In CCG and other highly lexicalized grammars, supertagging a sentence's words with their lexical categories is a critical step for efficient parsing. Because of the high degree of lexicalization in these grammars, the lexical categories can be very complex. Existing approaches to supervised CCG supertagging treat the categories as atomic units, even when the categories are not simple; when they encounter words with categories unseen during training, their guesses are accordingly unsophisticated.In this paper, we make use of the primitives and operators that constitute the lexical categories of categorial grammars. Instead of opaque labels, we treat lexical categories themselves as linear sequences. We present an LSTM-based model that replaces standard word-level classification with prediction of a sequence of primitives, similarly to LSTM decoders. Our model obtains state-of-the-art word accuracy for single-task English CCG supertagging, increases parser coverage and F 1 , and is able to produce novel categories. Analysis shows a synergistic effect between this decomposed view and incorporation of prediction history.

Profile hidden Markov models (Profile HMMs) are specific types of hidden Markov models used in biological sequence analysis. We propose the use of Profile HMMs for word-related tasks. We test their applicability to the tasks of multiple cognate alignment and cognate set matching, and find that they work well in general for both tasks. On the latter task, the Profile HMM method outperforms average and minimum edit distance. Given the success for these two tasks, we further discuss the potential applications of Profile HMMs to any task where consideration of a set of words is necessary.

<p class="abstract"><strong><span lang="EN-US">Background:</span></strong>Hearing loss is an invisible injury that has been viewed as an acceptable by-product of military service. It is imperative to detect hearing loss at early stage to take immediate remedial measures. In Indian armed forces the current method of assessment of hearing is primarily by Free Field Hearing which is obsolete and has numerous shortcomings. We contucted a study using free iOS application to detect hearing loss. The objectives of the study were to investigate the validity and reproducibility of app based hearing assement and free field hearing with clinical pure tone audiometer as gold standard. It is cross sectional intra-subject comparative study</p><p class="abstract"><strong><span lang="EN-US">Methods:</span></strong>The study was conducted at CHAF where 200 patients were accrued. Hearing assessment was carried out by Pure Tone Audiometry (PTA) which is gold standard. Thereafter these patients were subjected to hearing assessment by using windows application “freehearingtestsoftware.com” and by free field hearing (FFH).  </p><p class="abstract"><strong><span lang="EN-US">Results:</span></strong>Hearing assessment by FFH and hearing check app was compared with PTA. Hearing check app was found to be more sensitive than FFH (98% and 73%). Both modalities had high specificity (95% and 99%). The test retest reproducibility measured with Pearson correlation coefficient was high (0.99) with hearing check app.</p><p class="abstract"><strong><span lang="EN-US">Conclusions:</span></strong>Smart phone application like Hearing check app is a cheap and effective way to assess hearing with reasonable accuracy. It’s high sensitivity and high test retest reproducibility makes it an ideal tool for screening and early detection of hearing loss replacing out-dated free field hearing.</p><p class="abstract"><span lang="EN-US"> </span></p>

We present DIRECTL: an online discriminative sequence prediction model that employs a many-to-many alignment between target and source. Our system incorporates input segmentation, target character prediction, and sequence modeling in a unified dynamic programming framework. Experimental results suggest that DIRECTL is able to independently discover many of the language-specific regularities in the training data.

In this paper, we present the first statistical parser for Lambek categorial grammar (LCG), a grammatical formalism for which the graphical proof method known as proof nets is applicable. Our parser incorporates proof net structure and constraints into a system based on selfattention networks via novel model elements. Our experiments on an English LCG corpus show that incorporating term graph structure is helpful to the model, improving both parsing accuracy and coverage. Moreover, we derive novel loss functions by expressing proof net constraints as differentiable functions of our model output, enabling us to train our parser without ground-truth derivations.