Sentence Level N-Gram Context Feature in Real-Word Spelling Error Detection and Correction: Unsupervised Corpus Based Approach

Spell checking is the process of finding misspelled words and possibly correcting them. Most of the modern commercial spell checkers use a straightforward approach to finding misspellings, which considered a word is erroneous when it is not found in the dictionary. However, this approach is not able to check the correctness of words in their context and this is called real-word spelling error. To solve this issue, in the state-of-the-art researchers use context feature at fixed size n-gram (i.e. tri-gram) and this reduces the effectiveness of model due to limited feature. In this paper, we address the problem of this issue by adopting sentence level n-gram feature for real-word spelling error detection and correction. In this technique, all possible word n-grams are used to learn proposed model about properties of target language and this enhance its effectiveness. In this investigation, the only corpus required to training proposed model is unsupervised corpus (or raw text) and this enables the model flexible to be adoptable for any natural languages. But, for demonstration purpose we adopt under-resourced languages such as Amharic, Afaan Oromo and Tigrigna. The model has been evaluated in terms of Recall, Precision, F-measure and a comparison with literature was made (i.e. fixed n-gram context feature) to assess if the technique used performs as good.  The experimental result indicates proposed model with sentence level n-gram context feature achieves a better result: for real-word error detection and correction achieves an average F-measure of 90.03%, 85.95%, and 84.24% for Amharic, Afaan Oromo and Tigrigna respectively. Keywords: Sentence level n-gram, real-word spelling error, spell checker, unsupervised corpus based spell checker DOI: 10.7176/JIEA/10-4-02 Publication date:September 30th 202

How did the discussion go: Discourse act classification in social media conversations

We propose a novel attention based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of the task is the complete categorization of possible pragmatic roles in informal textual discussions, contrary to extraction of question-answers, stance detection or sarcasm identification which are very much role specific tasks. Early attempt was made on a Reddit discussion dataset. We train our model on the same data, and present test results on two different datasets, one from Reddit and one from Facebook. Our proposed model outperformed the previous one in terms of domain independence; without using platform-dependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively to predict discourse roles of comments in Reddit and Facebook discussions. Efficiency of recurrent and convolutional architectures in order to learn discursive representation on the same task has been presented and analyzed, with different word and comment embedding schemes. Our attention mechanism enables us to inquire into relevance ordering of text segments according to their roles in discourse. We present a human annotator experiment to unveil important observations about modeling and data annotation. Equipped with our text-based discourse identification model, we inquire into how heterogeneous non-textual features like location, time, leaning of information etc. play their roles in charaterizing online discussions on Facebook

Advancements in eHealth Data Analytics through Natural Language Processing and Deep Learning

The healthcare environment is commonly referred to as "information-rich" but also "knowledge poor". Healthcare systems collect huge amounts of data from various sources: lab reports, medical letters, logs of medical tools or programs, medical prescriptions, etc. These massive sets of data can provide great knowledge and information that can improve the medical services, and overall the healthcare domain, such as disease prediction by analyzing the patient's symptoms or disease prevention, by facilitating the discovery of behavioral factors for diseases. Unfortunately, only a relatively small volume of the textual eHealth data is processed and interpreted, an important factor being the difficulty in efficiently performing Big Data operations. In the medical field, detecting domain-specific multi-word terms is a crucial task as they can define an entire concept with a few words. A term can be defined as a linguistic structure or a concept, and it is composed of one or more words with a specific meaning to a domain. All the terms of a domain create its terminology. This chapter offers a critical study of the current, most performant solutions for analyzing unstructured (image and textual) eHealth data. This study also provides a comparison of the current Natural Language Processing and Deep Learning techniques in the eHealth context. Finally, we examine and discuss some of the current issues, and we define a set of research directions in this area

mARC: Memory by Association and Reinforcement of Contexts

This paper introduces the memory by Association and Reinforcement of Contexts (mARC). mARC is a novel data modeling technology rooted in the second quantization formulation of quantum mechanics. It is an all-purpose incremental and unsupervised data storage and retrieval system which can be applied to all types of signal or data, structured or unstructured, textual or not. mARC can be applied to a wide range of information clas-sification and retrieval problems like e-Discovery or contextual navigation. It can also for-mulated in the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast to Conway approach, the objects evolve in a massively multidimensional space. In order to start evaluating the potential of mARC we have built a mARC-based Internet search en-gine demonstrator with contextual functionality. We compare the behavior of the mARC demonstrator with Google search both in terms of performance and relevance. In the study we find that the mARC search engine demonstrator outperforms Google search by an order of magnitude in response time while providing more relevant results for some classes of queries