Sense-based biomedical indexing and retrieval

International audienceThis paper tackles the problem of term ambiguity, especially for biomedical literature. We propose and evaluate two methods of Word Sense Disambiguation (WSD) for biomedical terms and integrate them to a sense-based document indexing and retrieval framework. Ambiguous biomedical terms in documents and queries are disambiguated using the Medical Subject Headings (MeSH) thesaurus and semantically indexed with their associated correct sense. Experimental evaluation carried out on the TREC9-FT 2000 collection shows that our approach of WSD and sense-based indexing and retrieval is promising

Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems

A growing number of applications, e.g. video surveillance and medical image analysis, require training recognition systems from large amounts of weakly annotated data while some targeted interactions with a domain expert are allowed to improve the training process. In such cases, active learning (AL) can reduce labeling costs for training a classifier by querying the expert to provide the labels of most informative instances. This paper focuses on AL methods for instance classification problems in multiple instance learning (MIL), where data is arranged into sets, called bags, that are weakly labeled. Most AL methods focus on single instance learning problems. These methods are not suitable for MIL problems because they cannot account for the bag structure of data. In this paper, new methods for bag-level aggregation of instance informativeness are proposed for multiple instance active learning (MIAL). The \textit{aggregated informativeness} method identifies the most informative instances based on classifier uncertainty, and queries bags incorporating the most information. The other proposed method, called \textit{cluster-based aggregative sampling}, clusters data hierarchically in the instance space. The informativeness of instances is assessed by considering bag labels, inferred instance labels, and the proportion of labels that remain to be discovered in clusters. Both proposed methods significantly outperform reference methods in extensive experiments using benchmark data from several application domains. Results indicate that using an appropriate strategy to address MIAL problems yields a significant reduction in the number of queries needed to achieve the same level of performance as single instance AL methods

Cyber bullying identification and tackling using natural language processing techniques

Abstract. As offensive content has a detrimental influence on the internet and especially in social media, there has been much research identifying cyberbullying posts from social media datasets. Previous works on this topic have overlooked the problems for cyberbullying categories detection, impact of feature choice, negation handling, and dataset construction. Indeed, many natural language processing (NLP) tasks, including cyberbullying detection in texts, lack comprehensive manually labeled datasets limiting the application of powerful supervised machine learning algorithms, including neural networks. Equally, it is challenging to collect large scale data for a particular NLP project due to the inherent subjectivity of labeling task and man-made effort. For this purpose, this thesis attempts to contribute to these challenges by the following. We first collected and annotated a multi-category cyberbullying (10K) dataset from the social network platform (ask.fm). Besides, we have used another publicly available cyberbullying labeled dataset, ’Formspring’, for comparison purpose and ground truth establishment. We have devised a machine learning-based methodology that uses five distinct feature engineering and six different classifiers. The results showed that CNN classifier with Word-embedding features yielded a maximum performance amidst all state-of-art classifiers, with a detection accuracy of 93\% for AskFm and 92\% for FormSpring dataset. We have performed cyberbullying category detection, and CNN architecture still provide the best performance with 81\% accuracy and 78\% F1-score on average. Our second purpose was to handle the problem of lack of relevant cyberbullying instances in the training dataset through data augmentation. For this end, we developed an approach that makes use of wordsense disambiguation with WordNet-aided semantic expansion. The disambiguation and semantic expansion were intended to overcome several limitations of the social media (SM) posts/comments, such as unstructured content, limited semantic content, among others, while capturing equivalent instances induced by the wordsense disambiguation-based approach. We run several experiments and disambiguation/semantic expansion to estimate the impact of the classification performance using both original and the augmented datasets. Finally, we have compared the accuracy score for cyberbullying detection with some widely used classifiers before and after the development of datasets. The outcome supports the advantage of the data-augmentation strategy, which yielded 99\% of classifier accuracy, a 5\% improvement from the base score of 93\%. Our third goal related to negation handling was motivated by the intuitive impact of negation on cyberbullying statements and detection. Our proposed approach advocates a classification like technique by using NegEx and POS tagging that makes the use of a particular data design procedure for negation detection. Performances using the negation-handling approach and without negation handling are compared and discussed. The result showed a 95\% of accuracy for the negated handed dataset, which corresponds to an overall accuracy improvement of 2\% from the base score of 93\%. Our final goal was to develop a software tool using our machine learning models that will help to test our experiments and provide a real-life example of use case for both end-users and research communities. To achieve this objective, a python based web-application was developed and successfully tested

Human-Level Performance on Word Analogy Questions by Latent Relational Analysis

This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason/stone is analogous to the pair carpenter/wood; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on semantic similarity measures has mainly been concerned with attributional similarity. For instance, Latent Semantic Analysis (LSA) can measure the degree of similarity between two words, but not between two relations. Recently the Vector Space Model (VSM) of information retrieval has been adapted to the task of measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus (they are not predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data (it is also used this way in LSA), and (3) automatically generated synonyms are used to explore reformulations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying noun-modifier relations, LRA achieves similar gains over the VSM, while using a smaller corpus