Anand Kumar M

Also published as: Anand Kumar Madasamy, Anand Kumar M


2020

pdf bib
NITK NLP at FinCausal-2020 Task 1 Using BERT and Linear models.
Hariharan R L | Anand Kumar M
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation

FinCausal-2020 is the shared task which focuses on the causality detection of factual data for financial analysis. The financial data facts don’t provide much explanation on the variability of these data. This paper aims to propose an efficient method to classify the data into one which is having any financial cause or not. Many models were used to classify the data, out of which SVM model gave an F-Score of 0.9435, BERT with specific fine-tuning achieved best results with F-Score of 0.9677.

2019

pdf bib
NITK-IT_NLP@NSURL2019: Transfer Learning based POS Tagger for Under Resourced Bhojpuri and Magahi Language
Anand Kumar M
Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers

2018

pdf bib
Amrita_student at SemEval-2018 Task 1: Distributed Representation of Social Media Text for Affects in Tweets
Nidhin A Unnithan | Shalini K. | Barathi Ganesh H. B. | Anand Kumar M | Soman K. P.
Proceedings of The 12th International Workshop on Semantic Evaluation

In this paper we did an analysis of “Affects in Tweets” which was one of the task conducted by semeval 2018. Task was to build a model which is able to do regression and classification of different emotions from the given tweets data set. We developed a base model for all the subtasks using distributed representation (Doc2Vec) and applied machine learning techniques for classification and regression. Distributed representation is an unsupervised algorithm which is capable of learning fixed length feature representation from variable length texts. Machine learning techniques used for regression is ’Linear Regression’ while ’Random Forest Tree’ is used for classification purpose. Empirical results obtained for all the subtasks by our model are shown in this paper.

pdf bib
CENNLP at SemEval-2018 Task 1: Constrained Vector Space Model in Affects in Tweets
Naveen J R | Barathi Ganesh H. B. | Anand Kumar M | Soman K P
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper discusses on task 1, “Affect in Tweets” sharedtask, conducted in SemEval-2018. This task comprises of various subtasks, which required participants to analyse over different emotions and sentiments based on the provided tweet data and also measure the intensity of these emotions for subsequent subtasks. Our approach in these task was to come up with a model on count based representation and use machine learning techniques for regression and classification related tasks. In this work, we use a simple bag of words technique for supervised text classification model as to compare, that even with some advance distributed representation models we can still achieve significant accuracy. Further, fine tuning on various parameters for the bag of word, representation model we acquired better scores over various other baseline models (Vinayan et al.) participated in the sharedtask.

pdf bib
TeamCEN at SemEval-2018 Task 1: Global Vectors Representation in Emotion Detection
Anon George | Barathi Ganesh H. B. | Anand Kumar M | Soman K P
Proceedings of The 12th International Workshop on Semantic Evaluation

Emotions are a way of expressing human sentiments. In the modern era, social media is a platform where we convey our emotions. These emotions can be joy, anger, sadness and fear. Understanding the emotions from the written sentences is an interesting part in knowing about the writer. In the amount of digital language shared through social media, a considerable amount of data reflects the sentiment or emotion towards some product, person and organization. Since these texts are from users with diverse social aspects, these texts can be used to enrich the application related to the business intelligence. More than the sentiment, identification of intensity of the sentiment will enrich the performance of the end application. In this paper we experimented the intensity prediction as a text classification problem that evaluates the distributed representation text using aggregated sum and dimensionality reduction of the glove vectors of the words present in the respective texts .

pdf bib
CENNLP at SemEval-2018 Task 2: Enhanced Distributed Representation of Text using Target Classes for Emoji Prediction Representation
Naveen J R | Hariharan V | Barathi Ganesh H. B. | Anand Kumar M | Soman K P
Proceedings of The 12th International Workshop on Semantic Evaluation

Emoji is one of the “fastest growing language ” in pop-culture, especially in social media and it is very unlikely for its usage to decrease. These are generally used to bring an extra level of meaning to the texts, posted on social media platforms. Providing such an added info, gives more insights to the plain text, arising to hidden interpretation within the text. This paper explains our analysis on Task 2, ” Multilingual Emoji Prediction” sharedtask conducted by Semeval-2018. In the task, a predicted emoji based on a piece of Twitter text are labelled under 20 different classes (most commonly used emojis) where these classes are learnt and further predicted are made for unseen Twitter text. In this work, we have experimented and analysed emojis predicted based on Twitter text, as a classification problem where the entailing emoji is considered as a label for every individual text data. We have implemented this using distributed representation of text through fastText. Also, we have made an effort to demonstrate how fastText framework can be useful in case of emoji prediction. This task is divide into two subtask, they are based on dataset presented in two different languages English and Spanish.

pdf bib
AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.
Vivek Vinayan | Anand Kumar M | Soman K P
Proceedings of The 12th International Workshop on Semantic Evaluation

The “Capturing Discriminative Attributes” sharedtask is the tenth task, conjoint with SemEval2018. The task is to predict if a word can capture distinguishing attributes of one word from another. We use GloVe word embedding, pre-trained on openly sourced corpus for this task. A base representation is initially established over varied dimensions. These representations are evaluated based on validation scores over two models, first on an SVM based classifier and second on a one dimension CNN model. The scores are used to further develop the representation with vector combinations, by considering various distance measures. These measures correspond to offset vectors which are concatenated as features, mainly to improve upon the F1score, with the best accuracy. The features are then further tuned on the validation scores, to achieve highest F1score. Our evaluation narrowed down to two representations, classified on CNN models, having a total dimension length of 1204 & 1203 for the final submissions. Of the two, the latter feature representation delivered our best F1score of 0.658024 (as per result).

2016

pdf bib
Amrita_CEN at SemEval-2016 Task 1: Semantic Relation from Word Embeddings in Higher Dimension
Barathi Ganesh HB | Anand Kumar M | Soman KP
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

pdf bib
AmritaCEN at SemEval-2016 Task 11: Complex Word Identification using Word Embedding
Sanjay S.P | Anand Kumar M | Soman K P
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

pdf bib
AMRITA_CEN@SemEval-2015: Paraphrase Detection for Twitter using Unsupervised Feature Learning with Recursive Autoencoders
Mahalakshmi Shanumuga Sundaram | Anand Kumar Madasamy | Soman Kotti Padannayil
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)