Ontological Analysis of FrameNet for Natural Language Reasoning (original) (raw)
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Data-driven and ontological analysis of framenet for natural language reasoning
2010
This paper focuses on the improvement of the conceptual structure of FrameNet for the sake of applying this resource to knowledgeintensive NLP tasks requiring reasoning, such as question answering, information extraction etc. Ontological analysis supported by data-driven methods is used for axiomatizing, enriching and cleaning up frame relations. The impact of the achieved axiomatization is investigated on recognizing textual entailment.
FrameNet: A Knowledge Base for Natural Language Processing
Proceedings of Frame Semantics in NLP: A Workshop in Honor of Chuck Fillmore (1929-2014), 2014
Prof. Charles J. Fillmore had a lifelong interest in lexical semantics, and this culminated in the latter part of his life in a major research project, the FrameNet Project at the International Computer Science Institute in Berkeley, California (http://framenet. icsi.berkeley.edu). This paper reports on the background of this ongoing project, its connections to Fillmore's other research interests, and briefly outlines applications and current directions of growth for FrameNet, including FrameNets in languages other than English.
Building a computational lexicon and ontology with framenet
2004
This paper explores FrameNet as a resource for building a lexicon for deep syntactic and semantic parsing with a practical multipledomain parser. The TRIPS parser is a wide-coverage parser which uses a domain-independent ontology to produce semantic interpretations in 5 different application domains. We show how semantic information from FrameNet can be useful for developing a domainindependent ontology. While we used FrameNet as a starting point for our ontology development, we were unable to use FrameNet directly because it does not have links between syntax and semantics, and is not designed to include selectional restrictions. We discuss changes that needed to be made to the FrameNet frame structure to convert it to our domain-independent LF Ontology, the additions we made to FrameNet lexicon, and the resulting differences between the systems.
Semantic Analysis with a Lattice-Based Framenet
Language Individual Society, 2014
From a general perspective, models of reality defined using a formal language are known as formal ontologies. These general-purpose models can also be used in specific fields of application. As they are very big by nature, ontologies are prone to errors and omissions if they are formed manually. Furthermore, it might be difficult to adapt an ontology formed in this way to a particular field of application. This paper presents a formal ontology developed and integrated into the FrameNet technology via concept lattices which in turn provide us with a semantic analysis of sentences.
… , mobile Kommunikation und …, 2005
In this paper, we present a rule-based system for the assignment of FrameNet frames by way of a "detour via WordNet". The system can be used to overcome sparse-data problems of statistical systems trained on current FrameNet data. We devise a weighting scheme to select the best frame(s) out of a set of candidate frames, and present first figures of evaluation.
This chapter presents a comparison between FunGramKB, a multipurpose lexico-conceptual base for Natural Language Processing (NLP) systems, and FrameNet, a lexical resource for English whose objective is to document the range of semantic and syntactic combinatory possibilities of each sense of a word. After providing the reader with an overview of the two resources under scrutiny, we address their similarities and differences by focusing on the following issues: (1) methodology; (2) information at the lexical and conceptual levels; (3) relations between frames and concepts; (4) information management; and (5) multilingualism. To illustrate this comparison, we analyze how the verb dry is represented in each project.
Using Frame Semantics in Natural Language Processing
Proceedings of Frame Semantics in NLP: A Workshop in Honor of Chuck Fillmore (1929-2014), 2014
We summarize our experience using FrameNet in two rather different projects in natural language processing (NLP). We conclude that NLP can benefit from FrameNet in different ways, but we sketch some problems that need to be overcome.
FrameNet Meets the Semantic Web: Lexical Semantics for the Web
Lecture Notes in Computer Science, 2003
This paper describes FrameNet [9,1,3], an online lexical resource for English based on the principles of frame semantics [5,7,2]. We provide a data category specification for frame semantics and FrameNet annotations in an RDF-based language. More specifically, we provide an RDF markup for lexical units, defined as a relation between a lemma and a semantic frame, and frame-to-frame relations, namely Inheritance and Subframes. The paper includes simple examples of FrameNet annotated sentences in an XML/RDF format that references the project-specific data category specification. Frame Semantics and the FrameNet Project FrameNet's goal is to provide, for a significant portion of the vocabulary of contemporary English, a body of semantically and syntactically annotated sentences from which reliable information can be reported on the valences or combinatorial possibilities of each item included. A semantic frame is a script-like structure of inferences, which are linked to the meanings of linguistic units (lexical items). Each frame identifies a set of frame elements (FEs), which are frame-specific semantic roles (participants, props, phases of a state of affairs). Our description of each lexical item identifies the frames which underlie a given meaning and the ways in which the FEs are realized in structures headed by the word. The FrameNet database documents the range of semantic and syntactic combinatory possibilities (valences) of each word in each of its senses, through manual annotation of example sentences and automatic summarization of the resulting annotations. FrameNet I focused on governors, meaning that for the most part, annotation was done in respect to verbs; in FrameNet II, we have been annotating in respect to governed words as well. The FrameNet database is available in XML, and can be displayed and queried via the web and other interfaces. FrameNet data has also been translated into the DAML+OIL extension to XML and the Resource Description Framework (RDF). This paper will explain the theory behind FrameNet, briefly discuss the annotation process, and then describe how the FrameNet data can be represented in RDF, using DAML+OIL, so that researchers on the semantic web can use the data.
This is a contribution from Language Processing and Grammars. The role of functionally oriented computational models. Edited by Brian Nolan and Carlos Periñán-Pascual. This chapter presents a comparison between FunGramKB, a multipurpose lexico-conceptual base for Natural Language Processing (NLP) systems, and FrameNet, a lexical resource for English whose objective is to document the range of semantic and syntactic combinatory possibilities of each sense of a word. After providing the reader with an overview of the two resources under scrutiny, we address their similarities and differences by focusing on the following issues: (1) methodology; (2) information at the lexical and conceptual levels; (3) relations between frames and concepts; (4) information management; and (5) multilingualism. To illustrate this comparison, we analyze how the verb dry is represented in each project.
Linking FrameNet to the Suggested Upper Merged Ontology
International Conference on Formal Ontology in Information Systems, 2006
Deductive reasoning with natural language requires combining lexical resources with the world knowledge provided by ontologies. In this paper we describe the connection of FrameNet - a lexicon for English - to the Suggested Upper Merged Ontology (SUMO). We express general-domain links between FrameNet Semantic Types (ST) and SUMO classes in SUO- KIF, the language of SUMO. Based on these