With his theory of Frame Semantics, Charles J. Fillmore provided an utterly innovative way of approaching word meaning and conceptualization. FrameNet, the project developed in line of the new insights, gives out information on words that is often said to exceed that provided by traditional dictionaries. This term paper seeks to scrutinize this assumption by illustrating the working process by which entries for FrameNet are generated, using the example of the synonyms befuddle and bamboozle. Furthermore, it sets out to define the frames to which both Lexical Units belong. For this purpose, chapter 1 provides a theoretical introduc
and the design of FrameNet. Moreover, it contrasts FrameNet with other approaches to meaning. A corpus analysis for befuddle and bamboozle will be conducted typical use and the data will be analyzed in chapter 2. Chapter 3 presents befuddle and bamboozle in comparison and contrast. Finally, the frames that both lexical units have in common will be defined in chapter 4. The process is mainly based on the step-by-step description provided by Fillmore Lexicon. The Semantics of Risk & Atkins, 1992).
Table of Contents
0 Introduction
1 Theoretical Framework
1.1 Frame Semantics
1.2 FrameNet and Other Approaches
2 Corpus Analysis for Befuddle and Bamboozle
2.1 Analysis of the Data
2.1.1 Analysis of the Data for Befuddle
2.1.2 Analysis of the Data for Bamboozle
3 Befuddle and Bamboozle in Contrast
4 The Frames
4.1 The STIRRING_CONFUSION Frame
4.2 THE TRICKING FRAME
5 Conclusion
Objectives & Research Topics
This paper investigates the working process of generating FrameNet entries by analyzing the synonyms "befuddle" and "bamboozle". The research aims to scrutinize the efficacy of FrameNet in comparison to traditional dictionaries and to define the specific semantic frames that characterize these lexical units, while accounting for their respective agentivity, grammatical voice, and collocations.
- Theoretical foundations of Fillmore’s Frame Semantics and the design of FrameNet.
- Comparative corpus analysis of "befuddle" and "bamboozle" using data from the Corpus of Contemporary American English (COCA).
- Distinction between the STIRRING_CONFUSION frame and the TRICKING frame based on agent intentionality.
- Examination of semantic prosody, register, and syntactic behavior (active vs. passive) for both lexical units.
Excerpt from the Book
2.1.1 Analysis of the Data for Befuddle
As stated above, the verb befuddle is polysemous with two lexical units belonging to distinct frames. The first sense of the word belongs to the STIRRING_CONFUSION frame, while the second can be counted to the TRICKING frame. The difference is apparent in the degree of agentivity of the agent and in the grammatical voice.
Confusing Agent [CA] / Tricking Agent [CA]: The Confusing/Tricking Agent can either be an animate being, or an inanimate entity. The two options are distributed almost equally in the data (45.5% animate, 54.5 % inanimate). If the Confusing/Tricking Agent is an animate being, which BEFUDDLES the Confused/Tricked Patient intentionally it is most often expressed with a noun phrase in sentence initial position in an active sentence (97.1%). Here, the Confusing/Tricking Agent is highly agentive and usually the TRICKING sense of the word is expressed:
TA{NP} BEFUDDLE TP{NP}
TA [These skillful leaders]
BEFUDDLED, confused, and defeated
TP[the SEC].
Only once in the data, an animate Confusing Agent is introduced with a prepositional phrase in a passive sentence. Here it is the STIRRING_CONFUSION sense that is expressed:
CP{NP} BEFUDDLE CA{Prep NP}
It involved
Summary of Chapters
0. Introduction: Introduces the theory of Frame Semantics and the objective to contrast FrameNet’s entry generation with traditional dictionary approaches.
1. Theoretical Framework: Outlines the concepts of frames, frame elements, and null instantiation while positioning FrameNet within the broader linguistic landscape.
2. Corpus Analysis for Befuddle and Bamboozle: Details the corpus-based methodology and analyzes the distribution and semantic roles of the agents and patients for both target words.
3. Befuddle and Bamboozle in Contrast: Compares the two words, noting that "befuddle" is primarily used in playful contexts while "bamboozle" is frequently associated with serious, deceptive practices.
4. The Frames: Defines the new STIRRING_CONFUSION and TRICKING frames with their respective core and non-core frame elements.
5. Conclusion: Summarizes findings and discusses the limitations of current dictionary entries compared to the potential of the evolving FrameNet database.
Keywords
Frame Semantics, FrameNet, Corpus Linguistics, Befuddle, Bamboozle, Lexical Units, Semantic Prosody, STIRRING_CONFUSION, TRICKING, Agentivity, Null Instantiation, COCA, Polysemy, Lexicography, Syntactic Valence.
Frequently Asked Questions
What is the core focus of this linguistic study?
The study focuses on the process of creating FrameNet entries by analyzing the synonyms "befuddle" and "bamboozle" to reveal their semantic roles and usage contexts.
What are the primary thematic fields covered in the work?
The work covers frame semantics, corpus linguistics, the comparison of lexical synonyms, and the development of detailed semantic frames for specific verbal units.
What is the central research question?
The research seeks to determine how FrameNet entries are generated and to evaluate whether this approach provides a more comprehensive understanding of word meaning compared to traditional dictionary entries.
Which scientific methodology is applied?
The author employs a corpus-driven analysis using the Corpus of Contemporary American English (COCA) to extract tokens and examine the syntactic and semantic patterns of the target verbs.
What topics are discussed in the main chapters?
The main chapters discuss the theoretical background of frames, a detailed analysis of data for each target verb, the contrast between the two, and the definition of specific frames (STIRRING_CONFUSION and TRICKING).
Which keywords best characterize this research?
Key terms include Frame Semantics, FrameNet, Polysemy, Semantic Prosody, Agentivity, and Corpus Linguistics.
Why does the author suggest creating new frames like STIRRING_CONFUSION?
The author argues that existing FrameNet frames, such as EXPERIENCER_OBJ and INTENTIONAL_DECEPTION, are either too coarse-grained or fail to capture the specific nuance of the target words.
What role does the intentionality of the agent play in the results?
Intentionality is identified as the decisive factor; "bamboozle" is shown to have a higher degree of intentionality and is linked to the TRICKING frame, whereas "befuddle" often lacks such intent and is linked to the STIRRING_CONFUSION frame.
- Quote paper
- Marie Alicja Adler (Author), 2013, The Process of Creating a FrameNet Entry , Munich, GRIN Verlag, https://www.hausarbeiten.de/document/229949
