Susan Windisch Brown

Also published as: Susan Brown, Susan W. Brown, Susan Windisch Brown


Stability of Forensic Text Comparison System
Susan Brown | Shunichi Ishihara
Proceedings of the The 20th Annual Workshop of the Australasian Language Technology Association


A Graphical Interface for Curating Schemas
Piyush Mishra | Akanksha Malhotra | Susan Windisch Brown | Martha Palmer | Ghazaleh Kazeminejad
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

Much past work has focused on extracting information like events, entities, and relations from documents. Very little work has focused on analyzing these results for better model understanding. In this paper, we introduce a curation interface that takes an Information Extraction (IE) system’s output in a pre-defined format and generates a graphical representation of its elements. The interface supports editing while curating schemas for complex events like Improvised Explosive Device (IED) based scenarios. We identify various schemas that either have linear event chains or contain parallel events with complicated temporal ordering. We iteratively update an induced schema to uniquely identify events specific to it, add optional events around them, and prune unnecessary events. The resulting schemas are improved and enriched versions of the machine-induced versions.

RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System
Haoyang Wen | Ying Lin | Tuan Lai | Xiaoman Pan | Sha Li | Xudong Lin | Ben Zhou | Manling Li | Haoyu Wang | Hongming Zhang | Xiaodong Yu | Alexander Dong | Zhenhailong Wang | Yi Fung | Piyush Mishra | Qing Lyu | Dídac Surís | Brian Chen | Susan Windisch Brown | Martha Palmer | Chris Callison-Burch | Carl Vondrick | Jiawei Han | Dan Roth | Shih-Fu Chang | Heng Ji
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations

We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video). The system advances state-of-the-art from two aspects: (1) extending from sentence-level event extraction to cross-document cross-lingual cross-media event extraction, coreference resolution and temporal event tracking; (2) using human curated event schema library to match and enhance the extraction output. We have made the dockerlized system publicly available for research purpose at GitHub, with a demo video.

SemLink 2.0: Chasing Lexical Resources
Kevin Stowe | Jenette Preciado | Kathryn Conger | Susan Windisch Brown | Ghazaleh Kazeminejad | James Gung | Martha Palmer
Proceedings of the 14th International Conference on Computational Semantics (IWCS)

The SemLink resource provides mappings between a variety of lexical semantic ontologies, each with their strengths and weaknesses. To take advantage of these differences, the ability to move between resources is essential. This work describes advances made to improve the usability of the SemLink resource: the automatic addition of new instances and mappings, manual corrections, sense-based vectors and collocation information, and architecture built to automatically update the resource when versions of the underlying resources change. These updates improve coverage, provide new tools to leverage the capabilities of these resources, and facilitate seamless updates, ensuring the consistency and applicability of these mappings in the future.


VerbNet Representations: Subevent Semantics for Transfer Verbs
Susan Windisch Brown | Julia Bonn | James Gung | Annie Zaenen | James Pustejovsky | Martha Palmer
Proceedings of the First International Workshop on Designing Meaning Representations

This paper announces the release of a new version of the English lexical resource VerbNet with substantially revised semantic representations designed to facilitate computer planning and reasoning based on human language. We use the transfer of possession and transfer of information event representations to illustrate both the general framework of the representations and the types of nuances the new representations can capture. These representations use a Generative Lexicon-inspired subevent structure to track attributes of event participants across time, highlighting oppositions and temporal and causal relations among the subevents.


Integrating Generative Lexicon Event Structures into VerbNet
Susan Windisch Brown | James Pustejovsky | Annie Zaenen | Martha Palmer
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

Automatically Extracting Qualia Relations for the Rich Event Ontology
Ghazaleh Kazeminejad | Claire Bonial | Susan Windisch Brown | Martha Palmer
Proceedings of the 27th International Conference on Computational Linguistics

Commonsense, real-world knowledge about the events that entities or “things in the world” are typically involved in, as well as part-whole relationships, is valuable for allowing computational systems to draw everyday inferences about the world. Here, we focus on automatically extracting information about (1) the events that typically bring about certain entities (origins), (2) the events that are the typical functions of entities, and (3) part-whole relationships in entities. These correspond to the agentive, telic and constitutive qualia central to the Generative Lexicon. We describe our motivations and methods for extracting these qualia relations from the Suggested Upper Merged Ontology (SUMO) and show that human annotators overwhelmingly find the information extracted to be reasonable. Because ontologies provide a way of structuring this information and making it accessible to agents and computational systems generally, efforts are underway to incorporate the extracted information to an ontology hub of Natural Language Processing semantic role labeling resources, the Rich Event Ontology.

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Proceedings of the Workshop Events and Stories in the News 2018
Tommaso Caselli | Ben Miller | Marieke van Erp | Piek Vossen | Martha Palmer | Eduard Hovy | Teruko Mitamura | David Caswell | Susan W. Brown | Claire Bonial
Proceedings of the Workshop Events and Stories in the News 2018


The Rich Event Ontology
Susan Brown | Claire Bonial | Leo Obrst | Martha Palmer
Proceedings of the Events and Stories in the News Workshop

In this paper we describe a new lexical semantic resource, The Rich Event On-tology, which provides an independent conceptual backbone to unify existing semantic role labeling (SRL) schemas and augment them with event-to-event causal and temporal relations. By unifying the FrameNet, VerbNet, Automatic Content Extraction, and Rich Entities, Relations and Events resources, the ontology serves as a shared hub for the disparate annotation schemas and therefore enables the combination of SRL training data into a larger, more diverse corpus. By adding temporal and causal relational information not found in any of the independent resources, the ontology facilitates reasoning on and across documents, revealing relationships between events that come together in temporal and causal chains to build more complex scenarios. We envision the open resource serving as a valuable tool for both moving from the ontology to text to query for event types and scenarios of interest, and for moving from text to the ontology to access interpretations of events using the combined semantic information housed there.


Multimodal Use of an Upper-Level Event Ontology
Claire Bonial | David Tahmoush | Susan Windisch Brown | Martha Palmer
Proceedings of the Fourth Workshop on Events


The IMAGACT Visual Ontology. An Extendable Multilingual Infrastructure for the representation of lexical encoding of Action
Massimo Moneglia | Susan Brown | Francesca Frontini | Gloria Gagliardi | Fahad Khan | Monica Monachini | Alessandro Panunzi
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Action verbs have many meanings, covering actions in different ontological types. Moreover, each language categorizes action in its own way. One verb can refer to many different actions and one action can be identified by more than one verb. The range of variations within and across languages is largely unknown, causing trouble for natural language processing tasks. IMAGACT is a corpus-based ontology of action concepts, derived from English and Italian spontaneous speech corpora, which makes use of the universal language of images to identify the different action types extended by verbs referring to action in English, Italian, Chinese and Spanish. This paper presents the infrastructure and the various linguistic information the user can derive from it. IMAGACT makes explicit the variation of meaning of action verbs within one language and allows comparisons of verb variations within and across languages. Because the action concepts are represented with videos, extension into new languages beyond those presently implemented in IMAGACT is done using competence-based judgments by mother-tongue informants without intense lexicographic work involving underdetermined semantic description


VerbNet Class Assignment as a WSD Task
Susan Windisch Brown | Dmitriy Dligach | Martha Palmer
Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)

Incorporating Coercive Constructions into a Verb Lexicon
Claire Bonial | Susan Windisch Brown | Jena D. Hwang | Christopher Parisien | Martha Palmer | Suzanne Stevenson
Proceedings of the ACL 2011 Workshop on Relational Models of Semantics


Number or Nuance: Which Factors Restrict Reliable Word Sense Annotation?
Susan Windisch Brown | Travis Rood | Martha Palmer
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This study attempts to pinpoint the factors that restrict reliable word sense annotation, focusing on the influence of the number of senses annotators use and the semantic granularity of those senses. Both of these factors may be possible causes of low interannotator agreement (ITA) when tagging with fine-grained word senses, and, consequently, low WSD system performance (Ng et al., 1999; Snyder & Palmer, 2004; Chklovski & Mihalcea, 2002). If number of senses is the culprit, modifying the task to show fewer senses at a time could improve annotator reliability. However, if overly nuanced distinctions are the problem, then more general, coarse-grained distinctions may be necessary for annotator success and may be all that is needed to supply systems with the types of distinctions that people make. We describe three experiments that explore the role of these factors in annotation performance. Our results indicate that of these two factors, only the granularity of the senses restricts interannotator agreement, with broader senses resulting in higher annotation reliability.


VerbNet overview, extensions, mappings and applications
Karin Kipper Schuler | Anna Korhonen | Susan Brown
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Tutorial Abstracts


Choosing Sense Distinctions for WSD: Psycholinguistic Evidence
Susan Windisch Brown
Proceedings of ACL-08: HLT, Short Papers

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Invited Talk: The Relevance of a Cognitive Model of the Mental Lexicon to Automatic Word Sense Disambiguation
Martha Palmer | Susan Brown
Coling 2008: Proceedings of the workshop on Human Judgements in Computational Linguistics


Criteria for the Manual Grouping of Verb Senses
Cecily Jill Duffield | Jena D. Hwang | Susan Windisch Brown | Dmitriy Dligach | Sarah E. Vieweg | Jenny Davis | Martha Palmer
Proceedings of the Linguistic Annotation Workshop