Data Mining Practical Machine Learning Tools and Techniques

tical perspective. For instance, there are only three passing references to kuru in a book of 650 pages. This edition reflects the renewed interest in the immunological theories of multiple sclerosis. More than half the text is devoted to Professor Lumsden's analysis of the pathoIogy and, in particular, the chemical pathology of the immune response. There is a great deal of original work devoted to the chemistry and behaviour of the immunoglobulins. Much of this appears in specialist journals and one must be grateful for the critical summary provided here. Professor Lumsden unequivocally sees the key to the problem of multiple sclerosis in the study of its immunochemistry, relegating infection by a virus or a slow virus to a quite subsidiary role. The clinical studies drawing on wide practical experience help to get one's prejudices about the illness onto a more reasoned footing. The section on treatment is still sadly limited. Dr. McAlpine found little to add to the regime which he described in 1955. McAlpine, Lumsden, and Acheson's reappraisal is an essential reference for the practising neurologist and the new edition makes important modification of and changes in emphasis from the edition of 1965.

https://jnnp.bmj.com/content/jnnp/36/5/894.3.full.pdf

The assessment of aphasia and related disorders

Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on very large corpora, and use these pre-trained models in downstream tasks. In this paper, we describe how we trained such high quality word representations for 157 languages. We used two sources of data to train these models: the free online encyclopedia Wikipedia and data from the common crawl project. We also introduce three new word analogy datasets to evaluate these word vectors, for French, Hindi and Polish. Finally, we evaluate our pre-trained word vectors on 10 languages for which evaluation datasets exists, showing very strong performance compared to previous models.

Learning Word Vectors for 157 Languages

Distributed word representations have recently been proven to be an invaluable resource for NLP. These representations are normally learned using neural networks and capture syntactic and semantic information about words. Information about word morphology and shape is normally ignored when learning word representations. However, for tasks like part-of-speech tagging, intra-word information is extremely useful, specially when dealing with morphologically rich languages. In this paper, we propose a deep neural network that learns character-level representation of words and associate them with usual word representations to perform POS tagging. Using the proposed approach, while avoiding the use of any handcrafted feature, we produce state-of-the-art POS taggers for two languages: English, with 97.32% accuracy on the Penn Treebank WSJ corpus; and Portuguese, with 97.47% accuracy on the Mac-Morpho corpus, where the latter represents an error reduction of 12.2% on the best previous known result.

/pdf/learning-character-level-representations-for-part-of-speech-i2dwfr91wo.pdf

Learning Character-level Representations for Part-of-Speech Tagging

Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus. These methods are limited by the quality and quantity of manually simplified corpora, which are expensive to build. In this paper, we conduct an in-depth adaptation of statistical machine translation to perform text simplification, taking advantage of large-scale paraphrases learned from bilingual texts and a small amount of manual simplifications with multiple references. Our work is the first to design automatic metrics that are effective for tuning and evaluating simplification systems, which will facilitate iterative development for this task.

/pdf/optimizing-statistical-machine-translation-for-text-2uqxl8rf9e.pdf

Optimizing Statistical Machine Translation for Text Simplification

Natural Language Processing (NLP) tools aiming at the diagnosis of language impairing dementias generally extract several textual metrics of narrative transcripts. However, the absence of sentence boundary segmentation in transcripts prevents the direct application of NLP methods which rely on these marks to work properly, such as taggers and parsers. We present a method to segment the transcripts into sentences and another to detect the disfluencies present in them, to serve as a preprocessing step for the application of subsequent NLP tools. Our methods use recurrent convolutional neural networks with prosodic, morphosyntactic features, and word embeddings. We evaluated both tasks intrinsically, analyzing the most important features, comparing the proposed methods to simpler ones, and identifying the main hits and misses. In addition, a final method was created to combine all tasks and it was evaluated extrinsically using 9 syntactic metrics of Coh-Metrix-Dementia. In the intrinsic evaluations, we showed that our method achieved (i) state-of-the-art results for the sentence segmentation task on impaired speech, and (ii) results that are similar to related works for the English language for disfluency detection tasks. Regarding the extrinsic evaluation, only 3 metrics showed a statistically significant difference between manual MCI transcripts and those generated by our method, suggesting that our method is capable to preprocess transcriptions to be further analyzed by NLP tools.

Sentence Segmentation and Disfluency Detection in Narrative Transcripts from Neuropsychological Tests

Diagnoses of Alzheimer's Disease (AD) and Mild Cognitive Impairment (CCL) are based on the analysis of the patient's cognitive functions by administering cognitive and neuropsychological assessment batteries. The use of retelling narratives is common to help identify and quantify the degree of dementia. In general, one point is awarded for each unit recalled, and the final score represents the number of units recalled. In this paper, we evaluated two clinical tasks: the automatic identification of which elements of a retold narrative were recalled; and the binary classification of the narrative produced by a patient, having the units identified as attributes, aiming at an automatic screening of patients with cognitive impairment. We used two transcribed retelling data sets in which sentences were divided and manually annotated with the information units. These data sets were then made publicly available. They are: the Arizona Battery for Communication and Dementia Disorders (ABCD) that contains narratives of patients with CCL and Healthy Controls and the Avaliacao da Linguagem no Envelhecimento (BALE), which includes narratives of patients with AD and CCLs as well as Healthy Controls. We evaluated two methods based on semantic similarity, referred to here as STS and Chunking, and transformed the multi-label problem of identifying elements of a retold narrative into binary classification problems, finding a cutoff point for the similarity value of each information unit. In this way, we were able to overcome two baselines for the two datasets in the SubsetAccuracy metric, which is the most punitive for the multi-label scenario. In binary classification, however, not all six machine learning methods evaluated performed better than the baselines methods. For ABCD, the best methods were Decision Trees and KNN, and for BALE, SVM with RBF kernel stood out.

/pdf/identificacao-automatica-de-unidades-de-informacao-em-testes-21aipducwg.pdf

Identificação automática de unidades de informação em testes de reconto de narrativas usando métodos de similaridade semântica

Portuguese is a less resourced language in what concerns foreign language learning. Aiming to inform a module of a system designed to support scientific written production of Spanish native speakers learning Portuguese, we developed an approach to automatically generate a lexicon of wrong words, reproducing language transfer errors made by such foreign learners. Each item of the artificially generated lexicon contains, besides the wrong word, the respective Spanish and Portuguese correct words. The wrong word is used to identify the interlanguage error and the correct Spanish and Portuguese forms are used to generate the suggestions. Keeping control of the correct word forms, we can provide correction or, at least, useful suggestions for the learners. We propose to combine two automatic procedures to obtain the error correction: i) a similarity measure and ii) a translation algorithm based on aligned parallel corpus. The similarity-based method achieved a precision of 52%, whereas the alignment-based method achieved a precision of 90%. In this paper we focus only on interlanguage errors involving suffixes that have different forms in both languages. The approach, however, is very promising to tackle other types of errors, such as gender errors.

/pdf/generating-a-lexicon-of-errors-in-portuguese-to-support-an-5b64ov15aj.pdf

Generating a lexicon of errors in portuguese to support an error identification system for spanish native learners

Writing well in English is a challenge for non-native English speakers. When readers are unable to comprehend what they read they just give up reading and fail to get to the content. In this paper we describe problems encountered in the English writing of scientific abstracts by Brazilian Portuguese speakers. We collected and analyzed a corpus of 115 abstracts in which we identified specific language-related errors that affect comprehension. We show that students who must write scientific papers may benefit significantly with practice exercises, and computer-based tools. The use of such tools can enhance the students' level of confidence and thus enable them to improve their writing.

Sandra Maria Aluísio

Papers

Sentence Segmentation and Disfluency Detection in Narrative Transcripts from Neuropsychological Tests

Identificação automática de unidades de informação em testes de reconto de narrativas usando métodos de similaridade semântica

Generating a lexicon of errors in portuguese to support an error identification system for spanish native learners

Approaches for Helping Brazilian Students Improve their Scientific Writings

Sumarização automática para simplificação de textos: experimentos e lições aprendidas