Author name cluster

Mark Feblowitz

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6 papers

1 author row

IJCAI Conference 2022 Conference Paper

Knowledge-Based News Event Analysis and Forecasting Toolkit

Oktie Hassanzadeh
Parul Awasthy
Ken Barker
Onkar Bhardwaj
Debarun Bhattacharjya
Mark Feblowitz
Lee Martie
Jian Ni

We present a toolkit for knowledge-based news event analysis and forecasting. The toolkit is powered by a Knowledge Graph (KG) of events curated from structured and unstructured sources of event-related knowledge. The toolkit provides functions for 1) mapping ongoing news headlines to concepts in the KG, 2) retrieval, reasoning, and visualization for causal analysis and forecasting, and 3) extraction of causal knowledge from text documents to augment the KG with additional domain knowledge. Each function has a number of implementations using a wide range of state-of-the-art neuro-symbolic techniques. We show how the toolkit enables building a human-in-the-loop explainable solution for event analysis and forecasting.

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AAAI Conference 2021 System Paper

IBM Scenario Planning Advisor: A Neuro-Symbolic ERM Solution

Mark Feblowitz
Oktie Hassanzadeh
Michael Katz
Shirin Sohrabi
Kavitha Srinivas
Octavian Udrea

Scenario Planning is a commonly used Enterprise Risk Management (ERM) technique to help decision makers with longterm plans by considering multiple alternative futures. It is typically a manual, highly labor intensive process involving dozens of experts and hundreds to thousands of person-hours. We previously introduced a Scenario Planning Advisor prototype (Sohrabi et al. 2018a, b) that focuses on generating scenarios quickly based on expert-developed models. We present the evolution of that prototype into a full-scale, clouddeployed ERM solution that: (i) can automatically (through NLP) create models from authoritative documents such as books, reports and articles, such that what typically took hundreds to thousands of person-hours can now be achieved in minutes to hours; (ii) can gather news and other feeds relevant to forces in the risk models and group them into storylines without any other user input; (iii) can generate scenarios at scale, starting with dozens of forces of interest from models with thousands of forces in seconds; (iv) provides interactive visualizations of scenario and force model graphs, including a full model editor in the browser. The SPA solution is deployed under a non-commercial use license at https: //spa-service. draco. res. ibm. com and includes a user guide to help new users get started. A video demonstration is available at https: //www. youtube. com/watch? v=Gd4CMKclkBY.

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AAAI Conference 2021 Short Paper

Unsupervised Causal Knowledge Extraction from Text using Natural Language Inference (Student Abstract)

Manik Bhandari
Mark Feblowitz
Oktie Hassanzadeh
Kavitha Srinivas
Shirin Sohrabi

In this paper, we address the problem of extracting causal knowledge from text documents in a weakly supervised manner. We target use cases in decision support and risk management, where causes and effects are general phrases without any constraints. We present a method called CaKNowLI which only takes as input the text corpus and extracts a highquality collection of cause-effect pairs in an automated way. We approach this problem using state-of-the-art natural language understanding techniques based on pre-trained neural models for Natural Language Inference (NLI). Finally, we evaluate the proposed method on existing and new benchmark data sets.

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AAAI Conference 2020 System Paper

Causal Knowledge Extraction through Large-Scale Text Mining

Oktie Hassanzadeh
Debarun Bhattacharjya
Mark Feblowitz
Kavitha Srinivas
Michael Perrone
Shirin Sohrabi
Michael Katz

In this demonstration, we present a system for mining causal knowledge from large corpuses of text documents, such as millions of news articles. Our system provides a collection of APIs for causal analysis and retrieval. These APIs enable searching for the effects of a given cause and the causes of a given effect, as well as the analysis of existence of causal relation given a pair of phrases. The analysis includes a score that indicates the likelihood of the existence of a causal relation. It also provides evidence from an input corpus supporting the existence of a causal relation between input phrases. Our system uses generic unsupervised and weakly supervised methods of causal relation extraction that do not impose semantic constraints on causes and effects. We show example use cases developed for a commercial application in enterprise risk management.

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IJCAI Conference 2019 Conference Paper

Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts

Oktie Hassanzadeh
Debarun Bhattacharjya
Mark Feblowitz
Kavitha Srinivas
Michael Perrone
Shirin Sohrabi
Michael Katz

In this paper, we study the problem of answering questions of type "Could X cause Y? " where X and Y are general phrases without any constraints. Answering such questions will assist with various decision analysis tasks such as verifying and extending presumed causal associations used for decision making. Our goal is to analyze the ability of an AI agent built using state-of-the-art unsupervised methods in answering causal questions derived from collections of cause-effect pairs from human experts. We focus only on unsupervised and weakly supervised methods due to the difficulty of creating a large enough training set with a reasonable quality and coverage. The methods we examine rely on a large corpus of text derived from news articles, and include methods ranging from large-scale application of classic NLP techniques and statistical analysis to the use of neural network based phrase embeddings and state-of-the-art neural language models.

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AAAI Conference 2015 Conference Paper

Towards Cognitive Automation of Data Science

Alain Biem
Maria Butrico
Mark Feblowitz
Tim Klinger
Yuri Malitsky
Kenney Ng
Adam Perer
Chandra Reddy

A Data Scientist typically performs a number of tedious and time-consuming steps to derive insight from a raw data set. The process usually starts with data ingestion, cleaning, and transformation (e. g. outlier removal, missing value imputation), then proceeds to model building, and finally a presentation of predictions that align with the end-users objectives and preferences. It is a long, complex, and sometimes artful process requiring substantial time and effort, especially because of the combinatorial explosion in choices of algorithms (and platforms), their parameters, and their compositions. Tools that can help automate steps in this process have the potential to accelerate the time-to-delivery of useful results, expand the reach of data science to non-experts, and offer a more systematic exploration of the available options. This work presents a step towards this goal.

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