31 research outputs found
Novel geometric features for off-line writer identification
Writer identification is an important field in forensic document examination. Typically, a writer identification system consists of two main steps: feature extraction and matching and the performance depends significantly on the feature extraction step. In this paper, we propose a set of novel geometrical features that are able to characterize different writers. These features include direction, curvature, and tortuosity. We also propose an improvement of the edge-based directional and chain code-based features. The proposed methods are applicable to Arabic and English handwriting. We have also studied several methods for computing the distance between feature vectors when comparing two writers. Evaluation of the methods is performed using both the IAM handwriting database and the QUWI database for each individual feature reaching Top1 identification rates of 82 and 87 % in those two datasets, respectively. The accuracies achieved by Kernel Discriminant Analysis (KDA) are significantly higher than those observed before feature-level writer identification was implemented. The results demonstrate the effectiveness of the improved versions of both chain-code features and edge-based directional features
Novel geometric features for off-line writer identification
Writer identification is an important field in forensic document examination. Typically, a writer identification system consists of two main steps: feature extraction and matching and the performance depends significantly on the feature extraction step. In this paper, we propose a set of novel geometrical features that are able to characterize different writers. These features include direction, curvature, and tortuosity. We also propose an improvement of the edge-based directional and chain code-based features. The proposed methods are applicable to Arabic and English handwriting. We have also studied several methods for computing the distance between feature vectors when comparing two writers. Evaluation of the methods is performed using both the IAM handwriting database and the QUWI database for each individual feature reaching Top1 identification rates of 82 and 87 % in those two datasets, respectively. The accuracies achieved by Kernel Discriminant Analysis (KDA) are significantly higher than those observed before feature-level writer identification was implemented. The results demonstrate the effectiveness of the improved versions of both chain-code features and edge-based directional features.Qatar National Research Fund through the National Priority Research Program (NPRP) No. 09-864-1-128Scopu
Sentiment Analysis in Comments Associated to News Articles: Application to Al Jazeera Comments
Sentiment analysis is a very important research task that aims at understanding the general sentiment of a specific community or group of people. Sentiment analysis of Arabic content is still in its early development stages. In the scope of Islamic content mining, sentiment analysis helps understanding what topics Muslims around the world are discussing, which topics are trending and also which topics will be trending in the future.
This study has been conducted on a dataset of 5000 comments on news articles collected from Al Jazeera Arabic website. All articles were about the recent war against the Islamic State. The database has been annotated using Crowdflower which is website for crowdsourcing annotations of datasets. Users manually selected whether the sentiment associated with the comment was positive or negative or neutral. Each comment has been annotated by four different users and each annotation is associated with a confidence level between 0 and 1. The confidence level corresponds to whether the users who annotated the same comment agreed or not (1 corresponds to full agreement between the four annotators and 0 to full disagreement).
Our method represents the corpus by a binary relation between the set of comments (x) and the set of words (y). A relation exists between the comment (x) and the word (y) if, and only if, (x) contains (y). Three binary relations are created for comments associated with positive, negative and neutral sentiments. Our method then extracts keywords from the obtained binary relations using the hyper concept method [1]. This method decomposes the original relation into non-overlapping rectangles and highlights for each rectangle the most representative keyword. The output is a list of keywords sorted in a hierarchical ordering of importance. The obtained keyword list associated with positive, negative and neutral comments are fed into a random forest classifier of 1000 random trees in order to predict the sentiment associated with each comment of the test set.
Experiments have been conducted after splitting the database into 70% training and 30% testing subsets. Our method achieves a correct classification rate of 71% when considering annotations with all values of confidence and even 89% when only considering the annotation with a confidence value equal to 1. These results are very promising and testify of the relevance of the extracted keywords.
In conclusion, the hyper concept method extracts discriminative keywords which are used in order to successfully distinguish between comments containing positive, negative and neutral sentiments. Future work includes performing further experiments by using a varying threshold level for the confidence value. Moreover, by applying a part of speech tagger, it is planned to perform keyword extraction on words corresponding to specific grammatical roles (adjectives, verbs, nouns… etc.). Finally, it is also planned to test this method on publicly available datasets such as the Rotten Tomatoes Movie Reviews dataset [2].
Acknowledgment
This contribution was made possible by NPRP grant #06-1220-1-233 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.qscienc
Named Entity Disambiguation using Hierarchical Text Categorization
Named entity extraction is an important step in natural language processing. It aims at finding the entities which are present in text such as organizations, places or persons. Named entities extraction is of a paramount importance when it comes to automatic translation as different named entities are translated differently. Named entities are also very useful for advanced search engines which aim at searching for a detailed information regarding a specific entity. Named entity extraction is a difficult problem as it usually requires a disambiguation step as the same word might belong to different named entities depending on the context. This work has been conducted on the ANERCorp named entities database. This Arabic database contains four different named entities: person, organization, location and miscellaneous. The database contains 6099 sentences, out of which 60% are used for training 20% for validation and 20% for testing. Our method for named entity extraction contains two main steps: the first step predicts the list of named entities which are present at the sentence level. The second step predicts the named entity of each word of the sentence. The prediction of the list of named entities at the sentence level is done through separating the document into sentences using punctuation marks. Subsequently, a binary relation between the set of sentences (x) and the set of words (y) is created from the obtained list of sentences. A relation exists between the sentence (x) and the word (y) if, and only if, (x) contains (y). A binary relation is created for each category of named entities (person, organization, location and miscellaneous). If a sentence contains several named entities, it is duplicated in the relation corresponding to each one of them. Our method then extracts keywords from the obtained binary relations using the hyper concept method [1]. This method decomposes the original relation into non-overlapping rectangles and highlights for each rectangle the most representative keyword. The output is a list of keywords sorted in a hierarchical ordering of importance. The obtained keyword list associated with each category of named entities are fed into a random forest classifier of 10000 random trees in order to predict the list of named entities associated with each sentence. The random forest classifier produces for each sentence the list of probabilities corresponding to the existence of each category of named entities within the sentence. Random Forest [sentence(i)] = (P(Person),P(Organization),P(Location),P(miscellaneous)). Subsequently, the sentence is associated with the named entities for which the corresponding probability is larger than a threshold set empirically on the validation set. In the second step, we create a lookup table associating to each word in the database, the list of named entities to which it corresponds in the training set. For unseen sentences of the test set, the list of named entities predicted at the sentence level is produced, and for each word, the list of predicted named entities is also produced using the lookup table previously built. Ultimately, for each word, the intersection between the two predicted lists of named entities (at the sentence and the word level) will give the final predicted named entity. In the case where more than one named entity is produced at this stage, the one with the maximum probability is kept. We obtained an accuracy of 76.58% when only considering lookup tables of named entities produced at the word level. When performing the intersection with the list produced at the sentence level the accuracy reaches 77.96%. In conclusion, the hierarchical named entity extraction leads to improved results over direct extraction. Future work includes the use of other linguist features and larger lookup table in order to improve the results. Validation on other state of the art databases is also considered. Acknowledgements This contribution was made possible by NPRP grant #06-1220-1-233 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.qscienc
An explainable Transformer-based deep learning model for the prediction of incident heart failure
Predicting the incidence of complex chronic conditions such as heart failure
is challenging. Deep learning models applied to rich electronic health records
may improve prediction but remain unexplainable hampering their wider use in
medical practice. We developed a novel Transformer deep-learning model for more
accurate and yet explainable prediction of incident heart failure involving
100,071 patients from longitudinal linked electronic health records across the
UK. On internal 5-fold cross validation and held-out external validation, our
model achieved 0.93 and 0.93 area under the receiver operator curve and 0.69
and 0.70 area under the precision-recall curve, respectively and outperformed
existing deep learning models. Predictor groups included all community and
hospital diagnoses and medications contextualised within the age and calendar
year for each patient's clinical encounter. The importance of contextualised
medical information was revealed in a number of sensitivity analyses, and our
perturbation method provided a way of identifying factors contributing to risk.
Many of the identified risk factors were consistent with existing knowledge
from clinical and epidemiological research but several new associations were
revealed which had not been considered in expert-driven risk prediction models
Stratification of diabetes in the context of comorbidities, using representation learning and topological data analysis
Diabetes is a heterogenous, multimorbid disorder with a large variation in manifestations, trajectories, and outcomes. The aim of this study is to validate a novel machine learning method for the phenotyping of diabetes in the context of comorbidities. Data from 9967 multimorbid patients with a new diagnosis of diabetes were extracted from Clinical Practice Research Datalink. First, using BEHRT (a transformer-based deep learning architecture), the embeddings corresponding to diabetes were learned. Next, topological data analysis (TDA) was carried out to test how different areas in high-dimensional manifold correspond to different risk profiles. The following endpoints were considered when profiling risk trajectories: major adverse cardiovascular events (MACE), coronary artery disease (CAD), stroke (CVA), heart failure (HF), renal failure (RF), diabetic neuropathy, peripheral arterial disease, reduced visual acuity and all-cause mortality. Kaplan Meier curves were plotted for each derived phenotype. Finally, we tested the performance of an established risk prediction model (QRISK) by adding TDA-derived features. We identified four subgroups of patients with diabetes and divergent comorbidity patterns differing in their risk of future cardiovascular, renal, and other microvascular outcomes. Phenotype 1 (young with chronic inflammatory conditions) and phenotype 2 (young with CAD) included relatively younger patients with diabetes compared to phenotypes 3 (older with hypertension and renal disease) and 4 (older with previous CVA), and those subgroups had a higher frequency of pre-existing cardio-renal diseases. Within ten years of follow-up, 2592 patients (26%) experienced MACE, 2515 patients (25%) died, and 2020 patients (20%) suffered RF. QRISK3 model’s AUC was augmented from 67.26% (CI 67.25–67.28%) to 67.67% (CI 67.66–67.69%) by adding specific TDA-derived phenotype and the distances to both extremities of the TDA graph improving its performance in the prediction of CV outcomes. We confirmed the importance of accounting for multimorbidity when risk stratifying heterogenous cohort of patients with new diagnosis of diabetes. Our unsupervised machine learning method improved the prediction of clinical outcomes