Claire Bonial

A Hierarchical Unification of LIRICS and VerbNet Semantic Roles

This research compares several of the thematic roles of Verb Net (VN) to those of the Linguistic Infrastructure for Interoperable Resources and Systems (LIRICS). The purpose of this comparison is to develop a standard set of thematic roles that would be suited to a variety of natural language processing (NLP) applications. We draw from both resources to construct a unified set of semantic roles that will replace existing VN semantic roles. Through the process of comparison, we find that a hierarchical organization of coarse-grained, intermediate and fine-grained roles facilitates mapping between semantic resources of differing granularity and allows for flexibility in how VN can be used for diverse NLP applications, thus, we propose a hierarchical taxonomy of the unified role set. The comparison and subsequent development of the hierarchy reveals a level of granularity shared by both resources, which could be further developed into a standard set of thematic roles for the International Organization for Standardization (ISO).

On predicting social unrest using social media

We study the possibility of predicting a social protest (planned, or unplanned) based on social media messaging. We consider the process called mobilization, described in the literature as the precursor of participation. Mobilization includes four stages: being sympathetic to the cause, being aware of the movement, motivation to take part and ability to participate. We suggest that expressions of mobilization in communications of individuals may be used to predict the approaching protest. We have utilized several Natural Language Processing techniques to create a methodology to identify mobilization in social media communication. Results of experimentation with Twitter data collected before and during the 2015 Baltimore events and the information on actual protests taken from news media show a correlation over time between volume of Twitter communications related to mobilization and occurrences of protest at certain geographical locations. We conclude with discussion of possible theoretical explanations and practical applications of these results.

The VerbCorner Project: Findings from Phase 1 of crowd-sourcing a semantic decomposition of verbs

Any given verb can appear in some syntactic frames (Sally broke the vase, The vase broke) but not others (*Sally broke at the vase, *Sally broke the vase to John). There is now considerable evidence that the syntactic behaviors of some verbs can be predicted by their meanings, and many current theories posit that this is true for most if not all verbs. If true, this fact would have striking implications for theories and models of language acquisition, as well as numerous applications in natural language processing. However, empirical investigations to date have focused on a small number of verbs. We report on early results from VerbCorner, a crowd-sourced project extending this work to a large, representative sample of English verbs.

Exploring Variation of Natural Human Commands to a Robot in a Collaborative Navigation Task

Robot-directed communication is variable, and may change based on human perception of robot capabilities. To collect training data for a dialogue system and to investigate possible communication changes over time, we developed a Wizard-of-Oz study that (a) simulates a robot’s limited understanding, and (b) collects dialogues where human participants build a progressively better mental model of the robot’s understanding. With ten participants, we collected ten hours of human-robot dialogue. We analyzed the structure of instructions that participants gave to a remote robot before it responded. Our findings show a general initial preference for including metric information (e.g., move forward 3 feet) over landmarks (e.g., move to the desk) in motion commands, but this decreased over time, suggesting changes in perception.

Current Directions in English and Arabic PropBank

This chapter gives an overview of the infrastructure, annotation practices, and current challenges of both the English and Arabic PropBank corpora. More details about the Hindi and Urdu PropBanks can be found in chapter “ The Hindi/Urdu Treebank Project” (this volume). The focus of current efforts is on expanding the types of relations covered by PropBank. Previously, the annotation effort focused on event relations expressed solely by verbs. (A separate but related effort, NomBank, focused on nouns [26].) However, a complete representation of event relations within and across sentences requires expanding that focus to all syntactic realizations of event and state semantics, including expressions in the form of nouns, adjectives and multi-word expressions. This effort reflects a general desire to move to a deeper level of semantic understanding, abstracting away from language-particular syntactic facts. The chapter closes with a discussion of future directions for PropBank.

Assessing Agreement in Human-Robot Dialogue Strategies: A Tale of Two Wizards

The Wizard-of-Oz (WOz) method is a common experimental technique in virtual agent and human-robot dialogue research for eliciting natural communicative behavior from human partners when full autonomy is not yet possible. For the first phase of our research reported here, wizards play the role of dialogue manager, acting as a robot’s dialogue processing. We describe a novel step within WOz methodology that incorporates two wizards and control sessions: the wizards function much like corpus annotators, being asked to make independent judgments on how the robot should respond when receiving the same verbal commands in separate trials. We show that inter-wizard discussion after the control sessions and the resolution with a reconciled protocol for the follow-on pilot sessions successfully impacts wizard behaviors and significantly aligns their strategies. We conclude that, without control sessions, we would have been unlikely to achieve both the natural diversity of expression that comes with multiple wizards and a better protocol for modeling an automated system.

An Approach to Take Multi-Word Expressions

This research discusses preliminary efforts to expand the coverage of the PropBank lexicon to multi-word and idiomatic expressions, such as take one for the team. Given overwhelming numbers of such expressions, an efficient way for increasing coverage is needed. This research discusses an approach to adding multiword expressions to the PropBank lexicon in an effective yet semantically rich fashion. The pilot discussed here uses double annotation of take multi-word expressions, where annotations provide information on the best strategy for adding the multi-word expression to the lexicon. This work represents an important step for enriching the semantic information included in the PropBank corpus, which is a valuable and comprehensive resource for the field of Natural Language Processing.

The Rich Event Ontology.

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.

VerbNet/OntoNotes-Based Sense Annotation

In this chapter, we present our challenges and successes in producing the OntoNotes word sense groupings [41], which represent a slightly more coarse-grained set of English verb senses drawn from WordNet [13], and which have provided the foundation for our VerbNet sense annotation. These sense groupings were based on the successive merging of WordNet senses into more coarse-grained senses according to the results of inter-annotator agreement [10]. We find that the sense granularity, or level of semantic specificity found in this inventory, reflects sense distinctions that can be made consistently and accurately by human annotators, who achieve a high inter-annotator agreement rate of 89\(\%\). This, in turn, leads to a correspondingly high system performance for automatic WSD: sense distinctions with this level of granularity can be detected automatically at 87–89\(\%\) accuracy, making them effective for NLP applications [9].

Multimodal Use of an Upper-Level Event Ontology

We describe the ongoing development of a lexically-informed, upper-level event ontology and explore use cases of the ontology. This ontology draws its lexical sense distinctions from VerbNet, FrameNet and the Rich Entities, Relations and Events Project. As a result, the ontology facilitates interoperability and the combination of annotations done for each independent resource. While this ontology is intended to be practical for a variety of applications, here we take the initial steps in determining whether or not the event ontology could be utilized in multimodal applications, specifically to recognize and reason about events in both text and video. We find that the ontology facilitates the generalization of potentially noisy or sparse individual realizations of events into larger categories of events and enables reasoning about event relations and participants, both of which are useful in event recognition and interpretation regardless of modality.