NLP

Detection of Self-Introductions in Legislative Testimony

foaad — Sat, 29 Aug 2026 08:21:09 +0000

Detection of Self-Introductions in Legislative Testimony foaad Sat, 08/29/2026 - 01:21

Authorship

Sofija Dimitrijevic, Pallavi Das, Kasey Liu, Foaad Khosmood

Publication

Applied AI Research Conference (AAIRC)

Conference

Location

Las Vega, NV

Sunday, August 2, 2026 - 12:00

Discourse Analysis of Legislative Bill Discussions

foaad — Sat, 29 Aug 2026 07:57:18 +0000

Discourse Analysis of Legislative Bill Discussions foaad Sat, 08/29/2026 - 00:57

Authorship

Isaac Rudnick, Nipun Das and Foaad Khosmood

Publication

Applied AI Research Conference (AAIRC)

Conference

Location

Las Vega, NV

Sunday, August 2, 2026 - 12:00

Predicting news coverage based on legislative information

foaad — Sat, 29 Aug 2026 07:51:50 +0000

Predicting news coverage based on legislative information foaad Sat, 08/29/2026 - 00:51

Authorship

Venkata Chaturvedula, Foaad Khosmood, Franz Kurfess, Kim Bisheff

Publication

Digital Humanities

Conference

Location

Daejeon, South Korea

Saturday, July 25, 2026 - 12:00

Detection of Self-Introductions in Legislative Testimony

foaad — Sat, 29 Aug 2026 07:45:18 +0000

Detection of Self-Introductions in Legislative Testimony foaad Sat, 08/29/2026 - 00:45

Authorship

Sofija Dimitrijevic, Pallavi Das, Kasey Liu, Foaad Khosmood

Publication

CSCSU

Conference

Wednesday, April 1, 2026 - 12:00

Tags

Abstract

Self-introductions are common in legislative committee testimonies. Successfully detecting them and extracting the speaker's name is enormously helpful in the task of speaker identification in the context of government meetings. In this paper, we present a pipeline for detection of self-introductions in legislative committee testimony using machine learning.

MultiHuSE: A Multimodal Dataset for Humour Styles and Emotions

foaad — Sat, 29 Aug 2026 06:59:04 +0000

MultiHuSE: A Multimodal Dataset for Humour Styles and Emotions foaad Fri, 08/28/2026 - 23:59

Authorship

Mary Kenneth and Foaad Khosmood

Publication

International Conference on Content-Based Multimedia Indexing (IEEE CBMI)

Conference

Location

Dublin, Ireland

Tuesday, October 7, 2025 - 12:00

Tags

Abstract

Computational recognition of verbal humour re-mains a challenging task, requiring an understanding of lan-guage, delivery style, emotions, and cultural context. Most existing approaches focus on binary classification and lack datasets that capture psychological dimensions of humour alongside variations in expression.

Explaining Humour Style Classifications: An XAI Approach to Understanding Computational Humour Analysis

foaad — Sun, 04 May 2025 23:28:35 +0000

Explaining Humour Style Classifications: An XAI Approach to Understanding Computational Humour Analysis

foaad Sun, 05/04/2025 - 16:28

Authorship

Mary Ogbuka Kenneth, Foaad Khosmood, Abbas Edalat

Publication

Journal of Data Mining & Digital Humanities, NLP4DH

Journal

Wednesday, April 2, 2025 - 12:00

Tags

doi

JDMDH

Abstract

Humour styles can have either a negative or a positive impact on well-being. Given the importance of these styles to mental health, significant research has been conducted on their automatic identification. However, the automated machine learning models used for this purpose are black boxes, making their prediction decisions opaque. Clarity and transparency are vital in the field of mental health. This paper presents an explainable AI (XAI) framework for understanding humour style classification, building upon previous work in computational humour analysis.

Systematic Literature Review: Computational Approaches for Humour Style Classification

foaad — Mon, 19 Feb 2024 19:04:52 +0000

Systematic Literature Review: Computational Approaches for Humour Style Classification

foaad Mon, 02/19/2024 - 11:04

Authorship

Mary Ogbuka Kenneth, Foaad Khosmood, Abbas Edalat

Publication

arXiv

Non refereed paper

Tuesday, January 30, 2024 - 12:00

Tags

arXiv.org

Abstract

Understanding various humour styles is essential for comprehending the multifaceted nature of humour and its impact on fields such as psychology and artificial intelligence. This understanding has revealed that humour, depending on the style employed, can either have therapeutic or detrimental effects on an individual's health and relationships. Although studies dedicated exclusively to computational-based humour style analysis remain somewhat rare, an expansive body of research thrives within related task, particularly binary humour and sarcasm recognition.

Improving Startup Success with Text Analysis

foaad — Tue, 12 Dec 2023 04:56:13 +0000

Improving Startup Success with Text Analysis

foaad Mon, 12/11/2023 - 20:56

Authorship

Emily Gavrilenko, Foaad Khosmood, Mahdi Rastad and Sadra Amiri Moghaddam

Publication

ICAIF 2023: ACM International Conference on AI in Finance, Workshop on NLP and Network Analysis

Workshop

Location

New York, NY

Monday, November 27, 2023 - 12:00

Tags

arXiv

Abstract

Investors are interested in predicting future success of startup companies, preferably using publicly available data which can be gathered using free online sources. Using public-only data has been shown to work, but there is still much room for improvement. Two of the best performing prediction experiments use 17 and 49 features respectively, mostly numeric and categorical in nature. In this paper, we significantly expand and diversify both the sources and the number of features (to 171) to achieve better prediction.

Automatic Bill Recommendation for Statehouse Journalists

foaad — Wed, 06 Sep 2023 19:49:18 +0000

Automatic Bill Recommendation for Statehouse Journalists

foaad Wed, 09/06/2023 - 12:49

Authorship

Michelle Perkonigg and Foaad Khosmood

Publication

Electronic Government

Conference

Sunday, August 20, 2023 - 12:00

Tags

EGOV 2023 Proceedings

Abstract

AI4Reporters is a project designed to produce automated electronic tip sheets for news reporters covering the statehouses (state level legislatures) in the United States. The project aims to capture the most important information that occurred in a bill discussion to allow reporters to quickly decide if they want to pursue a story on the subject. In this paper, we present, discuss and evaluate a module for the tip sheets that is designed to recommend additional bills to investigate for the reporter that receives the tip sheet.

Perkonigg_Electronic_Government_2023.pdf

Feature Engineering for US State Legislative Hearings: Stance, Affiliation, Engagement and Absentees

foaad — Sun, 30 Jul 2023 00:32:22 +0000

Feature Engineering for US State Legislative Hearings: Stance, Affiliation, Engagement and Absentees foaad Sat, 07/29/2023 - 17:32

Authorship

Joshua Grace and Foaad Khosmood

Publication

Digital Humanities 2023

Conference

Location

Graz, Austria

Saturday, July 1, 2023 - 12:00

Tags

DH 2023 Paper

arXiv preprint version (2021)

DH 2023 Proceedings

Abstract

In US State government legislatures, most activity occurs in committees made of lawmakers discussing bills. This paper presents systems to extract legislators' engagement and absence during committee meetings and the stance and affiliation of non-lawmakers making public comments. We propose a system to track the affiliation of organizations in public comments and whether the organizational representative supports or opposes the bill.