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Found 162 papers, 26 papers with code
Experiences of Adapting Multimodal Machine Translation Techniques for Hindi
In this paper, we evaluate the existing methods on Hindi and report our findings.
Co-attention based Multimodal Factorized Bilinear Pooling for Internet Memes Analysis
Memes are a new type of social media communication found on social platforms.
Towards Explainable Dialogue System: Explaining Intent Classification using Saliency Techniques
In the process, we conclude that saliency method of eLRP (epsilon Layerwise Relevance Propagation) is a prominent process for highlighting the important features of the input responsible for the current classification which results in significant insights to the inner workings, such as the reasons for misclassification by the black box model.
EduMT: Developing Machine Translation System for Educational Content in Indian Languages
In this paper, we explore various approaches to build Hindi to Bengali Neural Machine Translation (NMT) systems for the educational domain.
EM-PERSONA: EMotion-assisted Deep Neural Framework for PERSONAlity Subtyping from Suicide Notes
We address the challenging task of personality subtyping from suicide notes.
PEPDS: A Polite and Empathetic Persuasive Dialogue System for Charity Donation
Our experimental results demonstrate that PEPDS increases the rate of persuasive responses with emotion and politeness acknowledgement compared to the current state-of-the-art dialogue models, while also enhancing the dialogue’s engagement and maintaining the linguistic quality.
HindiMD: A Multi-domain Corpora for Low-resource Sentiment Analysis
Social media platforms such as Twitter have evolved into a vast information sharing platform, allowing people from a variety of backgrounds and expertise to share their opinions on numerous events such as terrorism, narcotics and many other social issues.
Pre-training Synthetic Cross-lingual Decoder for Multilingual Samples Adaptation in E-Commerce Neural Machine Translation
We perform the experiments over eight low-resource and three high resource language pairs from the generic domain, and two language pairs from the product review domains.
Unknown Intent Detection Using Multi-Objective Optimization on Deep Learning Classifiers
This task of separating the unknown intent samples from known intents one is challenging as the unknown user intent can range from intents similar to the predefined intents to something completely different.
IITP-MT at WAT2021: Indic-English Multilingual Neural Machine Translation using Romanized Vocabulary
This paper describes the systems submitted to WAT 2021 MultiIndicMT shared task by IITP-MT team.
Modelling Source- and Target- Language Syntactic Information as Conditional Context in Interactive Neural Machine Translation
In this study, we model source-language syntactic constituency parse and target-language syntactic descriptions in the form of supertags as conditional context for interactive prediction in neural MT (NMT).
Product Review Translation using Phrase Replacement and Attention Guided Noise Augmentation
Given that 44% of Indian population speaks and operates in Hindi language and we address the above challenges by presenting an English–to–Hindi neural machine translation (NMT) system to translate the product reviews available on e-commerce websites by creating an in-domain parallel corpora and handling various types of noise in reviews via two data augmentation techniques and viz.
Sentiment Preservation in Review Translation using Curriculum-based Re-inforcement Framework
Machine Translation (MT) systems often fail to preserve different stylistic and pragmatic properties of the source text (e. g. sentiment and emotion and gender traits and etc.)
Investigating Active Learning in Interactive Neural Machine Translation
It also improves the present state-of-the-art by 0. 35 and 0. 12 BLEU points for German-English and Spanish-English and respectively.
SEPRG: Sentiment aware Emotion controlled Personalized Response Generation
We design a Transformer based Dialogue Generation framework, that generates responses that are sensitive to the emotion of the user and corresponds to the persona and sentiment as well.
Product Review Translation: Parallel Corpus Creation and Robustness towards User-generated Noisy Text
We train an English–to–Hindi neural machine translation (NMT) system to translate the product reviews available on e-commerce websites.
COMMA-DEER: COmmon-sense Aware Multimodal Multitask Approach for Detection of Emotion and Emotional Reasoning in Conversations
We present and discuss a novel task of detecting emotional reasoning (ER) and accompanying emotions in conversations.
A Sentiment and Emotion Aware Multimodal Multiparty Humor Recognition in Multilingual Conversational Setting
We design a multitasking framework wherein we first propose a Context Transformer to capture the deep contextual relationships with the input utterances.
PoliSe: Reinforcing Politeness Using User Sentiment for Customer Care Response Generation
The interaction between a consumer and the customer service representative greatly contributes to the overall customer experience.
A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference
We perform experiments on English-Hindi language pairs in the cross-lingual setting to find out that our novel loss formulation could enhance the performance of the baseline model by up to 2%.
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems
Even the most well-intended and reasoned persuasive conversations can fall through in the absence of empathetic connection between the speaker and listener.
Team IITP-AINLPML at WASSA 2022: Empathy Detection, Emotion Classification and Personality Detection
Computational comprehension and identifying emotional components in language have been critical in enhancing human-computer connection in recent years.
IITP-MT at CALCS2021: English to Hinglish Neural Machine Translation using Unsupervised Synthetic Code-Mixed Parallel Corpus
We submit a neural machine translation (NMT) system which is trained on the synthetic code-mixed (cm) English-Hinglish parallel corpus.
A Deep Ensemble Framework for Fake News Detection and Multi-Class Classification of Short Political Statements
Fake news, rumor, incorrect information, and misinformation detection are nowadays crucial issues as these might have serious consequences for our social fabrics.
Low Resource Chat Translation: A Benchmark for Hindi–English Language Pair
Chatbots are used in various sectors such as banking, healthcare, e-commerce, etc, and are mainly available in English.
Novelty Detection: A Perspective from Natural Language Processing
In this work, we build upon our earlier investigations for document-level novelty detection and present a comprehensive account of our efforts toward the problem.
Annotated Corpus of Tweets in English from Various Domains for Emotion Detection
We propose a Hierarchical Attention-based deep neural network for Emotion Detection (HAtED).
Only text? only image? or both? Predicting sentiment of internet memes
We propose a multi-modal framework for meme sentiment classification by utilizing textual and visual features of the meme.
A Multi-task Model for Multilingual Trigger Detection and Classification
In this paper we present a deep multi-task learning framework for multilingual event and argument trigger detection and classification.
Multi-linguality helps: Event-Argument Extraction for Disaster Domain in Cross-lingual and Multi-lingual setting
Extracting related arguments like Time, Place, Casualties, etc., provides a complete picture of the disaster event.
Team AINLPML @ MuP in SDP 2021: Scientific Document Summarization by End-to-End Extractive and Abstractive Approach
This paper introduces the proposed summarization system of the AINLPML team for the First Shared Task on Multi-Perspective Scientific Document Summarization at SDP 2022.
Universal Adversarial Framework to Improve Adversarial Robustness for Diabetic Retinopathy Detection
Diabetic Retinopathy (DR) is a prevalent illness associated with Diabetes which, if left untreated, can result in irreversible blindness.
When Reviewers Lock Horn: Finding Disagreement in Scientific Peer Reviews
To the best of our knowledge, we make the first attempt to identify disagreements among peer reviewers automatically.
Elevating Code-mixed Text Handling through Auditory Information of Words
With the growing popularity of code-mixed data, there is an increasing need for better handling of this type of data, which poses a number of challenges, such as dealing with spelling variations, multiple languages, different scripts, and a lack of resources.
INA: An Integrative Approach for Enhancing Negotiation Strategies with Reward-Based Dialogue System
We employ a set of novel rewards, specifically tailored for the negotiation task to train our Negotiation Agent, termed as the Integrative Negotiation Agent (INA).
Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific Rewards
Translating questions using Neural Machine Translation (NMT) poses more challenges, especially in noisy environments, where the grammatical correctness of the questions is not monitored.
Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes
In this paper, we present the overview of the Memotion 3 shared task, as part of the DeFactify 2 workshop at AAAI-23.
Impact of Visual Context on Noisy Multimodal NMT: An Empirical Study for English to Indian Languages
Interestingly, the effect of visual context varies with source text noise: no visual context works best for non-noisy translations, cropped image features are optimal for low noise, and full image features work better in high-noise scenarios.
A Case Study on Context Encoding in Multi-Encoder based Document-Level Neural Machine Translation
In this paper, we further explore this idea by evaluating with context-aware pronoun translation test set by training multi-encoder models trained on three different context settings viz, previous two sentences, random two sentences, and a mix of both as context.
Findings of Factify 2: Multimodal Fake News Detection
With social media usage growing exponentially in the past few years, fake news has also become extremely prevalent.
Multilingual Neural Machine Translation System for Indic to Indic Languages
To achieve this, English- Indic (EN-IL) models are also developed, with and without the usage of related languages.
A Unified Framework for Slot based Response Generation in a Multimodal Dialogue System
Lastly, a decoder generates the corresponding response for the given dialogue context and the extracted slot values.
Factify 2: A Multimodal Fake News and Satire News Dataset
In this paper, we provide a multi-modal fact-checking dataset called FACTIFY 2, improving Factify 1 by using new data sources and adding satire articles.
Memotion 3: Dataset on Sentiment and Emotion Analysis of Codemixed Hindi-English Memes
Memes are the new-age conveyance mechanism for humor on social media sites.
CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation
The development of conversational agents to interact with patients and deliver clinical advice has attracted the interest of many researchers, particularly in light of the COVID-19 pandemic.
DialoGen: Generalized Long-Range Context Representation for Dialogue Systems
In this work, we propose DialoGen, a novel encoder-decoder based framework for dialogue generation with a generalized context representation that can look beyond the last-$k$ utterances.
Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain
We construct a Knowledge Graph from Aircraft Accident reports and contribute this resource to the community of researchers.
Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation
In this paper, we present a novel open-domain dialogue generation model which effectively utilizes the large-scale commonsense and named entity based knowledge in addition to the unstructured topic-specific knowledge associated with each utterance.
EmoInHindi: A Multi-label Emotion and Intensity Annotated Dataset in Hindi for Emotion Recognition in Dialogues
The long-standing goal of Artificial Intelligence (AI) has been to create human-like conversational systems.
Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes
The World Health Organization (WHO) has emphasized the importance of significantly accelerating suicide prevention efforts to fulfill the United Nations' Sustainable Development Goal (SDG) objective of 2030.
COVIDRead: A Large-scale Question Answering Dataset on COVID-19
This dataset creates a new avenue of carrying out research on COVID-19 by providing a benchmark dataset and a baseline model.
Towards Developing a Multilingual and Code-Mixed Visual Question Answering System by Knowledge Distillation
We also create the large-scale multilingual and code-mixed VQA dataset in eleven different language setups considering the multiple Indian and European languages.
M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for humor recognition in conversations.
IITP at WAT 2021: System description for English-Hindi Multimodal Translation Task
Neural Machine Translation (NMT) is a predominant machine translation technology nowadays because of its end-to-end trainable flexibility.
IITP at AILA 2019: System Report for Artificial Intelligence for Legal Assistance Shared Task
The outcomes of this track would be helpful for the automation of the working process of the Indian Judiciary System.
IITP@COLIEE 2019: Legal Information Retrieval using BM25 and BERT
We make use of different Information Retrieval(IR) and deep learning based approaches to tackle these problems.
Modelling Context Emotions using Multi-task Learning for Emotion Controlled Dialog Generation
We employ multi-task learning to predict the emotion label and to generate a viable response for a given utterance using a common encoder with multiple decoders.
Leveraging Multi-domain, Heterogeneous Data using Deep Multitask Learning for Hate Speech Detection
With the exponential rise in user-generated web content on social media, the proliferation of abusive languages towards an individual or a group across the different sections of the internet is also rapidly increasing.
Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy
In this paper, we propose a hybrid technique for semantic question matching.
EmotionGIF-IITP-AINLPML: Ensemble-based Automated Deep Neural System for predicting category(ies) of a GIF response
In this paper, we describe the systems submitted by our IITP-AINLPML team in the shared task of SocialNLP 2020, EmotionGIF 2020, on predicting the category(ies) of a GIF response for a given unlabelled tweet.
MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations
Multi-label emotion detection in conversations is a significant task that provides the ability to the system to understand the various emotions of the users interacting.
All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes
The main motivation of iTRM is to learn the relationship between the tasks to realize how they help each other.
Unsupervised Aspect-Level Sentiment Controllable Style Transfer
Unsupervised style transfer in text has previously been explored through the sentiment transfer task.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
A Unified Framework for Multilingual and Code-Mixed Visual Question Answering
We propose a robust tech- nique capable of handling the multilingual and code-mixed question to provide the answer against the visual information (image).
IITP-AINLPML at SemEval-2020 Task 12: Offensive Tweet Identification and Target Categorization in a Multitask Environment
In this paper, we describe the participation of IITP-AINLPML team in the SemEval-2020 SharedTask 12 on Offensive Language Identification and Target Categorization in English Twitter data.
Hostility Detection Dataset in Hindi
In this paper, we present a novel hostility detection dataset in Hindi language.
Fighting an Infodemic: COVID-19 Fake News Dataset
This is further exacerbated at the time of a pandemic.
MultiDM-GCN: Aspect-guided Response Generation in Multi-domain Multi-modal Dialogue System using Graph Convolutional Network
In this work, we present a multi-modal conversational framework for a task-oriented dialogue setup that generates the responses following the different aspects of a product or service to cater to the user{'}s needs.
A Semi-supervised Approach to Generate the Code-Mixed Text using Pre-trained Encoder and Transfer Learning
Code-mixing, the interleaving of two or more languages within a sentence or discourse is ubiquitous in multilingual societies.
Hierarchical Deep Multi-modal Network for Medical Visual Question Answering
To address this issue, we propose a hierarchical deep multi-modal network that analyzes and classifies end-user questions/queries and then incorporates a query-specific approach for answer prediction.
Assessing the Severity of Health States based on Social Media Posts
The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
Relation Extraction from Biomedical and Clinical Text: Unified Multitask Learning Framework
Towards this, we model the relation extraction problem in multi-task learning (MTL) framework and introduce for the first time the concept of structured self-attentive network complemented with the adversarial learning approach for the prediction of relationships from the biomedical and clinical text.
Tweet to News Conversion: An Investigation into Unsupervised Controllable Text Generation
The second system stitches together the outputs from the first system to form a coherent news paragraph.
Exploiting stance hierarchies for cost-sensitive stance detection of Web documents
In this context, stance detection aims at identifying the position (stance) of a document towards a claim.
Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis
In this paper, we hypothesize that sarcasm is closely related to sentiment and emotion, and thereby propose a multi-task deep learning framework to solve all these three problems simultaneously in a multi-modal conversational scenario.
Investigating Deep Learning Approaches for Hate Speech Detection in Social Media
Moreover, the varieties in user-generated data and the presence of various forms of hate speech makes it very challenging to identify the degree and intention of the message.
A Deep Learning Approach for Automatic Detection of Fake News
We evaluate our techniques on the two recently released datasets, namely FakeNews AMT and Celebrity for fake news detection.
Multi-domain Tweet Corpora for Sentiment Analysis: Resource Creation and Evaluation
Due to the phenomenal growth of online content in recent time, sentiment analysis has attracted attention of the researchers and developers.
ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension
We present ScholarlyRead, span-of-word-based scholarly articles{'} Reading Comprehension (RC) dataset with approximately 10K manually checked passage-question-answer instances.
A Platform for Event Extraction in Hindi
In this paper, we present an Event Extraction framework for Hindi language by creating an annotated resource for benchmarking, and then developing deep learning based models to set as the baselines.
CEASE, a Corpus of Emotion Annotated Suicide notes in English
In this paper, we create a fine-grained emotion annotated corpus (CEASE) of suicide notes in English and develop various deep learning models to perform emotion detection on the curated dataset.
Incorporating Politeness across Languages in Customer Care Responses: Towards building a Multi-lingual Empathetic Dialogue Agent
Our system is competent in generating responses in different languages (here, English and Hindi) depending on the customer{'}s preference and also is able to converse with humans in an empathetic manner to ensure customer satisfaction and retention.
Reinforced Multi-task Approach for Multi-hop Question Generation
Question generation (QG) attempts to solve the inverse of question answering (QA) problem by generating a natural language question given a document and an answer.
Related Tasks can Share! A Multi-task Framework for Affective language
Expressing the polarity of sentiment as 'positive' and 'negative' usually have limited scope compared with the intensity/degree of polarity.
A Deep Neural Framework for Contextual Affect Detection
A short and simple text carrying no emotion can represent some strong emotions when reading along with its context, i. e., the same sentence can express extreme anger as well as happiness depending on its context.
Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis
Our network also boosts the performance of emotion analysis by 5 F-score points on Stance Sentiment Emotion Corpus.
Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis
In this paper, we introduce a recurrent neural network based approach for the multi-modal sentiment and emotion analysis.
Sentiment-Aware Recommendation System for Healthcare using Social Media
Over the last decade, health communities (known as forums) have evolved into platforms where more and more users share their medical experiences, thereby seeking guidance and interacting with people of the community.
Parallel Corpus Filtering Based on Fuzzy String Matching
Based on the scores, we sub-sample two sets (having 1 million and 5 millions English tokens) of parallel sentences from each parallel corpus, and train SMT systems for development purpose only.
IITP-MT System for Gujarati-English News Translation Task at WMT 2019
We describe our submission to WMT 2019 News translation shared task for Gujarati-English language pair.
DeepSentiPeer: Harnessing Sentiment in Review Texts to Recommend Peer Review Decisions
However, the peer review texts, which contains rich sentiment information of the reviewer, reflecting his/her overall attitude towards the research in the paper, could be a valuable entity to predict the acceptance or rejection of the manuscript under consideration.
A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining
The mining of adverse drug reaction (ADR) has a crucial role in the pharmacovigilance.
Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System
Multimodal dialogue systems have opened new frontiers in the traditional goal-oriented dialogue systems.
IITP at MEDIQA 2019: Systems Report for Natural Language Inference, Question Entailment and Question Answering
We submitted five system results in each of the NLI and RQE tasks, and four system results for the QA task.
Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network
We use real interactions on Twitter between customer care professionals and aggrieved customers to create a large conversational dataset having both forms of agent responses: {`}generic{'} and {`}courteous{'}.
NLP at SemEval-2019 Task 6: Detecting Offensive language using Neural Networks
The three best models that performed best on individual sub tasks are stacking of CNN-Bi-LSTM with Attention, BiLSTM with POS information added with word features and Bi-LSTM for third task.
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis
In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both.
Language-Agnostic Model for Aspect-Based Sentiment Analysis
In this paper, we propose a language-agnostic deep neural network architecture for aspect-based sentiment analysis.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Relation extraction between the clinical entities based on the shortest dependency path based LSTM
In this paper, we present an efficient relation extraction system based on the shortest dependency path (SDP) generated from the dependency parsed tree of the sentence.
A Deep Ensemble Framework for Fake News Detection and Classification
Fake news, rumor, incorrect information, and misinformation detection are nowadays crucial issues as these might have serious consequences for our social fabrics.
Helping each Other: A Framework for Customer-to-Customer Suggestion Mining using a Semi-supervised Deep Neural Network
We evaluate the performance of our proposed model on a benchmark customer review dataset, comprising of the reviews of Hotel and Electronics domains.
Time-Aware and Corpus-Specific Entity Relatedness
Entity relatedness has emerged as an important feature in a plethora of applications such as information retrieval, entity recommendation and entity linking.
Uncovering Code-Mixed Challenges: A Framework for Linguistically Driven Question Generation and Neural Based Question Answering
In this paper, we propose a linguistically motivated technique for code-mixed question generation (CMQG) and a neural network based architecture for code-mixed question answering (CMQA).
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis
Sentiment analysis has immense implications in e-commerce through user feedback mining.
Contextual Inter-modal Attention for Multi-modal Sentiment Analysis
We evaluate our proposed approach on two multi-modal sentiment analysis benchmark datasets, viz.
Ranked #6 on
Multimodal Sentiment Analysis
on MOSI
A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction
In this paper, through multi-task ensemble framework we address three problems of emotion and sentiment analysis i. e. "emotion classification & intensity", "valence, arousal & dominance for emotion" and "valence & arousal} for sentiment".
An Ensemble Approach for Aggression Identification in English and Hindi Text
This paper describes our system submitted in the shared task at COLING 2018 TRAC-1: Aggression Identification.
Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty Detection
The proposed method outperforms the existing state-of-the-art on a document-level novelty detection dataset by a margin of ∼5{\%} in terms of accuracy.
Leveraging Medical Sentiment to Understand Patients Health on Social Media
A large percentage of this population is actively engaged in health social networks to share health-related information.
Feature Assisted bi-directional LSTM Model for Protein-Protein Interaction Identification from Biomedical Texts
In this paper, we present a novel method based on deep bidirectional long short-term memory (B-LSTM) technique that exploits word sequences and dependency path related information to identify PPI information from text.
Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment
In this paper, we adopt a novel adversarial learning approach for our multi-task learning framework to learn the sentiment{'}s strengths expressed in a medical blog.
Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality
Efficient word representations play an important role in solving various problems related to Natural Language Processing (NLP), data mining, text mining etc.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Fine-Grained Temporal Orientation and its Relationship with Psycho-Demographic Correlates
In this paper, we propose a very first study to demonstrate the association between the sentiment view of the temporal orientation of the users and their different psycho-demographic attributes by analyzing their tweets.
TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem that has widespread NLP applications, such as extractive document summarization, tracking development of news events, predicting impact of scholarly articles, etc.
IITP at IJCNLP-2017 Task 4: Auto Analysis of Customer Feedback using CNN and GRU Network
Our empirical analysis shows that our models perform well in all the four languages on the setups of IJCNLP Shared Task on Customer Feedback Analysis.
Auto Analysis of Customer Feedback using CNN and GRU Network
Analyzing customer feedback is the best way to channelize the data into new marketing strategies that benefit entrepreneurs as well as customers.
A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis
In this paper, we propose a novel method for combining deep learning and classical feature based models using a Multi-Layer Perceptron (MLP) network for financial sentiment analysis.
IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features
This paper describes the system that we submitted as part of our participation in the shared task on Emotion Intensity (EmoInt-2017).
IITP at SemEval-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial Sentiment Analysis
In this paper we propose an ensemble based model which combines state of the art deep learning sentiment analysis algorithms like Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) along with feature based models to identify optimistic or pessimistic sentiments associated with companies and stocks in financial texts.
IITPB at SemEval-2017 Task 5: Sentiment Prediction in Financial Text
The other system was based on Support Vector Regression using word embeddings, lexicon features, and PMI scores as features.
IIT-UHH at SemEval-2017 Task 3: Exploring Multiple Features for Community Question Answering and Implicit Dialogue Identification
In this paper we present the system for Answer Selection and Ranking in Community Question Answering, which we build as part of our participation in SemEval-2017 Task 3.
IITP at SemEval-2017 Task 8 : A Supervised Approach for Rumour Evaluation
This paper describes our system participation in the SemEval-2017 Task 8 {`}RumourEval: Determining rumour veracity and support for rumours{'}.
Temporal Orientation of Tweets for Predicting Income of Users
Automatically estimating a user{'}s socio-economic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics.
Entity Extraction in Biomedical Corpora: An Approach to Evaluate Word Embedding Features with PSO based Feature Selection
The proposed system is evaluated on three benchmark biomedical datasets such as GENIA, GENETAG, and AiMed.
SMPOST: Parts of Speech Tagger for Code-Mixed Indic Social Media Text
For the task of PoS tagging on Code-Mixed Indian Social Media Text, we develop a supervised system based on Conditional Random Field classifier.
IITP English-Hindi Machine Translation System at WAT 2016
Experiments show the BLEU of 13. 71 on the benchmark test data.
A Hybrid Deep Learning Architecture for Sentiment Analysis
The sentiment augmented optimized vector obtained at the end is used for the training of SVM for sentiment classification.
Semi-supervised Clustering of Medical Text
Semi-supervised clustering is an attractive alternative for traditional (unsupervised) clustering in targeted applications.
Deep Learning Architecture for Patient Data De-identification in Clinical Records
However, medical records enclose patient Private Health Information (PHI) which can reveal the identities of the patients.
Building Tempo-HindiWordNet: A resource for effective temporal information access in Hindi
In this paper, we put forward a strategy that supplements Hindi WordNet entries with information on the temporality of its word senses.
Aspect based Sentiment Analysis in Hindi: Resource Creation and Evaluation
Due to the phenomenal growth of online product reviews, sentiment analysis (SA) has gained huge attention, for example, by online service providers.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
CRDT: Correlation Ratio Based Decision Tree Model for Healthcare Data Mining
In this paper, we have tried to focus on this characteristics of the datasets while analysing the performance of our proposed approach which is a variant of Decision Tree model and uses the concept of Correlation Ratio(CR).
