Relation-and Phrase-level Linking of FrameNet with Sar-graphs

Workshop on Linked Data in Linguistics: Resources and Applications, co-located with the Annual Meeting of the Association for Computational Linguistics (LDL @ ACL), 2015

We present sar-graphs, a knowledge resource that links semantic relations from factual knowledge graphs to the linguistic patterns with which a language can express instances of these relations. Sar-graphs expand upon existing lexico-semantic resources by modeling syntactic and semantic information at the level of relations, and are hence useful for tasks such as knowledge base population and relation extraction. We present a language-independent method to automatically construct sar-graph instances that is based on distantly supervised relation extraction. We link sar-graphs at the lexical level to BabelNet, WordNet and UBY, and present our ongoing work on pattern-and relation-level linking to FrameNet. An initial dataset of English sar-graphs for 25 relations is made publicly available, together with a Java-based API.

Proceedings of the ACL 2003 workshop on Linguistic annotation getting the model right -, 2003

This paper describes FrameNet , an online lexical resource for English based on the principles of frame semantics , and considers the FrameNet database in reference to the proposed ISO model for linguistic annotation of language resources (ISO TC37 SC4 ) . We provide a data category specification for frame semantics and FrameNet annotations in an RDF-based language. More specifically, we provide a DAML+OIL 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.

… , 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.

Proceedings of the sixth …, 2011

FrameNet is an important lexical knowledge base featuring cognitive plausibility, and grounded in a large corpus. Besides being actively used by the NLP community, frames are a great source of knowledge patterns once converted into a knowledge representation language. In this paper we present our experience in converting the 1.5 XML version of FrameNet into RDF datasets published on the Linked Open Data cloud, which are interoperable with WordNet and other resources. In the conversion we have used Semion, a new tool that allows a rule-based, customized pipeline from XML to RDF and OWL data. In addition, we introduce a method to select and refactor part of the information related to frames as full-fledged OWL knowledge patterns. This last result has required non-trivial assumptions on how to interpret FrameNet relations as formal knowledge.

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.

Journal of Cognitive Science, 2015

We describe here the principles underlying the automatic creation of a semantic map to support navigation in a lexicon. Whenever we read a book, write a letter, or launch a query on Google, we always use words, the shorthand labels for more or less well-specified thoughts. The problem is that words may refuse to come to our mind when we need them most, at the very moment of speaking or writing. This is when we tend to reach for a dictionary. Yet, even dictionaries may fail to reveal the target word, although they contain them. This is not only a problem of input (poor query word), but also a problem of design : the way how words are organized and the kind of information associated to each one of them. We will consider in this paper one of the most original hand-crafted resources, WordNet, discussing its relative strengths and weaknesses with respect to word access. We will then describe an attempt to build automatically a subset of this resource, to conclude with the presentation of an approach meant to help authors (speakers/writers) to overcome the tipof-the-tongue-problem (TOT) even in cases where other resources, including Wordnet or Roget's Thesaurus, would fail.

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.

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.

2020

This paper presents the work on enhancing WordNet with semantic relations between verb synsets and classes of noun synsets corresponding to major participants in the predicates' conceptual structure. We first provide the theoretical background and motivation for the study and discuss the integration of the three complementary semantic resources -WordNet, VerbNet and FrameNet -which we then use to the end of devising a framework for enriching the relational structure of WordNet with a system of predicateargument and predicateadjunct relations. We pay particular attention to the analysis of the relations of inheritance between conceptual frames in FrameNet and between frame elements (the elements of these conceptual frames) which results in the elaboration of a hierarchy that is then translated as a set of relations and relation subtypes between predicates and the main elements in their conceptual structure. The conceptual frames with their corresponding frame elements and selectional restrictions are assigned to verb synsets in WordNet. We then go on to propose a typology of selectional preferences that verbs impose on the nouns they combine with. Using restrictions that have already been defined in FrameNet and VerbNet as well as other semantic information from the three resources, we propose a unified and extended set of selectional preferences represented as sets of WordNet classes and (sub)trees in the WordNet structure. The model is illustrated by a case study of the relation Theme and its subtypes.

2008

In this paper we describe our progress towards building an Interlingua based machine transla- tion system, by capturing the semantics of the source language sentences in the form of Uni- versal Networking Language (UNL) graphs from which the target language sentences can be produced. There are two stages to the UNL graph generation: first, the conceptual argu- ments of a situation are identified in the form of semantically relatable sequences (SRS) which are potential candidates for linking with semantic relations; next, the conceptual rela- tions such as instrument, source, goal, reason or agent are recognized, irrespective of their different syntactic configurations. The system has been tested against gold standard UNL expressions collected from various sources like Oxford Advanced Learners' Dictionary, XTAG corpus and Framenet corpus. Results indicate the promise and effectiveness of our approach on the difficult task of interlingua generation from text.