Nitin Ramrakhiyani

Co-training for Extraction of Adverse Drug Reaction Mentions from Tweets

Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care. Current ADR surveillance systems are often associated with a substantial time lag before such events are officially published. On the other hand, online social media such as Twitter contain information about ADR events in real-time, much before any official reporting. Current state-of-the-art methods in ADR mention extraction use Recurrent Neural Networks (RNN), which typically need large labeled corpora. Towards this end, we propose a semi-supervised method based on co-training which can exploit a large pool of unlabeled tweets to augment the limited supervised training data, and as a result enhance the performance. Experiments with 0.1M tweets show that the proposed approach outperforms the state-of-the-art methods for the ADR mention extraction task by 5% in terms of F1 score.

Approaches to Temporal Expression Recognition in Hindi

Temporal annotation of plain text is considered a useful component of modern information retrieval tasks. In this work, different approaches for identification and classification of temporal expressions in Hindi are developed and analyzed. First, a rule-based approach is developed, which takes plain text as input and based on a set of hand-crafted rules, produces a tagged output with identified temporal expressions. This approach performs with a strict F1-measure of 0.83. In another approach, a CRF-based classifier is trained with human tagged data and is then tested on a test dataset. The trained classifier identifies the time expressions from plain text and further classifies them to various classes. This approach performs with a strict F1-measure of 0.78. Next, the CRF is replaced by an SVM-based classifier and the same experiment is performed with the same features. This approach is shown to be comparable to the CRF and performs with a strict F1-measure of 0.77. Using the rule base information as an additional feature enhances the performances to 0.86 and 0.84 for the CRF and SVM respectively. With three different comparable systems performing the extraction task, merging them to take advantage of their positives is the next step. As the first merge experiment, rule-based tagged data is fed to the CRF and SVM classifiers as additional training data. Evaluation results report an increase in F1-measure of the CRF from 0.78 to 0.8. Second, a voting-based approach is implemented, which chooses the best class for each token from the outputs of the three approaches. This approach results in the best performance for this task with a strict F1-measure of 0.88. In this process a reusable gold standard dataset for temporal tagging in Hindi is also developed. Named the ILTIMEX2012 corpus, it consists of 300 manually tagged Hindi news documents.

Temporal Expression Recognition in Hindi

Temporal annotation of plain text is considered as a useful component of modern information retrieval tasks. In this work, two approaches for identification and classification of temporal entities in Hindi are developed and analyzed. Firstly, a rule based approach is developed, which takes plain text as input and based on a set of hand-crafted rules, produces a tagged output with identified temporal expressions. This approach is shown to have a strict F1-measure of 0.83. In the other approach, a CRF based classifier is trained with human tagged data and is then tested on a test dataset. The trained classifier identifies the temporal expressions from plain text and further classifies them to various classes. This approach is shown to have a strict F1-measure of 0.78. In this process a reusable gold standard dataset for temporal tagging in Hindi was developed. Named the ILTIMEX2012 corpus, it consists of 300 manually tagged Hindi news documents.

Multi-task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets

Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care. Current ADR surveillance systems are often associated with a substantial time lag before such events are officially published. On the other hand, online social media such as Twitter contain information about ADR events in real-time, much before any official reporting. Current state-of-the-art in ADR mention extraction uses Recurrent Neural Networks (RNN), which typically need large labeled corpora. Towards this end, we propose a multi-task learning based method which can utilize a similar auxiliary task (adverse drug event detection) to enhance the performance of the main task, i.e., ADR extraction. Furthermore, in the absence of auxiliary task dataset, we propose a novel joint multi-task learning method to automatically generate weak supervision dataset for the auxiliary task when a large pool of unlabeled tweets is available. Experiments with 0.48M tweets show that the proposed approach outperforms the state-of-the-art methods for the ADR mention extraction task by 7.2% in terms of F1 score.

Role Models: Mining Role Transitions Data in IT Project Management

The notion of roles is crucial in project management across various domains. A role indicates a broad set of tasks, activities, deliverables and responsibilities that the person needs to carry out within a project. Assigning roles to team members clarifies the expectations of work items to be delivered by each and structures the interactions of the team among themselves as well as with external stakeholders. This paper analyzes a sizeable real-life dataset regarding the actual usage of roles in software development and maintenance projects in a large multinational IT organization. The paper introduces and formalizes concepts such as seniority level of a role, career progression and career lines, formulates various business questions related to role-based project management, proposes analytics techniques to answer them and outlines the actual results produced to answer the business questions. The business questions are related to dependencies between roles, patterns in role assignments and durations, predicting role changes, discovering insights useful for meeting career aspirations, interesting role sequences etc. The proposed analytics algorithms are based on Markov models, sequence mining, classification and survival analysis.

word2vec or JoBimText?: A Comparison for Lexical Expansion of Hindi Words

Exploration of distributional semantics for NLP tasks in Indian languages has been scarce. This work carries out a comparative analysis of two recent and high performing distributional semantics techniques namely word2vec and JoBimText. The task of lexical expansion of words in Hindi is considered for the analysis. A manual similarity assessment of the lexical expansions of words is employed for evaluation of the techniques. It can be observed that word2vec framework performs better than the JoBimText for various corpus sizes. Analysis of the results also presents insights on performance of the systems on various word types.