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Found 369 papers, 39 papers with code
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
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 Multimodal Corpus for Emotion Recognition in Sarcasm
Detecting the emotion behind a sarcastic expression is non-trivial yet an important task.
Ranked #1 on
Sarcasm Detection
on MUStARD++
Relation Extraction : A Survey
In this paper, we survey several important supervised, semi-supervised and unsupervised RE techniques.
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.
HiNER: A Large Hindi Named Entity Recognition Dataset
We use different language models to perform the sequence labelling task for NER and show the efficacy of our data by performing a comparative evaluation with models trained on another dataset available for the Hindi NER task.
Ranked #1 on
Named Entity Recognition (NER)
on HiNER-original
KITLM: Domain-Specific Knowledge InTegration into Language Models for Question Answering
Our research contributes to advancing the field of domain-specific language understanding and showcases the potential of knowledge infusion techniques in improving the performance of language models on question-answering.
Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays
To find out which traits work best for different types of essays, we conduct ablation tests for each of the essay traits.
Ranked #4 on
Automated Essay Scoring
on ASAP
"So You Think You're Funny?": Rating the Humour Quotient in Standup Comedy
We devise a novel scoring mechanism to annotate the training data with a humour quotient score using the audience's laughter.
“So You Think You’re Funny?”: Rating the Humour Quotient in Standup Comedy
The normalized duration (laughter duration divided by the clip duration) of laughter in each clip is used to compute this humour coefficient score on a five-point scale (0-4).
Global Object Proposals for Improving Multi-Sentence Video Descriptions
Recently, many works are proposed on the generation of multi-sentence video descriptions.
Ranked #2 on
Dense Video Captioning
on ActivityNet Captions
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 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.
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.
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.
Judicious Selection of Training Data in Assisting Language for Multilingual Neural NER
Multilingual learning for Neural Named Entity Recognition (NNER) involves jointly training a neural network for multiple languages.
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.
Yes, this is what I was looking for! Towards Multi-modal Medical Consultation Concern Summary Generation
Over the past few years, the use of the Internet for healthcare-related tasks has grown by leaps and bounds, posing a challenge in effectively managing and processing information to ensure its efficient utilization.
Divide and Conquer: An Ensemble Approach for Hostile Post Detection in Hindi
This paper present our system for Shared Task at Constraint2021 on "Hostile Post Detection in Hindi".
IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian Languages
To the best of our knowledge, IndicIRSuite is the first attempt at building large-scale Neural Information Retrieval resources for a large number of Indian languages, and we hope that it will help accelerate research in Neural IR for Indian Languages.
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.
Adversarial Training For Low-Resource Disfluency Correction
Disfluencies commonly occur in conversational speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning
Query-focused Summarization (QfS) deals with systems that generate summaries from document(s) based on a query.
Leveraging Domain Knowledge for Efficient Reward Modelling in RLHF: A Case-Study in E-Commerce Opinion Summarization
While this strategy has proven effective, the training methodology requires a lot of human preference annotation (usually in the order of tens of thousands) to train $\varphi$.
Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour
To demonstrate the efficacy of this multi-task learning based approach to automatic essay grading, we collect gaze behaviour for 48 essays across 4 essay sets, and learn gaze behaviour for the rest of the essays, numbering over 7000 essays.
Cognition-aware Cognate Detection
We collect gaze behaviour data for a small sample of cognates and show that extracted cognitive features help the task of cognate detection.
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages
We, then, evaluate the impact of our cognate detection mechanism on neural machine translation (NMT), as a downstream task.
Cross-Lingual Information Retrieval
Cross-Lingual Word Embeddings
+5
Challenge Dataset of Cognates and False Friend Pairs from Indian Languages
In this paper, we describe the creation of two cognate datasets for twelve Indian languages, namely Sanskrit, Hindi, Assamese, Oriya, Kannada, Gujarati, Tamil, Telugu, Punjabi, Bengali, Marathi, and Malayalam.
There is No Big Brother or Small Brother: Knowledge Infusion in Language Models for Link Prediction and Question Answering
In this context, we infuse knowledge in large and small language models and study their performance, and find the performance to be similar.
Ranked #1 on
Question Answering
on AviationQA
Experience and Evidence are the eyes of an excellent summarizer! Towards Knowledge Infused Multi-modal Clinical Conversation Summarization
We propose a knowledge-infused, multi-modal, multi-tasking medical domain identification and clinical conversation summary generation (MM-CliConSummation) framework.
Explain Thyself Bully: Sentiment Aided Cyberbullying Detection with Explanation
While plenty of research is going on to develop better models for cyberbullying detection in monolingual language, there is very little research on the code-mixed languages and explainability aspect of cyberbullying.
One Prompt To Rule Them All: LLMs for Opinion Summary Evaluation
Evaluation of opinion summaries using conventional reference-based metrics rarely provides a holistic evaluation and has been shown to have a relatively low correlation with human judgments.
Transformers are Expressive, But Are They Expressive Enough for Regression?
Expressivity of a neural network is the class of functions it can approximate.
The IIT Bombay English-Hindi Parallel Corpus
We present the IIT Bombay English-Hindi Parallel Corpus.
Phrase Pair Mappings for Hindi-English Statistical Machine Translation
In this paper, we present our work on the creation of lexical resources for the Machine Translation between English and Hindi.
Bilingual Words and Phrase Mappings for Marathi and Hindi SMT
Lack of proper linguistic resources is the major challenges faced by the Machine Translation system developments when dealing with the resource poor languages.
Morphology Generation for Statistical Machine Translation
We use this method with the phrase-based and factor-based experiments on two morphologically rich languages: Hindi and Marathi when translating from English.
Indowordnets help in Indian Language Machine Translation
Then we have augmented the training corpus with Indowordnet synset word entries of lexical database and further trained 110 models on top of the baseline system.
Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT
We investigate pivot-based translation between related languages in a low resource, phrase-based SMT setting.
Role of Morphology Injection in Statistical Machine Translation
It is very unlikely for a parallel corpus to contain all morphological forms of words.
"Having 2 hours to write a paper is fun!": Detecting Sarcasm in Numerical Portions of Text
We analyze the challenges of the problem, and present Rule-based, Machine Learning and Deep Learning approaches to detect sarcasm in numerical portions of text.
Automatic Identification of Sarcasm Target: An Introductory Approach
To compare our approach, we use two baselines: a na\"ive baseline and another baseline based on work in sentiment target identification.
Learning variable length units for SMT between related languages via Byte Pair Encoding
We explore the use of segments learnt using Byte Pair Encoding (referred to as BPE units) as basic units for statistical machine translation between related languages and compare it with orthographic syllables, which are currently the best performing basic units for this translation task.
Expect the unexpected: Harnessing Sentence Completion for Sarcasm Detection
However, since the exact word where such an incongruity occurs may not be known in advance, we present two approaches: an All-words approach (which consults sentence completion for every content word) and an Incongruous words-only approach (which consults sentence completion for the 50% most incongruous content words).
Comparison of SMT and RBMT; The Requirement of Hybridization for Marathi-Hindi MT
We present in this paper our work on comparison between Statistical Machine Translation (SMT) and Rule-based machine translation for translation from Marathi to Hindi.
Lexical Resources for Hindi Marathi MT
In this paper we describe some ways to utilize various lexical resources to improve the quality of statistical machine translation system.
A case study on English-Malayalam Machine Translation
In this paper we present our work on a case study on Statistical Machine Translation (SMT) and Rule based machine translation (RBMT) for translation from English to Malayalam and Malayalam to English.
Leveraging Cognitive Features for Sentiment Analysis
Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels.
Harnessing Cognitive Features for Sarcasm Detection
In this paper, we propose a novel mechanism for enriching the feature vector, for the task of sarcasm detection, with cognitive features extracted from eye-movement patterns of human readers.
`Who would have thought of that!': A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection
Designed on the basis of the intuition that sarcastic tweets are likely to have a mixture of words of both sentiments as against tweets with literal sentiment (either positive or negative), our hierarchical topic model discovers sarcasm-prevalent topics and topic-level sentiment.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Faster decoding for subword level Phrase-based SMT between related languages
The increase in length is also impacted by the specific choice of data format for representing the sentences as subwords.
Are Word Embedding-based Features Useful for Sarcasm Detection?
For example, this augmentation results in an improvement in F-score of around 4\% for three out of these four feature sets, and a minor degradation in case of the fourth, when Word2Vec embeddings are used.
A Computational Approach to Automatic Prediction of Drunk Texting
Alcohol abuse may lead to unsociable behavior such as crime, drunk driving, or privacy leaks.
Orthographic Syllable as basic unit for SMT between Related Languages
We explore the use of the orthographic syllable, a variable-length consonant-vowel sequence, as a basic unit of translation between related languages which use abugida or alphabetic scripts.
Automatic Sarcasm Detection: A Survey
Automatic sarcasm detection is the task of predicting sarcasm in text.
Sharing Network Parameters for Crosslingual Named Entity Recognition
In this paper, we propose a neural network based model which allows sharing the decoder as well as word and character level parameters between two languages thereby allowing a resource fortunate language to aid a resource deprived language.
Prepositional Attachment Disambiguation Using Bilingual Parsing and Alignments
In this paper, we attempt to solve the problem of Prepositional Phrase (PP) attachments in English.
Sentiment Analysis : A Literature Survey
We will discuss in details various approaches to perform a computational treatment of sentiments and opinions.
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.
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.
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".
Combining Graph-based Dependency Features with Convolutional Neural Network for Answer Triggering
Answer triggering is the task of selecting the best-suited answer for a given question from a set of candidate answers if exists.
Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour
Such subjective aspects are better handled using cognitive information.
Is your Statement Purposeless? Predicting Computer Science Graduation Admission Acceptance based on Statement Of Purpose
We present a quantitative, data-driven machine learning approach to mitigate the problem of unpredictability of Computer Science Graduate School Admissions.
Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages
To bridge this divergence, We propose to pre-order the assisting language sentence to match the word order of the source language and train the parent model.
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.
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.
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).
Leveraging Orthographic Similarity for Multilingual Neural Transliteration
We address the task of joint training of transliteration models for multiple language pairs (multilingual transliteration).
Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification
In this paper, we propose that words that do not change their polarity and significance represent the transferable (usable) information across domains for cross-domain sentiment classification.
Identification of Alias Links among Participants in Narratives
Identification of distinct and independent participants (entities of interest) in a narrative is an important task for many NLP applications.
Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network
We contend that manual extraction of features may not be the best way to tackle text subtleties that characteristically prevail in complex classification tasks like Sentiment Analysis and Sarcasm Detection, and that even the extraction and choice of features should be delegated to the learning system.
End-to-end Relation Extraction using Neural Networks and Markov Logic Networks
End-to-end relation extraction refers to identifying boundaries of entity mentions, entity types of these mentions and appropriate semantic relation for each pair of mentions.
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.
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.
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.
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{'}.
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.
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.
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.
Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings
Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a fine-grained sentiment analysis.
The Whole is Greater than the Sum of its Parts: Towards the Effectiveness of Voting Ensemble Classifiers for Complex Word Identification
In this paper, we present an effective system using voting ensemble classifiers to detect contextually complex words for non-native English speakers.
Meaningless yet meaningful: Morphology grounded subword-level NMT
We explore the use of two independent subsystems Byte Pair Encoding (BPE) and Morfessor as basic units for subword-level neural machine translation (NMT).
Thank ``Goodness''! A Way to Measure Style in Student Essays
Essays have two major components for scoring - content and style.
Adapting Pre-trained Word Embeddings For Use In Medical Coding
We add information from medical coding data, as well as the first level from the hierarchy of ICD-10 medical code set to different pre-trained word embeddings.
Towards Harnessing Memory Networks for Coreference Resolution
Coreference resolution task demands comprehending a discourse, especially for anaphoric mentions which require semantic information for resolving antecedents.
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).
Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation
In this paper, we empirically compare the two encoder-decoder neural machine translation architectures: convolutional sequence to sequence model (ConvS2S) and recurrent sequence to sequence model (RNNS2S) for English-Hindi language pair as part of IIT Bombay{'}s submission to WAT2017 shared task.
Hindi Shabdamitra: A Wordnet based E-Learning Tool for Language Learning and Teaching
In today{'}s technology driven digital era, education domain is undergoing a transformation from traditional approaches to more learner controlled and flexible methods of learning.
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.
IIT Bombay's English-Indonesian submission at WAT: Integrating Neural Language Models with SMT
The Neural Probabilistic Language Model (NPLM) gave relatively high BLEU points for Indonesian to English translation system while the Neural Network Joint Model (NNJM) performed better for English to Indonesian direction of translation system.
IITP English-Hindi Machine Translation System at WAT 2016
Experiments show the BLEU of 13. 71 on the benchmark test data.
Treat us like the sequences we are: Prepositional Paraphrasing of Noun Compounds using LSTM
It has been observed that uncovering the preposition is a significant step towards uncovering the predicate.
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.
Borrow a Little from your Rich Cousin: Using Embeddings and Polarities of English Words for Multilingual Sentiment Classification
In this paper, we provide a solution to multilingual sentiment classification using deep learning.
Towards Lower Bounds on Number of Dimensions for Word Embeddings
More specifically, we show that the number of pairwise equidistant words of the corpus vocabulary (as defined by some distance/similarity metric) gives a lower bound on the the number of dimensions , and going below this bound results in degradation of quality of learned word embeddings.
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.
