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Found 92 papers, 11 papers with code
Evaluating Biomedical Word Embeddings for Vocabulary Alignment at Scale in the UMLS Metathesaurus Using Siamese Networks
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.
Visual Hallucination: Definition, Quantification, and Prescriptive Remediations
The troubling rise of hallucination presents perhaps the most significant impediment to the advancement of responsible AI.
Grounding from an AI and Cognitive Science Lens
Grounding is a challenging problem, requiring a formal definition and different levels of abstraction.
On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)
Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity.
Neurosymbolic Value-Inspired AI (Why, What, and How)
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.
GEAR-Up: Generative AI and External Knowledge-based Retrieval Upgrading Scholarly Article Searches for Systematic Reviews
Systematic reviews (SRs) - the librarian-assisted literature survey of scholarly articles takes time and requires significant human resources.
RDR: the Recap, Deliberate, and Respond Method for Enhanced Language Understanding
Natural language understanding (NLU) using neural network pipelines often requires additional context that is not solely present in the input data.
Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety
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.
SEPSIS: I Can Catch Your Lies -- A New Paradigm for Deception Detection
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.
L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models
Fine-tuning pre-trained foundational language models (FLM) for specific tasks is often impractical, especially for resource-constrained devices.
On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models
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.
A Comprehensive Survey on Rare Event Prediction
This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, data processing, algorithmic approaches, and evaluation approaches.
Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness
As Large Language Models (LLMs) have advanced, they have brought forth new challenges, with one of the prominent issues being LLM hallucination.
A Survey of Hallucination in Large Foundation Models
Hallucination in a foundation model (FM) refers to the generation of content that strays from factual reality or includes fabricated information.
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.
Hi Model, generating 'nice' instead of 'good' is not as bad as generating 'rice'! Towards Context and Semantic Infused Dialogue Generation Loss Function and Evaluation Metric
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'.
RESTORE: Graph Embedding Assessment Through Reconstruction
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.
Why Do We Need Neuro-symbolic AI to Model Pragmatic Analogies?
A hallmark of intelligence is the ability to use a familiar domain to make inferences about a less familiar domain, known as analogical reasoning.
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.
IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations
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.
Knowledge-Infused Self Attention Transformers
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.
Process Knowledge-infused Learning for Clinician-friendly Explanations
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
Cook-Gen: Robust Generative Modeling of Cooking Actions from Recipes
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.).
ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance
LMs augmented with ProKnow guided method generated 89% safer questions in the depression and anxiety domain.
IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images
We also evaluate pre-trained IMAGINATOR JEs on three downstream tasks: (i) image captioning, (ii) Image2Tweet, and (iii) text-based image retrieval.
Knowledge Graph Guided Semantic Evaluation of Language Models For User Trust
However, they do not ascribe object and concept-level meaning and semantics to the learned stochastic patterns such as those described in knowledge graphs.
ANALOGICAL -- A Novel Benchmark for Long Text Analogy Evaluation in Large Language Models
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.
FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering
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.
Neurosymbolic AI - Why, What, and How
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.
"Can We Detect Substance Use Disorder?": Knowledge and Time Aware Classification on Social Media from Darkweb
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?
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.
Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case
After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important.
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.
KSAT: Knowledge-infused Self Attention Transformer -- Integrating Multiple Domain-Specific Contexts
Domain-specific language understanding requires integrating multiple pieces of relevant contextual information.
Can Language Models Capture Graph Semantics? From Graphs to Language Model and Vice-Versa
Knowledge Graphs are a great resource to capture semantic knowledge in terms of entities and relationships between the entities.
Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety
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.
MMTM: Multi-Tasking Multi-Decoder Transformer for Math Word Problems
Recently, quite a few novel neural architectures were derived to solve math word problems by predicting expression trees.
Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts
We demonstrate the challenge of using existing datasets to train a DLM for generating FQs that adhere to clinical process knowledge.
Exo-SIR: An Epidemiological Model to Analyze the Impact of Exogenous Spread of Infection
In this paper, we introduce the Exo-SIR model, an extension of the popular SIR model and a few variants of the model.
Process Knowledge-infused Learning for Suicidality Assessment on Social Media
Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world.
Knowledge-based Entity Prediction for Improved Machine Perception in Autonomous Systems
Knowledge-based entity prediction (KEP) is a novel task that aims to improve machine perception in autonomous systems.
A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework
We collect a corpus of $150k$ items (posts and comments) annotated using the subreddit labels and transfer learning approaches.
CausalKG: Causal Knowledge Graph Explainability using interventional and counterfactual reasoning
AI algorithms use a representation based on knowledge graphs (KG) to represent the concepts of time, space, and facts.
Towards Tractable Mathematical Reasoning: Challenges, Strategies, and Opportunities for Solving Math Word Problems
Mathematical reasoning would be one of the next frontiers for artificial intelligence to make significant progress.
Evaluating Biomedical BERT Models for Vocabulary Alignment at Scale in the UMLS Metathesaurus
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.
Knowledge-intensive Language Understanding for Explainable AI
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
Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits
Contextual Bandits find important use cases in various real-life scenarios such as online advertising, recommendation systems, healthcare, etc.
"Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit
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).
KI-BERT: Infusing Knowledge Context for Better Language and Domain Understanding
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.
Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS
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).
eDarkTrends: Harnessing Social Media Trends in Substance use disorders for Opioid Listings on Cryptomarket
However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means.
The Duality of Data and Knowledge Across the Three Waves of AI
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.
Knowledge Infused Policy Gradients for Adaptive Pandemic Control
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.
"Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision
With strong marketing advocacy of the benefits of cannabis use for improved mental health, cannabis legalization is a priority among legislators.
Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework
Existing studies on using social media for deriving mental health status of users focus on the depression detection task.
Medical Knowledge-enriched Textual Entailment Framework
One of the cardinal tasks in achieving robust medical question answering systems is textual entailment.
Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?
The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively.
"When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware Attention Framework for Relationship Extraction
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.
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.
Knowledge Graph semantic enhancement of input data for improving AI
Intelligent systems designed using machine learning algorithms require a large number of labeled data.
Knowledge Graphs and Knowledge Networks: The Story in Brief
Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities.
An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice
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.
Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning
Learning the underlying patterns in data goes beyond instance-based generalization to external knowledge represented in structured graphs or networks.
Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate
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.
Multimodal Emotion Classification
Our experiments demonstrate that the three modalities (text, emoji and images) encode different information to express emotion and therefore can complement each other.
Fusing Visual, Textual and Connectivity Clues for Studying Mental Health
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.
Analyzing and learning the language for different types of harassment
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.
Principles for Developing a Knowledge Graph of InterlinkedEvents from News Headlines on Twitter
The ever-growing datasets published on Linked Open Data mainly contain encyclopedic information.
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 Practical Incremental Learning Framework For Sparse Entity Extraction
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.
Predictive Analysis on Twitter: Techniques and Applications
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
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.
Concept2vec: Metrics for Evaluating Quality of Embeddings for Ontological Concepts
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.
A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research
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.
How will the Internet of Things enable Augmented Personalized Health?
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.
Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media
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.
On the Challenges of Sentiment Analysis for Dynamic Events
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.
Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models
Extracting location names from informal and unstructured social media data requires the identification of referent boundaries and partitioning compound names.
Implicit Entity Linking in Tweets
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.
A Semantics-Based Measure of Emoji Similarity
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.
EmojiNet: An Open Service and API for Emoji Sense Discovery
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.
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
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.
Logical Inferences with Contexts of RDF Triples
This formal semantics also allows us to derive a new set of entailment rules that could entail new contextual triples about triples.
Finding Street Gang Members on Twitter
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.
Word Embeddings to Enhance Twitter Gang Member Profile Identification
Gang affiliates have joined the masses who use social media to share thoughts and actions publicly.
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence.
EmojiNet: Building a Machine Readable Sense Inventory for Emoji
It is automatically constructed by integrating multiple emoji resources with BabelNet, which is the most comprehensive multilingual sense inventory available to date.
Semantic, Cognitive, and Perceptual Computing: Advances toward Computing for Human Experience
Toward this goal, we discuss computing paradigms of semantic computing, cognitive computing, and an emerging aspect of computing, which we call perceptual computing.
On Reasoning with RDF Statements about Statements using Singleton Property Triples
If the singleton property triples describe a data triple, then how can a reasoner infer this data triple from the singleton property triples?
