19 research outputs found
Finding Streams in Knowledge Graphs to Support Fact Checking
The volume and velocity of information that gets generated online limits
current journalistic practices to fact-check claims at the same rate.
Computational approaches for fact checking may be the key to help mitigate the
risks of massive misinformation spread. Such approaches can be designed to not
only be scalable and effective at assessing veracity of dubious claims, but
also to boost a human fact checker's productivity by surfacing relevant facts
and patterns to aid their analysis. To this end, we present a novel,
unsupervised network-flow based approach to determine the truthfulness of a
statement of fact expressed in the form of a (subject, predicate, object)
triple. We view a knowledge graph of background information about real-world
entities as a flow network, and knowledge as a fluid, abstract commodity. We
show that computational fact checking of such a triple then amounts to finding
a "knowledge stream" that emanates from the subject node and flows toward the
object node through paths connecting them. Evaluation on a range of real-world
and hand-crafted datasets of facts related to entertainment, business, sports,
geography and more reveals that this network-flow model can be very effective
in discerning true statements from false ones, outperforming existing
algorithms on many test cases. Moreover, the model is expressive in its ability
to automatically discover several useful path patterns and surface relevant
facts that may help a human fact checker corroborate or refute a claim.Comment: Extended version of the paper in proceedings of ICDM 201
Effective Proxy for Human Labeling: Ensemble Disagreement Scores in Large Language Models for Industrial NLP
Large language models (LLMs) have demonstrated significant capability to
generalize across a large number of NLP tasks. For industry applications, it is
imperative to assess the performance of the LLM on unlabeled production data
from time to time to validate for a real-world setting. Human labeling to
assess model error requires considerable expense and time delay. Here we
demonstrate that ensemble disagreement scores work well as a proxy for human
labeling for language models in zero-shot, few-shot, and fine-tuned settings,
per our evaluation on keyphrase extraction (KPE) task. We measure fidelity of
the results by comparing to true error measured from human labeled ground
truth. We contrast with the alternative of using another LLM as a source of
machine labels, or silver labels. Results across various languages and domains
show disagreement scores provide a better estimation of model performance with
mean average error (MAE) as low as 0.4% and on average 13.8% better than using
silver labels
Computational fact checking from knowledge networks
Traditional fact checking by expert journalists cannot keep up with the
enormous volume of information that is now generated online. Computational fact
checking may significantly enhance our ability to evaluate the veracity of
dubious information. Here we show that the complexities of human fact checking
can be approximated quite well by finding the shortest path between concept
nodes under properly defined semantic proximity metrics on knowledge graphs.
Framed as a network problem this approach is feasible with efficient
computational techniques. We evaluate this approach by examining tens of
thousands of claims related to history, entertainment, geography, and
biographical information using a public knowledge graph extracted from
Wikipedia. Statements independently known to be true consistently receive
higher support via our method than do false ones. These findings represent a
significant step toward scalable computational fact-checking methods that may
one day mitigate the spread of harmful misinformation
Ayurvedic management of allergic conjunctivitis in 30 years old patient: A case study
Allergic Conjunctivitis is a common clinical condition faced by practitioners. About 5-22% percent of the world population suffer from some sort of allergic ocular disease. In Ayurveda it can be correlated with Vataja Abhishyanda on the basis of symptoms like Toda (Pricking pain), Sangharsha (foreign body sensation), Achchasruta (watering), Alpa Shopha (mild chemosis), Vishushka Bhava (feeling of dryness), Parushya (dryness), Alpa Dushika (discharge), Kandu (itching) etc. Vata is the chief culprit and other Doshas are associated with it. In these studies, Ksheer Saindhav Pariseka was used as treatment.This treatment modalities showed highly significant results in relieving the signs and symptoms of disease with no adverse reaction