Applied Knowledge Representation and Natural Language Understanding Lab

AKRaNLU is a research team of students and faculty interested in working on Natural Lanaguage Understanding problems. The group enhances technologies that benefit from full language understanding by combining the meaning extracted from Natural Language texts with the knowledge of the world, represented in conceptual forms (knowledge respresentation and reasoning).

LAB MEMBERS

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Faculty

Dr. Julia Rayz

Director

Dr. Victor Raskin

Faculty Member

Dr. C. F. Hempelmann

Faculty Member

Students

Xiaonan Jing

Ph.D Candidate

Shih Feng Yang

Ph.D Candidate

Tatiana Ringenberg

Ph.D Candidate

Saltanat Tazhibayeva

Ph.D Candidate

Kanishka Misra

Ph.D Student

Soumya Agrawal

M.S. Student

Geetanjali Bihani

M.S. Student

Yifei Hu

M.S. Student

John Phan

Undergraduate Student

Alumni

Gilchan Park

M.S., Ph.D

Chien-Yi Hsiang

Ph.D

Hemanth Devarapalli

M.S.

Qiaofei Ye

M.S.

Parag Guruji

M.S.

Robert Hinh

M.S.

NEWS & PUBLICATIONS

Recent News
2020.03 Due to the coronavirus, AKRANLU will be moving all the lab meetings online after spring break
2019.12 Congrats to Tatiana on passing her thesis proposal defense!
2019.11 Congrats to Geetanjali on passing her thesis proposal defense!
2019.11 Congrats to Soumya on passing her thesis proposal defense!
2019.08 Welcome, Yifei, to AKRaNLU-grad!
2019.08 Congrats to Xiaonan "Shannon" Jing on passing her prelims!
2019.06 Julia and Victor recieved Outstanding Paper Award at IFSA-NAFIPS 2019
2019.06 Julia is elected to the Board of NAFIPS
2019.06 Congratulations to Qiaofei, Kanishka, Tatiana, and Hemanth on their papers being accepted to IEEE-SMC 2019
2019.05 Congratulations to Kanishka and Hemanth on their work being accepted to ICCM'2019 and CogSci 2019
2019.04 Congratulations to Shih-Feng on passing his prelims!
2019.04 Congratulations to Tatiana on passing her prelims!
2018.12 Congratulations to Saltanat on passing her prelims
2018.10 Congrats to John on being selected 1 of 4 Purdue scholars for NSF Center for Science of Information Channels Undergraduate Research Program!
2018.09 Congrats to Kanishka on a blog mention http://blog.revolutionanalytics.com/2018/09/anonymous-nyt-op-ed.html
2018.06 Congratulations to Cheryl and Gilchan on successful dissertation deposits!
2018.04 Congratulations to Kanishka on receiving a PRF fellowship!
2018.04 Congratulations to Chinmay on a very successful MS defense!
Recent Publications
Ontological Detection of Phishing Emails

Gilchan Park, Julia Rayz

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Authorship Analysis of Online Predatory Conversations using Character Level Con- volution Neural Networks.

Kanishka Misra, Hemanth Devarapalli, Tatiana Ringenberg, Julia Taylor Rayz

2019 IEEE International Conference on Systems, Man, and Cybernetics

Not So Cute but Fuzzy: Estimating Risk of Sexual Predation in Online Conversations.

Tatiana Ringenberg, Kanishka Misra, Julia Taylor Rayz

2019 IEEE International Conference on Systems, Man, and Cybernetics

Exploring Automatic Identification of Fantasy-Driven and Contact-Driven Sexual Solicitors

Tatiana Ringenberg, Kanishka Misra, Kathryn C. Seigfried-Spellar, Julia Taylor Rayz

2019 Third IEEE International Conference on Robotic Computing

A Sentiment Based Automatic Question-Answering Framework

Qiaofei Ye, Kanishka Misra, Hemanth Devarapalli, Julia Taylor Rayz

2019 IEEE International Conference on Systems, Man, and Cybernetics

Measuring the Influence of L1 on Learner English Errors in Content Words within Word Embedding Models.

Kanishka Misra, Hemanth Devarapalli, Julia Taylor Rayz

17th International Conference on Cognitive Modelling, 2019

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PROJECTS

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Ongoing Projects
Ontological Semantic Technology

Ontological Semantic Technology (OST) is a set of resources that enable computational understand of natural language. The resources consist of language independent ontology (representing general knowledge of the world), a number of language-dependent lexica (one per supported natural language, such as English, Russian, Korean, etc.) and a set of tools that interpret text into text-meaning representation (TMR), results of which are stored in an information base. The ultimate goal of OST is to understand explicit and implicit information, knowledge of which is compatible with the ontology.

Communication about Energy Usage in Smart Homes
Part of Sociotechnical systems to enable smart and connected energy-aware residential communities

The overall goal of the project is to discover new knowledge on how individuals, groups, and residential communities make decisions related to their home energy consumption through some feedback mechanism. This feedback mechanism can visual or verbal. The goal of the verbal communication in this project is to give users information about their energy usage informally, in a way that is most acceptable to them. The grain size of information (both in terms of numbers, energy devices, and user behavior) to be delivered to the user as well as the acceptability of verbal cues, based on the density level of text, different voices, effects, and grammatical constructions is part of the experiments performed. The communication of individual cues as well as prolonged dialogs is currently accomplished through Amazon Alexa.

Chat Analysis for Law Enforcement
In collaboration with High Tech Crime Unit

The overall goal of the project is to provide law enforcement with a faster and reliable way to triage suspicious conversations between minors and adults in order to prioritize existing cases based on their risk level. AKRaNLU’s component of the data consists of anonymized chats, mostly from social media platforms, each of which includes two or more participants. In order to detect risk of conversations, themes of various parts of chats are considered as well as style of the conversations that may point out to the groups of individuals working together or the same individual using various aliases and multiple social media platforms

Detection of Information Inconsistency

The aim of the project is to develop framework and preliminary results to show feasibility of inconsistency detection. The goal is to taxonomize various inconsistency types that can be seen in text, and identify existing methods that are suitable for identifying each type, as well as outline and develop new methods that would improve the performance of the overall system.

Computational Humor

Advancements in Artificial Intelligence allow computational agents to become more ‘natural’ in various communication aspects. Humor is one of the components of human interaction that should be accounted for, if computational agents are to communicate in a way that is similar to humans. The goal of the project is to be able to detect (and be able to explain) when a component of a text contains humor, when an utterance contains humor potential that can be highlighted with a computer-generated punchline, and to understand humor preferences of a particular individual based on his/her interaction with a computational system, and respond appropriately to a user. Existing research from multiple disciplines that contribute to humor studies is considered, as well as natural language processing techniques.

Detecting Biased Information in Text

Bias detection has becoming an increasingly popular area within natural language processing. The general goal of bias detection is not only to identify that it exists, and possibly flag it, but, more importantly, to reduce the its impact on models that are learned from data that contain it. Examples of bias within natural language processing includes gender and ethnic bias that can be traced through longitudinal data. However, biased information is also present in reporting various perspectives of events, social or political, as well as in what is commonly known as propaganda, with the latter heavily overlapping with psychological warfare and false information.