2020.framenet.xml

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<collection id="2020.framenet">
  <volume id="1">
    <meta>
      <booktitle>Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet</booktitle>
      <editor><first>Tiago T.</first><last>Torrent</last></editor>
      <editor><first>Collin F.</first><last>Baker</last></editor>
      <editor><first>Oliver</first><last>Czulo</last></editor>
      <editor><first>Kyoko</first><last>Ohara</last></editor>
      <editor><first>Miriam R. L.</first><last>Petruck</last></editor>
      <publisher>European Language Resources Association</publisher>
      <address>Marseille, France</address>
      <month>May</month>
      <year>2020</year>
      <isbn>979-10-95546-58-0</isbn>
    </meta>
    <frontmatter>
      <url hash="9b12a8db">2020.framenet-1.0</url>
    </frontmatter>
    <paper id="1">
      <title>Beyond lexical semantics: notes on pragmatic frames</title>
      <author><first>Oliver</first><last>Czulo</last></author>
      <author><first>Alexander</first><last>Ziem</last></author>
      <author><first>Tiago Timponi</first><last>Torrent</last></author>
      <pages>1–7</pages>
      <abstract>Framenets as an incarnation of frame semantics have been set up to deal with lexicographic issues (cf. Fillmore and Baker 2010, among others). They are thus concerned with lexical units (LUs) and the conceptual structure which categorizes these together. These lexically-evoked frames, however, do not reflect pragmatic properties of constructions (LUs and other types of constructions), such as expressing illocutions or being considered polite or very informal. From the viewpoint of a multilingual annotation effort, the Global FrameNet Shared Annotation Task, we discuss two phenomena, greetings and tag questions, which highlight the necessity both to investigate the role between construction and frame annotation on the one hand and to develop pragmatic frames describing social interactions which are not explicitly lexicalized.</abstract>
      <url hash="199c0b1d">2020.framenet-1.1</url>
      <language>eng</language>
    </paper>
    <paper id="2">
      <title>Finding Corresponding Constructions in <fixed-case>E</fixed-case>nglish and <fixed-case>J</fixed-case>apanese in a <fixed-case>TED</fixed-case> Talk Parallel Corpus using Frames-and-Constructions Analysis</title>
      <author><first>Kyoko</first><last>Ohara</last></author>
      <pages>8–12</pages>
      <abstract>This paper reports on an effort to search for corresponding constructions in English and Japanese in a TED Talk parallel corpus, using frames-and-constructions analysis (Ohara, 2019; Ohara and Okubo, 2020; cf. Czulo, 2013, 2017). The purpose of the paper is two-fold: (1) to demonstrate the validity of frames-and-constructions analysis to search for corresponding constructions in typologically unrelated languages; and (2) to assess whether the “Do schools kill creativity?” TED Talk parallel corpus, annotated in various languages for Multilingual FrameNet, is a good starting place for building a multilingual constructicon. The analysis showed that similar to our previous findings involving texts in a Japanese to English bilingual children’s book, the TED Talk bilingual transcripts include pairs of constructions that share similar pragmatic functions. While the TED Talk parallel corpus constitutes a good resource for frame semantic annotation in multiple languages, it may not be the ideal place to start aligning constructions among typologically unrelated languages. Finally, this work shows that the proposed method, which focuses on heads of sentences, seems valid for searching for corresponding constructions in transcripts of spoken data, as well as in written data of typologically-unrelated languages.</abstract>
      <url hash="4a76dec0">2020.framenet-1.2</url>
      <language>eng</language>
    </paper>
    <paper id="3">
      <title>Towards Reference-Aware <fixed-case>F</fixed-case>rame<fixed-case>N</fixed-case>et Annotation</title>
      <author><first>Levi</first><last>Remijnse</last></author>
      <author><first>Gosse</first><last>Minnema</last></author>
      <pages>13–22</pages>
      <abstract>In this paper, we introduce the task of using FrameNet to link structured information about real-world events to the conceptual frames used in texts describing these events. We show that frames made relevant by the knowledge of the real-world event can be captured by complementing standard lexicon-driven FrameNet annotations with frame annotations derived through pragmatic inference. We propose a two-layered annotation scheme with a ‘strict’ FrameNet-compatible lexical layer and a ‘loose’ layer capturing frames that are inferred from referential data.</abstract>
      <url hash="d3b3349c">2020.framenet-1.3</url>
      <language>eng</language>
    </paper>
    <paper id="4">
      <title>Frame-Based Annotation of Multimodal Corpora: Tracking (A)Synchronies in Meaning Construction</title>
      <author><first>Frederico</first><last>Belcavello</last></author>
      <author><first>Marcelo</first><last>Viridiano</last></author>
      <author><first>Alexandre</first><last>Diniz da Costa</last></author>
      <author><first>Ely Edison da Silva</first><last>Matos</last></author>
      <author><first>Tiago Timponi</first><last>Torrent</last></author>
      <pages>23–30</pages>
      <abstract>Multimodal aspects of human communication are key in several applications of Natural Language Processing, such as Machine Translation and Natural Language Generation. Despite recent advances in integrating multimodality into Computational Linguistics, the merge between NLP and Computer Vision techniques is still timid, especially when it comes to providing fine-grained accounts for meaning construction. This paper reports on research aiming to determine appropriate methodology and develop a computational tool to annotate multimodal corpora according to a principled structured semantic representation of events, relations and entities: FrameNet. Taking a Brazilian television travel show as corpus, a pilot study was conducted to annotate the frames that are evoked by the audio and the ones that are evoked by visual elements. We also implemented a Multimodal Annotation tool which allows annotators to choose frames and locate frame elements both in the text and in the images, while keeping track of the time span in which those elements are active in each modality. Results suggest that adding a multimodal domain to the linguistic layer of annotation and analysis contributes both to enrich the kind of information that can be tagged in a corpus, and to enhance FrameNet as a model of linguistic cognition.</abstract>
      <url hash="f00f5e27">2020.framenet-1.4</url>
      <language>eng</language>
    </paper>
    <paper id="5">
      <title>Combining Conceptual and Referential Annotation to Study Variation in Framing</title>
      <author><first>Marten</first><last>Postma</last></author>
      <author><first>Levi</first><last>Remijnse</last></author>
      <author><first>Filip</first><last>Ilievski</last></author>
      <author><first>Antske</first><last>Fokkens</last></author>
      <author><first>Sam</first><last>Titarsolej</last></author>
      <author><first>Piek</first><last>Vossen</last></author>
      <pages>31–40</pages>
      <abstract>We introduce an annotation tool whose purpose is to gain insights into variation of framing by combining FrameNet annotation with referential annotation. English FrameNet enables researchers to study variation in framing at the conceptual level as well through its packaging in language. We enrich FrameNet annotations in two ways. First, we introduce the referential aspect. Secondly, we annotate on complete texts to encode connections between mentions. As a result, we can analyze the variation of framing for one particular event across multiple mentions and (cross-lingual) documents. We can examine how an event is framed over time and how core frame elements are expressed throughout a complete text. The data model starts with a representation of an event type. Each event type has many incidents linked to it, and each incident has several reference texts describing it as well as structured data about the incident. The user can apply two types of annotations: 1) mappings from expressions to frames and frame elements, 2) reference relations from mentions to events and participants of the structured data.</abstract>
      <url hash="bc763d8d">2020.framenet-1.5</url>
      <language>eng</language>
    </paper>
    <paper id="6">
      <title><fixed-case>F</fixed-case>rame<fixed-case>N</fixed-case>et Annotations Alignment using Attention-based Machine Translation</title>
      <author><first>Gabriel</first><last>Marzinotto</last></author>
      <pages>41–47</pages>
      <abstract>This paper presents an approach to project FrameNet annotations into other languages using attention-based neural machine translation (NMT) models. The idea is to use an NMT encoder-decoder attention matrix to propose a word-to-word correspondence between the source and the target language. We combine this word alignment along with a set of simple rules to securely project the FrameNet annotations into the target language. We successfully implemented, evaluated and analyzed this technique on the English-to-French configuration. First, we analyze the obtained FrameNet lexicon qualitatively. Then, we use existing French FrameNet corpora to assert the quality of the translation. Finally, we trained a BERT-based FrameNet parser using the projected annotations and compared it to a BERT baseline. Results show substantial improvements in the French language, giving evidence to support that our approach could help to propagate FrameNet data-set on other languages.</abstract>
      <url hash="7e8b26b9">2020.framenet-1.6</url>
      <language>eng</language>
    </paper>
    <paper id="7">
      <title><fixed-case>G</fixed-case>reek within the Global <fixed-case>F</fixed-case>rame<fixed-case>N</fixed-case>et Initiative: Challenges and Conclusions so far</title>
      <author><first>Voula</first><last>Giouli</last></author>
      <author><first>Vera</first><last>Pilitsidou</last></author>
      <author><first>Hephaestion</first><last>Christopoulos</last></author>
      <pages>48–55</pages>
      <abstract>Large coverage lexical resources that bear deep linguistic information have always been considered useful for many natural language processing (NLP) applications including Machine Translation (MT). In this respect, Frame-based resources have been developed for many languages following Frame Semantics and the Berkeley FrameNet project. However, to a great extent, all those efforts have been kept fragmented. Consequentially, the Global FrameNet initiative has been conceived of as a joint effort to bring together FrameNets in different languages. The proposed paper is aimed at describing ongoing work towards developing the Greek (EL) counterpart of the Global FrameNet and our efforts to contribute to the Shared Annotation Task. In the paper, we will elaborate on the annotation methodology employed, the current status and progress made so far, as well as the problems raised during annotation.</abstract>
      <url hash="ec031005">2020.framenet-1.7</url>
      <language>eng</language>
    </paper>
    <paper id="8">
      <title>Using Verb Frames for Text Difficulty Assessment</title>
      <author><first>John</first><last>Lee</last></author>
      <author><first>Meichun</first><last>Liu</last></author>
      <author><first>Tianyuan</first><last>Cai</last></author>
      <pages>56–62</pages>
      <abstract>This paper presents the first investigation on using semantic frames to assess text difficulty. Based on Mandarin VerbNet, a verbal semantic database that adopts a frame-based approach, we examine usage patterns of ten verbs in a corpus of graded Chinese texts. We identify a number of characteristics in texts at advanced grades: more frequent use of non-core frame elements; more frequent omission of some core frame elements; increased preference for noun phrases rather than clauses as verb arguments; and more frequent metaphoric usage. These characteristics can potentially be useful for automatic prediction of text readability.</abstract>
      <url hash="a50f1c7f">2020.framenet-1.8</url>
      <language>eng</language>
    </paper>
    <paper id="9">
      <title>Deriving a <fixed-case>P</fixed-case>rop<fixed-case>B</fixed-case>ank Corpus from Parallel <fixed-case>F</fixed-case>rame<fixed-case>N</fixed-case>et and <fixed-case>UD</fixed-case> Corpora</title>
      <author><first>Normunds</first><last>Gruzitis</last></author>
      <author><first>Roberts</first><last>Darģis</last></author>
      <author><first>Laura</first><last>Rituma</last></author>
      <author><first>Gunta</first><last>Nešpore-Bērzkalne</last></author>
      <author><first>Baiba</first><last>Saulite</last></author>
      <pages>63–69</pages>
      <abstract>We propose an approach for generating an accurate and consistent PropBank-annotated corpus, given a FrameNet-annotated corpus which has an underlying dependency annotation layer, namely, a parallel Universal Dependencies (UD) treebank. The PropBank annotation layer of such a multi-layer corpus can be semi-automatically derived from the existing FrameNet and UD annotation layers, by providing a mapping configuration from lexical units in [a non-English language] FrameNet to [English language] PropBank predicates, and a mapping configuration from FrameNet frame elements to PropBank semantic arguments for the given pair of a FrameNet frame and a PropBank predicate. The latter mapping generally depends on the underlying UD syntactic relations. To demonstrate our approach, we use Latvian FrameNet, annotated on top of Latvian UD Treebank, for generating Latvian PropBank in compliance with the Universal Propositions approach.</abstract>
      <url hash="966c8930">2020.framenet-1.9</url>
      <language>eng</language>
    </paper>
    <paper id="10">
      <title>Building the Emirati <fixed-case>A</fixed-case>rabic <fixed-case>F</fixed-case>rame<fixed-case>N</fixed-case>et</title>
      <author><first>Andrew</first><last>Gargett</last></author>
      <author><first>Tommi</first><last>Leung</last></author>
      <pages>70–76</pages>
      <abstract>The Emirati Arabic FrameNet (EAFN) project aims to initiate a FrameNet for Emirati Arabic, utilizing the Emirati Arabic Corpus. The goal is to create a resource comparable to the initial stages of the Berkeley FrameNet. The project is divided into manual and automatic tracks, based on the predominant techniques being used to collect frames in each track. Work on the EAFN is progressing, and we here report on initial results for annotations and evaluation. The EAFN project aims to provide a general semantic resource for the Arabic language, sure to be of interest to researchers from general linguistics to natural language processing. As we report here, the EAFN is well on target for the first release of data in the coming year.</abstract>
      <url hash="b1093a31">2020.framenet-1.10</url>
      <language>eng</language>
    </paper>
    <paper id="11">
      <title>Exploring Crosslinguistic Frame Alignment</title>
      <author><first>Collin F.</first><last>Baker</last></author>
      <author><first>Arthur</first><last>Lorenzi</last></author>
      <pages>77–84</pages>
      <abstract>The FrameNet (FN) project at the International Computer Science Institute in Berkeley (ICSI), which documents the core vocabulary of contemporary English, was the first lexical resource based on Fillmore’s theory of Frame Semantics. Berkeley FrameNet has inspired related projects in roughly a dozen other languages, which have evolved somewhat independently; the current Multilingual FrameNet project (MLFN) is an attempt to find alignments between all of them. The alignment problem is complicated by the fact that these projects have adhered to the Berkeley FrameNet model to varying degrees, and they were also founded at different times, when different versions of the Berkeley FrameNet data were available. We describe several new methods for finding relations of similarity between semantic frames across languages. We will demonstrate ViToXF, a new tool which provides interactive visualizations of these cross-lingual relations, between frames, lexical units, and frame elements, based on resources such as multilingual dictionaries and on shared distributional vector spaces, making clear the strengths and weaknesses of different alignment methods.</abstract>
      <url hash="05813073">2020.framenet-1.11</url>
      <language>eng</language>
    </paper>
    <paper id="12">
      <title>Building Multilingual Specialized Resources Based on <fixed-case>F</fixed-case>rame<fixed-case>N</fixed-case>et: Application to the Field of the Environment</title>
      <author><first>Marie-Claude</first><last>L’ Homme</last></author>
      <author><first>Benoît</first><last>Robichaud</last></author>
      <author><first>Carlos</first><last>Subirats</last></author>
      <pages>85–92</pages>
      <abstract>The methodology developed within the FrameNet project is being used to compile resources in an increasing number of specialized fields of knowledge. The methodology along with the theoretical principles on which it is based, i.e. Frame Semantics, are especially appealing as they allow domain-specific resources to account for the conceptual background of specialized knowledge and to explain the linguistic properties of terms against this background. This paper presents a methodology for building a multilingual resource that accounts for terms of the environment. After listing some lexical and conceptual differences that need to be managed in such a resource, we explain how the FrameNet methodology is adapted for describing terms in different languages. We first applied our methodology to French and then extended it to English. Extensions to Spanish, Portuguese and Chinese were made more recently. Up to now, we have defined 190 frames: 112 frames are new; 38 are used as such; and 40 are slightly different (a different number of obligatory participants; a significant alternation, etc.) when compared to Berkeley FrameNet.</abstract>
      <url hash="99ffe3df">2020.framenet-1.12</url>
      <language>eng</language>
    </paper>
  </volume>
</collection>