no code implementations • insights (ACL) 2022 • Goonmeet Bajaj, Vinh Nguyen, Thilini Wijesiriwardene, Hong Yung Yip, Vishesh Javangula, Amit Sheth, Srinivasan Parthasarathy, Olivier Bodenreider
Recent work uses a Siamese Network, initialized with BioWordVec embeddings (distributed word embeddings), for predicting synonymy among biomedical terms to automate a part of the UMLS (Unified Medical Language System) Metathesaurus construction process.
no code implementations • 3 May 2024 • Deepa Tilwani, Yash Saxena, Ali Mohammadi, Edward Raff, Amit Sheth, Srinivasan Parthasarathy, Manas Gaur
Automatic citation generation for sentences in a document or report is paramount for intelligence analysts, cybersecurity, news agencies, and education personnel.
no code implementations • 26 Mar 2024 • Anku Rani, Vipula Rawte, Harshad Sharma, Neeraj Anand, Krishnav Rajbangshi, Amit Sheth, Amitava Das
The troubling rise of hallucination presents perhaps the most significant impediment to the advancement of responsible AI.
no code implementations • 19 Feb 2024 • Goonmeet Bajaj, Srinivasan Parthasarathy, Valerie L. Shalin, Amit Sheth
Grounding is a challenging problem, requiring a formal definition and different levels of abstraction.
no code implementations • 4 Jan 2024 • Vishal Pallagani, Kaushik Roy, Bharath Muppasani, Francesco Fabiano, Andrea Loreggia, Keerthiram Murugesan, Biplav Srivastava, Francesca Rossi, Lior Horesh, Amit Sheth
Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity.
no code implementations • 15 Dec 2023 • Amit Sheth, Kaushik Roy
The rapid progression of Artificial Intelligence (AI) systems, facilitated by the advent of Large Language Models (LLMs), has resulted in their widespread application to provide human assistance across diverse industries.
no code implementations • 15 Dec 2023 • Kaushik Roy, Vedant Khandelwal, Harshul Surana, Valerie Vera, Amit Sheth, Heather Heckman
Systematic reviews (SRs) - the librarian-assisted literature survey of scholarly articles takes time and requires significant human resources.
no code implementations • 15 Dec 2023 • Yuxin Zi, Hariram Veeramani, Kaushik Roy, Amit Sheth
Natural language understanding (NLU) using neural network pipelines often requires additional context that is not solely present in the input data.
no code implementations • 5 Dec 2023 • Manas Gaur, Amit Sheth
We present the CREST framework that shows how Consistency, Reliability, user-level Explainability, and Safety are built on NeuroSymbolic methods that use data and knowledge to support requirements for critical applications such as health and well-being.
no code implementations • 1 Dec 2023 • Anku Rani, Dwip Dalal, Shreya Gautam, Pankaj Gupta, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
This research explores the problem of deception through the lens of psychology, employing a framework that categorizes deception into three forms: lies of omission, lies of commission, and lies of influence.
no code implementations • 23 Nov 2023 • Sumit Dalal, Deepa Tilwani, Kaushik Roy, Manas Gaur, Sarika Jain, Valerie Shalin, Amit Sheth
We develop such a system in the context of MH using clinical practice guidelines (CPG) for diagnosing depression, a mental health disorder of global concern.
no code implementations • 11 Nov 2023 • Aidin Shiri, Kaushik Roy, Amit Sheth, Manas Gaur
Fine-tuning pre-trained foundational language models (FLM) for specific tasks is often impractical, especially for resource-constrained devices.
1 code implementation • 11 Oct 2023 • Thilini Wijesiriwardene, Ruwan Wickramarachchi, Aishwarya Naresh Reganti, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
Through our analysis, we find that LLMs' ability to identify sentence analogies is positively correlated with their ability to encode syntactic and semantic structures of sentences.
no code implementations • 20 Sep 2023 • Vipula Rawte, Prachi Priya, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman, Amit Sheth, Amitava Das
As Large Language Models (LLMs) have advanced, they have brought forth new challenges, with one of the prominent issues being LLM hallucination.
no code implementations • 20 Sep 2023 • Chathurangi Shyalika, Ruwan Wickramarachchi, Amit Sheth
This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, data processing, algorithmic approaches, and evaluation approaches.
no code implementations • 12 Sep 2023 • Shreyash Mishra, S Suryavardan, Megha Chakraborty, Parth Patwa, Anku Rani, Aman Chadha, Aishwarya Reganti, Amitava Das, Amit Sheth, Manoj Chinnakotla, Asif Ekbal, Srijan Kumar
In this paper, we present the overview of the Memotion 3 shared task, as part of the DeFactify 2 workshop at AAAI-23.
1 code implementation • 12 Sep 2023 • Vipula Rawte, Amit Sheth, Amitava Das
Hallucination in a foundation model (FM) refers to the generation of content that strays from factual reality or includes fabricated information.
no code implementations • 11 Sep 2023 • Abhisek Tiwari, Muhammed Sinan, Kaushik Roy, Amit Sheth, Sriparna Saha, Pushpak Bhattacharyya
These lexical-based metrics have the following key limitations: (a) word-to-word matching without semantic consideration: It assigns the same credit for failure to generate 'nice' and 'rice' for 'good'.
no code implementations • 28 Aug 2023 • Hong Yung Yip, Chidaksh Ravuru, Neelabha Banerjee, Shashwat Jha, Amit Sheth, Aman Chadha, Amitava Das
We analyze their effectiveness in preserving the (a) topological structure of node-level graph reconstruction with an increasing number of hops and (b) semantic information on various word semantic and analogy tests.
no code implementations • 2 Aug 2023 • Thilini Wijesiriwardene, Amit Sheth, Valerie L. Shalin, Amitava Das
A hallmark of intelligence is the ability to use a familiar domain to make inferences about a less familiar domain, known as analogical reasoning.
no code implementations • 19 Jul 2023 • S Suryavardan, Shreyash Mishra, Megha Chakraborty, Parth Patwa, Anku Rani, Aman Chadha, Aishwarya Reganti, Amitava Das, Amit Sheth, Manoj Chinnakotla, Asif Ekbal, Srijan Kumar
With social media usage growing exponentially in the past few years, fake news has also become extremely prevalent.
no code implementations • 24 Jun 2023 • Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Manas Gaur, Amit Sheth
Crowdsourced and expert-curated knowledge graphs such as ConceptNet are designed to capture the meaning of words from a compact set of well-defined contexts.
no code implementations • 23 Jun 2023 • Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Amit Sheth
However, the ad-hoc nature of existing methods makes it difficult to properly analyze the effects of knowledge infusion on the many moving parts or components of a transformer.
no code implementations • 16 Jun 2023 • Kaushik Roy, Yuxin Zi, Manas Gaur, Jinendra Malekar, Qi Zhang, Vignesh Narayanan, Amit Sheth
In this study, we introduce Process Knowledge-infused Learning (PK-iL), a new learning paradigm that layers clinical process knowledge structures on language model outputs, enabling clinician-friendly explanations of the underlying language model predictions.
Explainable Artificial Intelligence (XAI) Language Modelling
1 code implementation • 1 Jun 2023 • Revathy Venkataramanan, Kaushik Roy, Kanak Raj, Renjith Prasad, Yuxin Zi, Vignesh Narayanan, Amit Sheth
In this study, we explore the use of generative AI methods to extend current food computation models, primarily involving the analysis of nutrition and ingredients, to also incorporate cooking actions (e. g., add salt, fry the meat, boil the vegetables, etc.).
no code implementations • 13 May 2023 • Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Kalyan, Amit Sheth
LMs augmented with ProKnow guided method generated 89% safer questions in the depression and anxiety domain.
no code implementations • 12 May 2023 • Varuna Krishna, S Suryavardan, Shreyash Mishra, Sathyanarayanan Ramamoorthy, Parth Patwa, Megha Chakraborty, Aman Chadha, Amitava Das, Amit Sheth
We also evaluate pre-trained IMAGINATOR JEs on three downstream tasks: (i) image captioning, (ii) Image2Tweet, and (iii) text-based image retrieval.
no code implementations • 8 May 2023 • Thilini Wijesiriwardene, Ruwan Wickramarachchi, Bimal G. Gajera, Shreeyash Mukul Gowaikar, Chandan Gupta, Aman Chadha, Aishwarya Naresh Reganti, Amit Sheth, Amitava Das
Over the past decade, analogies, in the form of word-level analogies, have played a significant role as an intrinsic measure of evaluating the quality of word embedding methods such as word2vec.
no code implementations • 8 May 2023 • Kaushik Roy, Tarun Garg, Vedant Palit, Yuxin Zi, Vignesh Narayanan, Amit Sheth
However, they do not ascribe object and concept-level meaning and semantics to the learned stochastic patterns such as those described in knowledge graphs.
no code implementations • 7 May 2023 • Anku Rani, S. M Towhidul Islam Tonmoy, Dwip Dalal, Shreya Gautam, Megha Chakraborty, Aman Chadha, Amit Sheth, Amitava Das
Finally, we report a baseline QA system to automatically locate those answers from evidence documents, which can serve as a baseline for future research in the field.
no code implementations • 1 May 2023 • Amit Sheth, Kaushik Roy, Manas Gaur
Humans interact with the environment using a combination of perception - transforming sensory inputs from their environment into symbols, and cognition - mapping symbols to knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning.
no code implementations • 20 Apr 2023 • Usha Lokala, Orchid Chetia Phukan, Triyasha Ghosh Dastidar, Francois Lamy, Raminta Daniulaityte, Amit Sheth
We use the Drug Abuse Ontology, state-of-the-art deep learning, and knowledge-aware BERT-based models to generate sentiment and emotion for the social media posts to understand users' perceptions on social media by investigating questions such as: which synthetic opioids people are optimistic, neutral, or negative about?
1 code implementation • 8 Apr 2023 • S Suryavardan, Shreyash Mishra, Parth Patwa, Megha Chakraborty, Anku Rani, Aishwarya Reganti, Aman Chadha, Amitava Das, Amit Sheth, Manoj Chinnakotla, Asif Ekbal, Srijan Kumar
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.
no code implementations • 31 Mar 2023 • Kaushik Roy, Vedant Khandelwal, Raxit Goswami, Nathan Dolbir, Jinendra Malekar, Amit Sheth
After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important.
1 code implementation • 17 Mar 2023 • Shreyash Mishra, S Suryavardan, Parth Patwa, Megha Chakraborty, Anku Rani, Aishwarya Reganti, Aman Chadha, Amitava Das, Amit Sheth, Manoj Chinnakotla, Asif Ekbal, Srijan Kumar
Memes are the new-age conveyance mechanism for humor on social media sites.
no code implementations • 9 Oct 2022 • Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Amit Sheth
Domain-specific language understanding requires integrating multiple pieces of relevant contextual information.
no code implementations • 18 Jun 2022 • Tarun Garg, Kaushik Roy, Amit Sheth
Knowledge Graphs are a great resource to capture semantic knowledge in terms of entities and relationships between the entities.
no code implementations • 9 Jun 2022 • Amit Sheth, Manas Gaur, Kaushik Roy, Revathy Venkataraman, Vedant Khandelwal
For such applications, in addition to data and domain knowledge, the AI systems need to have access to and use the Process Knowledge, an ordered set of steps that the AI system needs to use or adhere to.
no code implementations • 2 Jun 2022 • Keyur Faldu, Amit Sheth, Prashant Kikani, Darshan Patel
Recently, quite a few novel neural architectures were derived to solve math word problems by predicting expression trees.
1 code implementation • NAACL (CLPsych) 2022 • Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth
We demonstrate the challenge of using existing datasets to train a DLM for generating FQs that adhere to clinical process knowledge.
no code implementations • 3 May 2022 • Nirmal Kumar Sivaraman, Manas Gaur, Shivansh Baijal, Sakthi Balan Muthiah, Amit Sheth
In this paper, we introduce the Exo-SIR model, an extension of the popular SIR model and a few variants of the model.
no code implementations • 26 Apr 2022 • Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth
Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world.
no code implementations • 30 Mar 2022 • Ruwan Wickramarachchi, Cory Henson, Amit Sheth
Knowledge-based entity prediction (KEP) is a novel task that aims to improve machine perception in autonomous systems.
no code implementations • 22 Mar 2022 • Usha Lokala, Aseem Srivastava, Triyasha Ghosh Dastidar, Tanmoy Chakraborty, Md Shad Akthar, Maryam Panahiazar, Amit Sheth
We collect a corpus of $150k$ items (posts and comments) annotated using the subreddit labels and transfer learning approaches.
no code implementations • 6 Jan 2022 • Utkarshani Jaimini, Amit Sheth
AI algorithms use a representation based on knowledge graphs (KG) to represent the concepts of time, space, and facts.
no code implementations • 29 Oct 2021 • Keyur Faldu, Amit Sheth, Prashant Kikani, Manas Gaur, Aditi Avasthi
Mathematical reasoning would be one of the next frontiers for artificial intelligence to make significant progress.
no code implementations • 14 Sep 2021 • Goonmeet Bajaj, Vinh Nguyen, Thilini Wijesiriwardene, Hong Yung Yip, Vishesh Javangula, Srinivasan Parthasarathy, Amit Sheth, Olivier Bodenreider
Given the SOTA performance of these BERT models for other downstream tasks, our experiments yield surprisingly interesting results: (1) in both model architectures, the approaches employing these biomedical BERT-based models do not outperform the existing approaches using Siamese Network with BioWordVec embeddings for the UMLS synonymy prediction task, (2) the original BioBERT large model that has not been pre-trained with the UMLS outperforms the SapBERT models that have been pre-trained with the UMLS, and (3) using the Siamese Networks yields better performance for synonymy prediction when compared to using the biomedical BERT models.
no code implementations • 2 Aug 2021 • Amit Sheth, Manas Gaur, Kaushik Roy, Keyur Faldu
To understand and validate an AI system's outcomes (such as classification, recommendations, predictions), that lead to developing trust in the AI system, it is necessary to involve explicit domain knowledge that humans understand and use.
Decision Making Explainable Artificial Intelligence (XAI) +1
no code implementations • 25 Jun 2021 • Kaushik Roy, Qi Zhang, Manas Gaur, Amit Sheth
Contextual Bandits find important use cases in various real-life scenarios such as online advertising, recommendation systems, healthcare, etc.
no code implementations • 12 May 2021 • Manas Gaur, Kaushik Roy, Aditya Sharma, Biplav Srivastava, Amit Sheth
During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs).
no code implementations • 9 Apr 2021 • Keyur Faldu, Amit Sheth, Prashant Kikani, Hemang Akbari
We take BERT as a baseline model and implement the "Knowledge-Infused BERT" by infusing knowledge context from ConceptNet and WordNet, which significantly outperforms BERT and other recent knowledge-aware BERT variants like ERNIE, SenseBERT, and BERT_CS over eight different subtasks of GLUE benchmark.
no code implementations • 9 Apr 2021 • Manas Gaur, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonanthan Beich, Jyotishman Pathak, Amit Sheth
In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS).
no code implementations • 29 Mar 2021 • Usha Lokala, Francois Lamy, Triyasha Ghosh Dastidar, Kaushik Roy, Raminta Daniulaityte, Srinivasan Parthasarathy, Amit Sheth
However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means.
no code implementations • 24 Mar 2021 • Amit Sheth, Krishnaprasad Thirunarayan
We will draw a parallel with the role of knowledge and experience in human intelligence based on cognitive science, and discuss emerging neuro-symbolic or hybrid AI systems in which knowledge is the critical enabler for combining capabilities of the data-intensive statistical AI systems with those of symbolic AI systems, resulting in more capable AI systems that support more human-like intelligence.
no code implementations • 11 Feb 2021 • Kaushik Roy, Qi Zhang, Manas Gaur, Amit Sheth
To this end, we introduce a mathematical framework for KIPG methods that can (a) induce relevant feature counts over multi-relational features of the world, (b) handle latent non-homogeneous counts as hidden variables that are linear combinations of kernelized aggregates over the features, and (b) infuse knowledge as functional constraints in a principled manner.
no code implementations • 1 Feb 2021 • Kaushik Roy, Usha Lokala, Vedant Khandelwal, Amit Sheth
With strong marketing advocacy of the benefits of cannabis use for improved mental health, cannabis legalization is a priority among legislators.
no code implementations • COLING 2020 • Shweta Yadav, Jainish Chauhan, Joy Prakash Sain, Krishnaprasad Thirunarayan, Amit Sheth, Jeremiah Schumm
Existing studies on using social media for deriving mental health status of users focus on the depression detection task.
no code implementations • COLING 2020 • Shweta Yadav, Vishal Pallagani, Amit Sheth
One of the cardinal tasks in achieving robust medical question answering systems is textual entailment.
no code implementations • 16 Oct 2020 • Manas Gaur, Keyur Faldu, Amit Sheth
The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively.
no code implementations • 21 Sep 2020 • Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, Amit Sheth
In this interdisciplinary study, we demonstrate the value of incorporating domain-specific knowledge in the learning process to identify the relationships between cannabis use and depression.
no code implementations • 21 Sep 2020 • Shweta Yadav, Joy Prakash Sain, Amit Sheth, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya
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.
no code implementations • 10 May 2020 • Shreyansh Bhatt, Amit Sheth, Valerie Shalin, Jinjin Zhao
Intelligent systems designed using machine learning algorithms require a large number of labeled data.
no code implementations • 7 Mar 2020 • Amit Sheth, Swati Padhee, Amelie Gyrard
Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities.
no code implementations • 29 Feb 2020 • Ruwan Wickramarachchi, Cory Henson, Amit Sheth
With the expectation that neuro-symbolic fusion through KGEs will improve scene understanding, this research explores the generation and evaluation of KGEs for autonomous driving data.
no code implementations • 1 Dec 2019 • Ugur Kursuncu, Manas Gaur, Amit Sheth
Learning the underlying patterns in data goes beyond instance-based generalization to external knowledge represented in structured graphs or networks.
no code implementations • 18 Aug 2019 • Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, K. Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, Amit Sheth
Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming.
no code implementations • 13 Mar 2019 • Anurag Illendula, Amit Sheth
Our experiments demonstrate that the three modalities (text, emoji and images) encode different information to express emotion and therefore can complement each other.
no code implementations • 19 Feb 2019 • Amir Hossein Yazdavar, Mohammad Saeid Mahdavinejad, Goonmeet Bajaj, William Romine, Amirhassan Monadjemi, Krishnaprasad Thirunarayan, Amit Sheth, Jyotishman Pathak
With ubiquity of social media platforms, millions of people are sharing their online persona by expressing their thoughts, moods, emotions, feelings, and even their daily struggles with mental health issues voluntarily and publicly on social media.
no code implementations • 1 Nov 2018 • Mohammadreza Rezvan, Saeedeh Shekarpour, Faisal Alshargi, Krishnaprasad Thirunarayan, Valerie L. Shalin, Amit Sheth
In this paper, we introduce the notion of contextual type to harassment involving five categories: (i) sexual, (ii) racial, (iii) appearance-related, (iv) intellectual and (v) political.
no code implementations • 6 Aug 2018 • Saeedeh Shekarpour, Ankita Saxena, Krishnaprasad Thirunarayan, Valerie L. Shalin, Amit Sheth
The ever-growing datasets published on Linked Open Data mainly contain encyclopedic information.
no code implementations • 30 Jul 2018 • Shweta Yadav, Joy Sain, Amit Sheth, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya
A large percentage of this population is actively engaged in health social networks to share health-related information.
1 code implementation • COLING 2018 • Hussein S. Al-Olimat, Steven Gustafson, Jason Mackay, Krishnaprasad Thirunarayan, Amit Sheth
This work addresses challenges arising from extracting entities from textual data, including the high cost of data annotation, model accuracy, selecting appropriate evaluation criteria, and the overall quality of annotation.
1 code implementation • 6 Jun 2018 • Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, I. Budak Arpinar
Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide.
Social and Information Networks
no code implementations • NAACL 2018 • Shweta Yadav, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya, Amit Sheth
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.
1 code implementation • 12 Mar 2018 • Faisal Alshargi, Saeedeh Shekarpour, Tommaso Soru, Amit Sheth
This deficiency is further sensed with respect to embeddings generated for structured data because there are no concrete evaluation metrics measuring the quality of the encoded structure as well as semantic patterns in the embedding space.
no code implementations • 26 Feb 2018 • Mohammadreza Rezvan, Saeedeh Shekarpour, Lakshika Balasuriya, Krishnaprasad Thirunarayan, Valerie Shalin, Amit Sheth
In this paper, we publish first, a quality annotated corpus and second, an offensive words lexicon capturing different types type of harassment as (i) sexual harassment, (ii) racial harassment, (iii) appearance-related harassment, (iv) intellectual harassment, and (v) political harassment. We crawled data from Twitter using our offensive lexicon.
no code implementations • 31 Dec 2017 • Amit Sheth, Utkarshani Jaimini, Hong Yung Yip
It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual.
no code implementations • 16 Oct 2017 • Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth
With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter.
no code implementations • 6 Oct 2017 • Monireh Ebrahimi, Amir Hossein Yazdavar, Amit Sheth
With the proliferation of social media over the last decade, determining people's attitude with respect to a specific topic, document, interaction or events has fueled research interest in natural language processing and introduced a new channel called sentiment and emotion analysis.
1 code implementation • COLING 2018 • Hussein S. Al-Olimat, Krishnaprasad Thirunarayan, Valerie Shalin, Amit Sheth
Extracting location names from informal and unstructured social media data requires the identification of referent boundaries and partitioning compound names.
no code implementations • 26 Jul 2017 • Sujan Perera, Pablo N. Mendes, Adarsh Alex, Amit Sheth, Krishnaprasad Thirunarayan
We demonstrate how to use these models to perform implicit entity linking on a ground truth dataset with 397 tweets from two domains, namely, Movie and Book.
no code implementations • 14 Jul 2017 • Amit Sheth, Sujan Perera, Sanjaya Wijeratne, Krishnaprasad Thirunarayan
Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.
no code implementations • 14 Jul 2017 • Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran
This paper presents the release of EmojiNet, the largest machine-readable emoji sense inventory that links Unicode emoji representations to their English meanings extracted from the Web.
2 code implementations • 14 Jul 2017 • Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran
This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base.
no code implementations • 20 Jan 2017 • Vinh Nguyen, Amit Sheth
This formal semantics also allows us to derive a new set of entailment rules that could entail new contextual triples about triples.
no code implementations • 29 Oct 2016 • Lakshika Balasuriya, Sanjaya Wijeratne, Derek Doran, Amit Sheth
A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population.
no code implementations • 27 Oct 2016 • Sanjaya Wijeratne, Lakshika Balasuriya, Derek Doran, Amit Sheth
Gang affiliates have joined the masses who use social media to share thoughts and actions publicly.
no code implementations • 25 Oct 2016 • Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran
It is automatically constructed by integrating multiple emoji resources with BabelNet, which is the most comprehensive multilingual sense inventory available to date.
no code implementations • 25 Oct 2016 • Amit Sheth, Sujan Perera, Sanjaya Wijeratne
Machine Learning has been a big success story during the AI resurgence.
no code implementations • 20 Oct 2015 • Amit Sheth, Pramod Anantharam, Cory Henson
Toward this goal, we discuss computing paradigms of semantic computing, cognitive computing, and an emerging aspect of computing, which we call perceptual computing.
no code implementations • 15 Sep 2015 • Vinh Nguyen, Olivier Bodenreider, Krishnaprasad Thirunarayan, Gang Fu, Evan Bolton, Núria Queralt Rosinach, Laura I. Furlong, Michel Dumontier, Amit Sheth
If the singleton property triples describe a data triple, then how can a reasoner infer this data triple from the singleton property triples?