Aggressive language identification using word embeddings and sentiment features

This paper describes our participation in the First Shared Task on Aggression Identification. The method proposed relies on machine learning to identify social media texts which contain aggression. The main features employed by our method are information extracted from word embeddings and the output of a sentiment analyser. Several machine learning methods and different combinations of features were tried. The official submissions used Support Vector Machines and Random Forests. The official evaluation showed that for texts similar to the ones in the training dataset Random Forests work best, whilst for texts which are different SVMs are a better choice. The evaluation also showed that despite its simplicity the method performs well when compared with more elaborated methods

NP Animacy Identification for Anaphora Resolution

In anaphora resolution for English, animacy identification can play an integral role in the application of agreement restrictions between pronouns and candidates, and as a result, can improve the accuracy of anaphora resolution systems. In this paper, two methods for animacy identification are proposed and evaluated using intrinsic and extrinsic measures. The first method is a rule-based one which uses information about the unique beginners in WordNet to classify NPs on the basis of their animacy. The second method relies on a machine learning algorithm which exploits a WordNet enriched with animacy information for each sense. The effect of word sense disambiguation on the two methods is also assessed. The intrinsic evaluation reveals that the machine learning method reaches human levels of performance. The extrinsic evaluation demonstrates that animacy identification can be beneficial in anaphora resolution, especially in the cases where animate entities are identified with high precision

A corpus-based investigation of junk emails

Almost everyone who has an email account receives from time to time unwanted emails. These emails can be jokes from friends or commercial product offers from unknown people. In this paper we focus on these unwanted messages which try to promote a product or service, or to offer some “hot” business opportunities. These messages are called junk emails. Several methods to filter junk emails were proposed, but none considers the linguistic characteristics of junk emails. In this paper, we investigate the linguistic features of a corpus of junk emails, and try to decide if they constitute a distinct genre. Our corpus of junk emails was build from the messages received by the authors over a period of time. Initially, the corpus consisted of 1563, but after eliminating the duplications automatically we kept only 673 files, totalising just over 373,000 tokens. In order to decide if the junk emails constitute a different genre, a comparison with a corpus of leaflets extracted from BNC and with the whole BNC corpus is carried out. Several characteristics at the lexical and grammatical levels were identified

An evaluation of syntactic simplification rules for people with autism

Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR) at the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2014)Syntactically complex sentences constitute an obstacle for some people with Autistic Spectrum Disorders. This paper evaluates a set of simplification rules specifically designed for tackling complex and compound sentences. In total, 127 different rules were developed for the rewriting of complex sentences and 56 for the rewriting of compound sentences. The evaluation assessed the accuracy of these rules individually and revealed that fully automatic conversion of these sentences into a more accessible form is not very reliable.EC FP7-ICT-2011-

Thrombocytopenia in end-stage renal disease and chronic viral hepatitis B or C

Objectives. We evaluated platelet counts in end-stage renal disease and chronic viral hepatitis. Materials and Methods. We studied 70 patients with end-stage renal disease and chronic viral hepatitis and compared them to a control group of 45 patients without hepatitis. Results. The presence of viral hepatitis was associated with a higher prevalence of thrombocytopenia. Correlations between age, C-reactive protein, liver stiffness measurement, and platelet count were observed. C-reactive protein levels \u3e 10 mg/dl were associated with a lower risk of thrombocytopenia in patients with end-stage renal disease and chronic viral hepatitis, yet age \u3e 60 years, dialysis vintage \u3e 10 years, aspartate and alanine aminotransferase levels \u3e 20 IU/L, albumin levels \u3c 3.5 g/dl, and fibrosis stage ≥ 3 were not related. Conclusions. Chronic viral hepatitis leads to a higher prevalence of thrombocytopenia. Platelet counts in these patients begin to decrease significantly once liver fibrosis reaches stage III

Sleep Beliefs, Subjective Sleep Quality and Diurnal Preference – Findings from Depressed Patients

This study evaluated the relationship between dysfunctional sleep beliefs, circadian typology and self-reported sleep quality and insomnia. We assessed these parameters both in healthy controls and patients with depression. One hundred eighty six subjects were assessed and completed measures of sleep beliefs, sleep disturbance, sleep quality, daytime sleepiness, depressive symptoms and circadian typology. We found that sleep beliefs are slightly linked with the subjective sleep quality, but with neither the diurnal preference, nor the self-reported insomnia

Trouble on the road: Finding reasons for commuter stress from tweets

Intelligent Transportation Systems could benefit from harnessing social media content to get continuous feedback. In this work, we implement a system to identify reasons for stress in tweets related to traffic using a word vector strategy to select a reason from a predefined list generated by topic modeling and clustering. The proposed system, which performs better than standard machine learning algorithms, could provide inputs to warning systems for commuters in the area and feedback for the authorities.Published versio

Evaluation of Patients with Alopecia

This chapter outlines the clinical approaches for alopecic patients that are reliable in practice. We discuss three different categories of hair evaluation options: invasive methods (biopsy), semi-invasive methods (trichogram) and noninvasive methods. Besides describing the current status of diagnosis and quantification of alopecia, the chapter provides an objective assessment of these investigation tools: detailed medical history collection by structured interview and questionnaires, clinical examination of the scalp and other hair-bearing areas, laboratory investigations, assessment of hair loss distribution (patterned/diffuse/focal), dermoscopic evaluation, assessment of alopecia severity (by pull test, hair part width, counting hair test), common scales for hair loss staging, photography of alopecic areas, biopsy, trichogram, unit area trichogram, tug test, hair mount and microscopic evaluation, electron microscopy, hair card test, hair weight determination, hair densitometry, mechanical test of hair quality and computed hair analysis. Unfortunately, the disadvantages of most of these methods generate a lack of use in clinical practice, leading to few reliable evaluation methods for patients suffering from alopecia. We underline the necessity of easy, refined and precise evaluation tools for the assessment of alopecia patients