Henry Lieberman

JAAMAS Journal 2026 Journal Article

Training Agents to Recognize Text by Example

• Henry Lieberman
• Bonnie A. Nardi
• David J. Wright

Abstract An important function of an agent is to be “on the lookout” for bits of information that are interesting to its user, even if these items appear in the midst of a larger body of unstructured information. But how to tell these agents which patterns are meaningful and what to do with the result? Especially when agents are used to recognize text, they are usually driven by parsers which require input in the form of textual grammar rules. Editing grammars is difficult and error-prone for end users. Grammex [“Grammars by Example”] is the first direct manipulation interface designed to allow non-expert users to define grammars interactively. The user presents concrete examples of text that he or she would like the agent to recognize. Rules are constructed by an iterative process, where Grammex heuristically parses the example, displays a set of hypotheses, and the user critiques the system's suggestions. Actions to take upon recognition are also demonstrated by example.

KR Conference 2021 Short Paper

PATCHCOMM: Using Commonsense Knowledge to Guide Syntactic Parsers

• Yida Xin
• Henry Lieberman
• Peter Chin

Syntactic parsing technologies have become significantly more robust thanks to advancements in their underlying statistical and Deep Neural Network (DNN) techniques: most modern syntactic parsers can produce a syntactic parse tree for almost any sentence, including ones that may not be strictly grammatical. Despite improved robustness, such parsers still do not reflect the alternatives in parsing that are intrinsic in syntactic ambiguities. Two most notable such ambiguities are prepositional phrase (PP) attachment ambiguities and pronoun coreference ambiguities. In this paper, we discuss PatchComm, which uses commonsense knowledge to help resolve both kinds of ambiguities. To the best of our knowledge, we are the first to propose the general-purpose approach of using external commonsense knowledge bases to guide syntactic parsers. We evaluated PatchComm against the state-of-the-art (SOTA) spaCy parser on a PP attachment task and against the SOTA NeuralCoref module on a coreference task. Results show that PatchComm is successful at detecting syntactic ambiguities and using commonsense knowledge to help resolve them.

IJCAI Conference 2015 Conference Paper

Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying (Extended Abstract)

• Karthik Dinakar
• Rosalind Picard
• Henry Lieberman

We present an approach for cyberbullying detection based on state-of-the-art text classification and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying and construct BullySpace, a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers. On the intervention side, we explore a set of reflective userinteraction paradigms with the goal of promoting empathy among social network participants. We propose an air traffic control-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that incontext, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies.

AAAI Conference 2015 Conference Paper

Visualizing Inference

• Henry Lieberman
• Joe Henke

IS Journal 2010 Journal Article

Digital Intuition: Applying Common Sense Using Dimensionality Reduction

• Catherine Havasi
• Robert Speer
• James Pustejovsky
• Henry Lieberman

IS Journal 2009 Journal Article

Digital Intuition: Applying Common Sense Using Dimensionality Reduction

• Catherine Havasi
• Robert Speer
• James Pustejovsky
• Henry Lieberman

IS Journal 2009 Journal Article

Digital Intuition: Applying Common Sense Using Dimensionality Reduction

• Catherine Havasi
• Robyn Speer
• James Pustejovsky
• Henry Lieberman

Understanding the world we live in requires access to a large amount of background knowledge: the commonsense knowledge that most people have and most computer systems don't. Many of the limitations of artificial intelligence today relate to the problem of acquiring and understanding common sense. The Open Mind Common Sense project began to collect common sense from volunteers on the Internet starting in 2000. The collected information is converted to a semantic network called ConceptNet. Reducing the dimensionality of ConceptNet's graph structure gives a matrix representation called AnalogySpace, which reveals large-scale patterns in the data, smoothes over noise, and predicts new knowledge. Extending this work, we have created a method that uses singular value decomposition to aid in the integration of systems or representations. This technique, called blending, can be harnessed to find and exploit correlations between different resources, enabling commonsense reasoning over a broader domain.

IJCAI Conference 1995 Conference Paper

Letizia: An Agent That Assists Web Browsing

• Henry Lieberman

Letizia is a user interface agent that assists a user browsing the World Wide Web. As the user operates a conventional Web browser such as Netscape, the agent tracks user behavior and attempts to anticipate items of interest by doing concurrent, autonomous exploration of links from the user's current position. The agent automates a browsing strategy consisting of a best-first search augmented by heuristics inferring user interest from browsing behavior.

AAAI Conference 1994 Conference Paper

A User Interface for Knowledge Acquisition from Video

• Henry Lieberman

In conventional knowledge acquisition, a domain expert interacts with a knowledge engineer, who interviews the expert, and codes knowledge about the domain objects and procedures in a rule-based language, or other textual representation language. This indirect methodology can be tedious and errorprone, since the domain expert ’ verbal descriptions can be inaccurate or incomplete, and the knowledge engineer may not correctly interpret the expert ’ intent. We describe a user interface that allows a domain expert who is not a programmer to construct representations of objects and procedures directly from a video of a human performing an example procedure. The domain expert need not be fluent in the underlying representation language, since all interaction is through direct manipulation. Starting from digitized video, the user selects significant frames that illustrate before- and afterstates of important operations. Then the user graphically annotates the contents of each selected frame, selecting portions of the image to represent each part, labeling the parts, and indicating part/whole relationships. Finally, programming by demonstration techniques describe the actions that represent the transition between frames. The result is object descriptions for each object in the domain, generalized procedural descriptions, and visual and natural language documentation of the procedure. We illustrate the system in the domain of documentation of electrical devices. operational and maintenance procedures for

AAAI Conference 1983 Conference Paper

An Object-Oriented Simulator for the Apiary

• Henry Lieberman

This paper describes a simulator for the proposed Apiary, an object-oriented, message passing parallel machine for artificial intelligence applications, using the QCKV model of computation. The simulator implements an interpreter for the lowest level "virtual machine language" of the Apiary, specifying computations in terms of creating objects and sending messages rather than loading and storing registers. The simulator is itself programmed in the object-oriented style advocated by the actor philosophy, allowing experimentation with alternative implementation mechanisms without disturbing the behavior of the simulation. Technical details in the paper assume some familiarity with object-oriented programming and the actor formalism.