Automated discourse analysis tools based on Natural Language Processing (NLP) aiming at the diagnosis of language-impairing dementias generally extract several textual metrics of narrative transcripts. However, the absence of sentence boundary segmentation in the transcripts prevents the direct application of NLP methods which rely on these marks in order to function properly, such as taggers and parsers. We present the first steps taken towards automatic neuropsychological evaluation based on narrative discourse analysis, presenting a new automatic sentence segmentation method for impaired speech. Our model uses recurrent convolutional neural networks with prosodic, Part of Speech (PoS) features, and word embeddings. It was evaluated intrinsically on impaired, spontaneous speech as well as normal, prepared speech. The results suggest that our model is robust for impaired speech and can be used in automated discourse analysis tools to differentiate narratives produced by Mild Cognitive Impairment and healthy elderly patients.
@cite_24: This work addresses two metadata annotation tasks, involved in the production of rich transcripts: automatic capitalization, and punctuation marks recovery. The main focus concerns broadcast news, using both manual and automatic speech transcripts. Different capitalization models were analysed and compared, and results support the ideia that generative approaches capture the structure of written corpora better, while the discriminative approaches are robust to ASR errors and suitable for dealing with speech transcripts. The so-called language dynamics has been addressed, and results indicate that the capitalization performance is affected by the temporal distance between the training and testing data. In what concerns the punctuation task, this study covers the three most frequent marks: full stop, comma, and question mark, combining lexical, acoustic, and prosodic information. Much of the research described here is language independent, but a special focus is given to the Portuguese language. This work provides the first evaluation results of these two tasks over European Portuguese broadcast news data.
@cite_18: Automatic analysis of impaired speech for screening or diagnosis is a growing research field; however there are still many barriers to a fully automated approach. When automatic speech recognition is used to obtain the speech transcripts, sentence boundaries must be inserted before most measures of syntactic complexity can be computed. In this paper, we consider how language impairments can affect segmentation methods, and compare the results of computing syntactic complexity metrics on automatically and manually segmented transcripts. We find that the important boundary indicators and the resulting segmentation accuracy can vary depending on the type of impairment observed, but that results on patient data are generally similar to control data. We also find that a number of syntactic complexity metrics are robust to the types of segmentation errors that are typically made.
@cite_25: In this paper we present a study comparing the performance of different systems found in the literature that perform the task of automatic text segmentation in sentences for English documents. We also show the difficulties found to adapt these systems to make them work with Portuguese documents and the results obtained after the adaptation. We analyzed two systems that use a machine learning approach: MxTerminator and Satz, and a customized system based on fixed rules expressed by Regular Expressions. The results achieved by the Satz system were surprisingly positive for Portuguese documents.
@cite_17: Sentence Boundary Detection (SBD) is a very important prerequisite for proper sentence analysis in different Natural Language Processing tasks. During the last years, many SBD methods have been used in the transcriptions produced by Automatic Speech Recognition systems and in well-structured texts (e.g. news, scientific texts). However, there are few researches about SBD in informal user-generated content such as web reviews, comments, and posts, which are not necessarily well written and structured. In this paper, we adapt and extend a well-known SBD method to the domain of the opinionated texts in the web. Particularly, we evaluate our proposal in a set of online product reviews and compare it with other traditional SBD methods. The experimental results show that we outperform these other methods.
Although there are proposed methods for sentence segmentation of Portuguese datasets @cite_25 @cite_24 @cite_17 , none of them are used for transcriptions produced in a clinical setting for the elderly with dementia and related syndromes. The study most similar to our scenario is @cite_18 , which proposes a segmentation method for aphasic speech based on lexical, PoS and prosodic features using tools and a generic acoustic model trained for English. Their approach is based on a CRF model, and the best results for this study were obtained for non-spontaneous broadcast news data.