FrameNet II: Extended Theory and Practice Josef Ruppenhofer Michael Ellsworth Miriam R. L. Petruck Christopher R. Johnson Jan Scheffczyk Printed August 7, 2006 2 Contents 1 Introduction to the Project 5 1.1 Comparison with WordNet and ontologies . 7 1.2 What do we mean by word? . 8 2 Frame Development 11 3 FrameNet Annotation 19 3.1 Introduction . 19 3.2 Lexicographically motivated annotation practices . 23 3.3 Annotation with verbs as targets . 49 3.4 Annotation with nouns as targets . 52 3.5 Annotation with adjectives as targets . 62 3.6 Annotation with adverbs as targets . 62 3.7 Annotation with prepositions as targets . 63 3.8 Annotation relative to slot fillers . 65 4 Identifying Phrase Types 67 4.1 List of phrase types . 68 4.2 Phrase Type Labels for Noun Phrases . 72 4.3 Phrase Type Labels for Prepositional Phrases . 74 4.4 Phrase Type Labels for Verb Phrases . 78 4.5 Phrase Type Labels for Clauses . 81 4.6 Phrase Type Labels for Adjective Phrases . 85 4.7 Phrase Type Labels for Adverb Phrases (AVP) . 87 4.8 Phrase Type Labels for Quantifiers (QUANT) . 87 4.9 Phrase Type Labels for Quotes (QUO) . 87 5 Assigning Grammatical Functions 89 5.1 List of Grammatical Functions . 91 5.2 Assigning GFs for Verbs . 92 5.3 Assigning GFs for Adjectives . 96 5.4 Assigning GFs for Adverbs . 98 5.5 Assigning GFs for Prepositions . 99 5.6 Assigning GFs for Nouns . 100 3 4 CONTENTS 6 Semantic Relations and Types 103 6.1 Frame-to-frame Relations . 104 6.2 Semantic Type . 111 6.3 How to use relations . 120 7 Consistency Management in FrameNet 129 7.1 The Technical Architecture of FrameNet . 130 7.2 Techniques for Managing Quality . 131 7.3 Tolerant Quality Management . 131 7.4 Achievements . 132 A Major Extra-thematic Frame Elements 135 A.1 FEs related to temporal structure . 136 A.2 FEs related to places . 138 A.3 FEs related to additional participants . 140 A.4 FEs describing participants . 142 A.5 FEs describing events . 143 A.6 FEs related to co-occurring events and circumstances . 146 A.7 FEs related to the causal chain . 149 A.8 Other . 155 A.9 Constructionally induced interpretations . 157 Bibliography 161 Index 162 Chapter 1 Introduction to the Project The Berkeley FrameNet project is creating an on-line lexical resource for En- glish, based on frame semantics and supported by corpus evidence. The aim is to document the range of semantic and syntactic combinatory possibilities{ valences{of each word in each of its senses, through computer-assisted anno- tation of example sentences and automatic tabulation and display of the anno- tation results. The major product of this work, the FrameNet lexical database, currently contains more than 10,000 lexical units (defined below), more than 6,000 of which are fully annotated, in nearly 800 hierarchically-related semantic frames, exemplified in more than 135,000 annotated sentences. Beginning with Release 1.3, the quality of FrameNet data is monitored by a consistency manage- ment system. The database has gone through three releases, and is now in use by hundreds of researchers, teachers, and students around the world. (See the FrameNet Users page on our web-site). Active research projects are seeking to produce comparable frame-semantic lexicons for other languages and to devise means of automatically labeling running text with semantic frame information. A lexical unit (LU) is a pairing of a word with a meaning. Typically, each sense of a polysemous word belongs to a different semantic frame, a script- like conceptual structure that describes a particular type of situation, object, or event along with its participants and props. For example, the Apply heat frame describes a common situation involving a Cook, some Food, and a Heating Instrument, and is evoked by words such as bake, blanch, boil, broil, brown, simmer, steam, etc. We call these roles frame elements (FEs) and the frame-evoking words are LUs in the Apply heat frame. Some frames are more abstract, such as Change position on a scale, which is evoked by LUs such as decline, decrease, gain, plummet, rise, etc., and has FEs such as Item, Attribute, Initial value and Final value. In the simplest case, the frame-evoking LU is a verb and the FEs are its syntactic dependents: [Cook Matilde] fried [Food the catfish] [Heating instrument in a heavy iron skillet]. 5 6 CHAPTER 1. INTRODUCTION TO THE PROJECT [Item Colgate's stock] rose [Difference $3.64] [Final value to $49.94]. However, event nouns such as reduction in the Cause change of scalar position frame also evoke frames: ...the reduction [Item of debt levels] [Value 2 to $665 million] [Value 1 from $2.6 billion] or adjectives such as asleep in the Sleep frame: [Sleeper They] [Copula were] asleep [Duration for hours] The lexical entry for a predicating word, derived from such annotations, identifies the frame which underlies a given meaning and specifies the ways in which FEs are realized in structures headed by the word. Many common nouns, such as artifacts like hat or tower, typically serve as dependents rather than clearly evoking their own frames. The main purpose of annotating such items is to identify the most common predicates that govern phrases headed by them, and thus to illustrate the ways in which these common nouns function as FEs within frames evoked by the governing predicates. We do recognize that artifact and natural kind nouns also have a minimal frame structure of their own. For example, artifacts often occur together with expressions indicating their sub-type, the material of which they are made, their manner of production, and their purpose/use; these are defined as FEs in the frames for various types of artifacts. Consider two example sentences from the Clothing frame. (1) He took a packet of Woodbines out of the breast pocket of [his Wearer] [cotton Material] [shirt Garment] and lit one. (2) She had a [white Descriptor] [silk Material] [blouse Gar- ment] on, and a severe grey skirt that reached halfway down her calves. However, the frames evoked by artifact and natural kind nouns rarely dom- inate the clauses in which they occur, and so are seldom selected as targets of annotation. Formally, FrameNet annotations are constellations of triples that make up the frame element realization for each annotated sentence, each consisting of a frame element (for example, Food), a grammatical function (say, Object) and a phrase type (say, NP). We think of these three types of annotation on each tagged frame element as layers and they are displayed as such in the annotation software used in the project. However, to avoid visual clutter, the grammatical function and phrase type layers are not displayed in the web-based report system. The full data, available as part of the data download (see the FN- data link on the FrameNet homepage), include these three layers (and several more not discussed here) for all of the annotated sentences, along with com- plete frame and FE descriptions, frame-to-frame relations, and lexical entries summarizing the valence patterns for each annotated LU. 1.1. COMPARISON WITH WORDNET AND ONTOLOGIES 7 FrameNet annotations derive from two sources. In pursuing the goal of recording the range of semantic and syntactic combinatory possibilities (va- lences) of each word in each of its senses, we normally concentrate on a par- ticular target LU and extract sentences from the different texts of a corpus containing that LU. Then we annotate a selection of the extracted sentences in respect to the target LU. In another kind of work that represents a much smaller percentage of our overall annotations, we annotate running text. Full- text annotation differs from sentence annotation mostly in that the sentences are chosen for us, so to speak, by the author of the text. The annotation of running text is technically possible thanks to the annotation layering technique: FN lexicographers can one by one declare each word in a sentence a target, select a frame relative to which the new target is to be annotated, get a new set of annotation layers (frame element, grammatical function, phrase type) and appropriate frame element tags, and then annotate the relevant constituents. 1.1 Comparison with WordNet and ontologies The FrameNet database is a lexical resource with unique characteristics that differentiate it from other resources such as commercially available dictionaries and thesauri as well as from the best-known on-line lexical resource, WordNet. • Like dictionary subentries, FrameNet lexical units come with definitions, either from the Concise Oxford Dictionary, 10th Edition (courtesy of Ox- ford University Press) or a definition written by a FrameNet staff member. • Unlike commercial dictionaries, we provide multiple annotated examples of each sense of a word (i.e. each lexical unit). Moreover, the set of examples (approximately 20 per LU) illustrates all of the combinatorial possibilities of the lexical unit. • The examples are attestations taken from naturalistic corpora, rather than constructed by a linguist or lexicographer. The main FrameNet corpus is the 100-million-word British National CorpusBritish National Corpus (BNC), which is both large and balanced across genres (editorials, text- books, advertisements, novels, sermons, etc.), but, of course, lacks many specifically American expressions. We also use U.S. newswire texts pro- vided by the Linguistic Data ConsortiumLinguistic Data Consortium, and have recently acquired the newly released initial part of the American Na- tional Corpus, which we will begin using soon. • Our analysis of the English lexicon proceeds frame by frame rather than by lemma, whereas traditional dictionary-making proceeds word by word through the alphabet.