Michael Ellsworth

SemEval-2007 Task 19: Frame Semantic Structure Extraction

This task consists of recognizing words and phrases that evoke semantic frames as defined in the FrameNet project (http://framenet.icsi.berkeley.edu), and their semantic dependents, which are usually, but not always, their syntactic dependents (including subjects). The training data was FN annotated sentences. In testing, participants automatically annotated three previously unseen texts to match gold standard (human) annotation, including predicting previously unseen frames and roles. Precision and recall were measured both for matching of labels of frames and FEs and for matching of semantic dependency trees based on the annotation.

Graph Methods for Multilingual FrameNets.

This paper introduces a new, graph-based view of the data of the FrameNet project, which we hope will make it easier to understand the mixture of semantic and syntactic information contained in FrameNet annotation. We show how English FrameNet and other Frame Semantic resources can be represented as sets of interconnected graphs of frames, frame elements, semantic types, and annotated instances of them in text. We display examples of the new graphical representation based on the annotations, which combine Frame Semantics and Construction Grammar, thus capturing most of the syntax and semantics of each sentence. We consider how graph theory could help researchers to make better use of FrameNet data for tasks such as automatic Frame Semantic role labeling, paraphrasing, and translation. Finally, we describe the development of FrameNet-like lexical resources for other languages in the current Multilingual FrameNet project. which seeks to discover cross-lingual alignments, both in the lexicon (for frames and lexical units within frames) and across parallel or comparable texts. We conclude with an example showing graphically the semantic and syntactic similarities and differences between parallel sentences in English and Japanese. We will release software for displaying such graphs from the current data releases.

Representing Support Verbs in FrameNet

This paper presents FrameNet’s approach to the representation of Support Verbs, as but one type of multiword expression (MWE) included in the database. In addition to motivating and illustrating FrameNet’s newly consistent annotation practice for Support Verb constructions, the present work advocates adopting a broad view of what constitutes a multiword expression.

Getting the Roles Right: Using FrameNet in NLP

The FrameNet lexical database (Fillmore & Baker 2010; Ruppenhofer et al. 2006) http://framenet.icsi. berkeley.edu), covers roughly 13,000 lexical units (word senses) for the core Engish lexicon, associating them with roughly 1,200 fully defined semantic frames; these frames and their roles cover the majority of event types in everyday, non-specialist text, and they are documented with 200,000 manually annotated examples. This tutorial will teach attendees what they need to know to start using the FrameNet lexical database as part of an NLP system. We will cover the basics of Frame Semantics, explain how the database was created, introduce the Python API and the state of the art in automatic frame semantic role labeling systems; and we will discuss FrameNet collaboration with commercial partners. Time permitting, we will present new research on frames and annotation of locative relations, as well as corresponding metaphorical uses, along with information about how frame semantic roles can aid the interpretation of metaphors.