanyol peninsular i es distingeixen de l'altra seva llengua nativa, el basc parlat a Biscaia. Les valls s'alineen abans del començament de la síl·laba accentuada, llevat dels accents que es troben en posició final de frase. Els pics s'alineen abans del final de la síl·laba accentuada (fins i tot abans en posició d'objecte directe). Al final dels subjectes, els pics mostren més desplaçament cap a la síl·laba següent. El nombre de síl·labes àtones entre accents tonals també sembla afectar la posició de les valls i dels pics en algunes posicions. En posicions que no són d'objecte, les valls d'F0 s'alineen abans quan hi ha més síl·labes àtones intermèdies, i en posició final de subjecte, els pics se situen més tard quan hi ha més síl·labes àtones

An ERP study

Autism spectrum disorder (ASD) is frequently associated with communicative impairment, regardless of intelligence level or mental age. Impairment of prosodic processing in particular is a common feature of ASD. Despite extensive overlap in neural resources involved in prosody and music processing, music perception seems to be spared in this population. The present study is the first to investigate prosodic phrasing in ASD in both language and music, combining event-related brain potential (ERP) and behavioral methods. We tested phrase boundary processing in language and music in neuro-typical adults and high-functioning individuals with ASD. We targeted an ERP response associated with phrase boundary processing in both language and music – i.e., the Closure Positive Shift (CPS). While a language-CPS was observed in the neuro-typical group, for ASD participants a smaller response failed to reach statistical significance. In music, we found a boundary-onset music-CPS for both groups during pauses between musical phrases. Our results support the view of preserved processing of musical cues in ASD individuals, with a corresponding prosodic impairment. This suggests that, despite the existence of a domain-general processing mechanism (the CPS), key differences in the integration of features of language and music may lead to the prosodic impairment in ASD

Extending AuToBI to prominence detection in European Portuguese

This paper describes our exploratory work in applying the Automatic ToBI annotation system (AuToBI), originally developed for Standard American English, to European Portuguese. This work is motivated by the current availability of large amounts of (highly spontaneous) transcribed data and the need to further enrich those transcripts with prosodic information. Manual prosodic annotation, however, is almost impractical for extensive data sets. For that reason, automatic systems such as AuToBi stand as an alternate solution. We have started by applying the AuToBI prosodic event detection system using the existing English models to the prediction of prominent prosodic events (accents) in European Portuguese. This approach achieved an overall accuracy of 74% for prominence detection, similar to state-of-the-art results for other languages. Later, we have trained new models using prepared and spontaneous Portuguese data, achieving a considerable improvement of about 6% accuracy (absolute) over the existing English models. The achieved results are quite encouraging and provide a starting point for automatically predicting prominent events in European Portuguese.info:eu-repo/semantics/publishedVersio