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Found 32 papers, 9 papers with code
GupShup: Summarizing Open-Domain Code-Switched Conversations
Code-switching is the communication phenomenon where the speakers switch between different languages during a conversation.
A Preliminary Exploration of GANs for Keyphrase Generation
We observed that our model achieves state-of-the-art performance in the generation of abstractive keyphrases and is comparable to the best performing extractive techniques.
LDKP: A Dataset for Identifying Keyphrases from Long Scientific Documents
Identifying keyphrases (KPs) from text documents is a fundamental task in natural language processing and information retrieval.
On the Evaluation of Answer-Agnostic Paragraph-level Multi-Question Generation
Second, we compare different strategies to utilize a pre-trained seq2seq model to generate and select a set of questions related to a given paragraph.
Learning Rich Representation of Keyphrases from Text
In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (upto 8. 16 points in F1) over SOTA, when the LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction.
On the Use of Context for Predicting Citation Worthiness of Sentences in Scholarly Articles
In this paper, we study the importance of context in predicting the citation worthiness of sentences in scholarly articles.
GupShup: An Annotated Corpus for Abstractive Summarization of Open-Domain Code-Switched Conversations
Towards this objective, we introduce abstractive summarization of Hindi-English code-switched conversations and develop the first code-switched conversation summarization dataset - GupShup, which contains over 6, 831 conversations in Hindi-English and their corresponding human-annotated summaries in English and Hindi-English.
Get It Scored Using AutoSAS -- An Automated System for Scoring Short Answers
In this paper, we present a fast, scalable, and accurate approach towards automated Short Answer Scoring (SAS).
Two-Step Classification using Recasted Data for Low Resource Settings
We further improve the performance by using a joint-objective for classification and textual entailment.
MIDAS at SemEval-2020 Task 10: Emphasis Selection using Label Distribution Learning and Contextual Embeddings
This paper presents our submission to the SemEval 2020 - Task 10 on emphasis selection in written text.
Semi-Supervised Iterative Approach for Domain-Specific Complaint Detection in Social Media
These indicators are then used to expand the dataset.
An Annotated Dataset of Discourse Modes in Hindi Stories
In this paper, we present a new corpus consisting of sentences from Hindi short stories annotated for five different discourse modes argumentative, narrative, descriptive, dialogic and informative.
An Iterative Approach for Identifying Complaint Based Tweets in Social Media Platforms
Twitter is a social media platform where users express opinions over a variety of issues.
#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement
In this paper, we present a dataset containing 9, 973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts.
Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings
In this paper, we formulate keyphrase extraction from scholarly articles as a sequence labeling task solved using a BiLSTM-CRF, where the words in the input text are represented using deep contextualized embeddings.
BHAAV- A Text Corpus for Emotion Analysis from Hindi Stories
In this paper, we introduce the first and largest Hindi text corpus, named BHAAV, which means emotions in Hindi, for analyzing emotions that a writer expresses through his characters in a story, as perceived by a narrator/reader.
Keyphrase Generation for Scientific Articles using GANs
In this paper, we present a keyphrase generation approach using conditional Generative Adversarial Networks (GAN).
\#YouToo? Detection of Personal Recollections of Sexual Harassment on Social Media
The availability of large-scale online social data, coupled with computational methods can help us answer fundamental questions relat- ing to our social lives, particularly our health and well-being.
SNAP-BATNET: Cascading Author Profiling and Social Network Graphs for Suicide Ideation Detection on Social Media
Suicide is a leading cause of death among youth and the use of social media to detect suicidal ideation is an active line of research.
Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment
The {\#}MeToo movement is an ongoing prevalent phenomenon on social media aiming to demonstrate the frequency and widespread of sexual harassment by providing a platform to speak narrate personal experiences of such harassment.
MobiVSR: A Visual Speech Recognition Solution for Mobile Devices
Visual speech recognition (VSR) is the task of recognizing spoken language from video input only, without any audio.
Identifying Offensive Posts and Targeted Offense from Twitter
In this paper we present our approach and the system description for Sub-task A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media.
Suggestion Mining from Online Reviews using ULMFiT
In this paper we present our approach and the system description for Sub Task A of SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums.
Harnessing GANs for Zero-shot Learning of New Classes in Visual Speech Recognition
To solve this problem, we present a novel approach to zero-shot learning by generating new classes using Generative Adversarial Networks (GANs), and show how the addition of unseen class samples increases the accuracy of a VSR system by a significant margin of 27% and allows it to handle speaker-independent out-of-vocabulary phrases.
Kiki Kills: Identifying Dangerous Challenge Videos from Social Media
There has been upsurge in the number of people participating in challenges made popular through social media channels.
Did you take the pill? - Detecting Personal Intake of Medicine from Twitter
We believe that the developed classifier has direct uses in the areas of psychology, health informatics, pharmacovigilance and affective computing for tracking moods, emotions and sentiments of patients expressing intake of medicine in social media.
A Multimodal Approach to Predict Social Media Popularity
In this work, we propose a multimodal dataset consisiting of content, context, and social information for popularity prediction.
Theme-weighted Ranking of Keywords from Text Documents using Phrase Embeddings
Keyword extraction is a fundamental task in natural language processing that facilitates mapping of documents to a concise set of representative single and multi-word phrases.
Detecting Offensive Tweets in Hindi-English Code-Switched Language
The paper focuses on the classification of offensive tweets written in Hinglish language, which is a portmanteau of the Indic language Hindi with the Roman script.
Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings
Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases.
#phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter
Mining social media messages for health and drug related information has received significant interest in pharmacovigilance research.
InfyNLP at SMM4H Task 2: Stacked Ensemble of Shallow Convolutional Neural Networks for Identifying Personal Medication Intake from Twitter
This paper describes Infosys's participation in the "2nd Social Media Mining for Health Applications Shared Task at AMIA, 2017, Task 2".
