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.
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.
@cite_24 @cite_18 @cite_25 @cite_17
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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. We address the issue of consuming heterogeneous annotation data for Chinese word segmentation and part-of-speech tagging. We empirically analyze the diversity between two representative corpora, i.e. Penn Chinese Treebank (CTB) and PKU's People's Daily (PPD), on manually mapped data, and show that their linguistic annotations are systematically different and highly compatible. The analysis is further exploited to improve processing accuracy by (1) integrating systems that are respectively trained on heterogeneous annotations to reduce the approximation error, and (2) re-training models with high quality automatically converted data to reduce the estimation error. Evaluation on the CTB and PPD data shows that our novel model achieves a relative error reduction of 11 over the best reported result in the literature. 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. An unsupervised iterative approach for extracting a new lexicon (or unknown words) from a Chinese text corpus is proposed in this paper. Instead of using a non-iterative segmentation-merging-filtering-and-disambiguation approach, the proposed method iteratively integrates the contextual constraints (among word candidates) and a joint character association metric to progressively improve the segmentation results of the input corpus (and thus the new word list.) An augmented dictionary, which includes potential unknown words (in addition to known words), is used to segment the input corpus, unlike traditional approaches which use only known words for segmentation. In the segmentation process, the augmented dictionary is used to impose contextual constraints over known words and potential unknown words within input sentences; an unsupervised Viterbi Training process is then applied to ensure that the selected potential unknown words (and known words) maximize the likelihood of the input corpus. On the other hand, the joint character association metric (which reflects the global character association characteristics across the corpus) is derived by integrating several commonly used word association metrics, such as mutual information and entropy, with a joint Gaussian mixture density function; such integration allows the filter to use multiple features simultaneously to evaluate character association, unlike traditional filters which apply multiple features independently. The proposed method then allows the contextual constraints and the joint character association metric to enhance each other; this is achieved by iteratively applying the joint association metric to truncate unlikely unknown words in the augmented dictionary and using the segmentation result to improve the estimation of the joint association metric. The refined augmented dictionary and improved estimation are then used in the next iteration to acquire better segmentation and carry out more reliable filtering. Experiments show that both the precision and recall rates are improved almost monotonically, in contrast to non-iterative segmentation-merging-filtering-and-disambiguation approaches, which often sacrifice precision for recall or vice versa. With a corpus of 311,591 sentences, the performance is 76 (bigram), 54 (trigram), and 70 (quadragram) in F-measure, which is significantly better than using the non-iterative approach with F-measures of 74 (bigram), 46 (trigram), and 58 (quadragram).