StreamSide: A Fully-Customizable Open-Source Toolkit for Efficient Annotation of Meaning Representations

• URL: http://arxiv.org/abs/2109.09853v1
• Date: Mon, 20 Sep 2021 21:36:22 GMT
• Title: StreamSide: A Fully-Customizable Open-Source Toolkit for Efficient Annotation of Meaning Representations
• Authors: Jinho D. Choi and Gregor Williamson
• Abstract summary: StreamSide is an open-source toolkit for annotating multiple kinds of meaning representations. It supports frame-based and frameless annotation schemes. StreamSide is released under the Apache 2.0 license.
• Score: 17.74208462902158
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This demonstration paper presents StreamSide, an open-source toolkit for annotating multiple kinds of meaning representations. StreamSide supports frame-based annotation schemes e.g., Abstract Meaning Representation (AMR) and frameless annotation schemes e.g., Widely Interpretable Semantic Representation (WISeR). Moreover, it supports both sentence-level and document-level annotation by allowing annotators to create multi-rooted graphs for input text. It can open and automatically convert between several types of input formats including plain text, Penman notation, and its own JSON format enabling richer annotation. It features reference frames for AMR predicate argument structures, and also concept-to-text alignment. StreamSide is released under the Apache 2.0 license, and is completely open-source so that it can be customized to annotate enriched meaning representations in different languages (e.g., Uniform Meaning Representations). All StreamSide resources are publicly distributed through our open source project at: https://github.com/emorynlp/StreamSide.

Related papers

• LATex: Leveraging Attribute-based Text Knowledge for Aerial-Ground Person Re-Identification [63.07563443280147]
  We propose a novel framework named LATex for AG-ReID. It adopts prompt-tuning strategies to leverage attribute-based text knowledge. Our framework can fully leverage attribute-based text knowledge to improve the AG-ReID.
  arXiv  Detail & Related papers  (2025-03-31T04:47:05Z)
• OVMR: Open-Vocabulary Recognition with Multi-Modal References [96.21248144937627]
  Existing works have proposed different methods to embed category cues into the model, eg, through few-shot fine-tuning. This paper tackles open-vocabulary recognition from a different perspective by referring to multi-modal clues composed of textual descriptions and exemplar images. The proposed OVMR is a plug-and-play module, and works well with exemplar images randomly crawled from the Internet.
  arXiv  Detail & Related papers  (2024-06-07T06:45:28Z)
• Thresh: A Unified, Customizable and Deployable Platform for Fine-Grained Text Evaluation [11.690442820401453]
  We introduce Thresh, a unified, customizable and deployable platform for fine-grained evaluation. Thresh provides a community hub that hosts a collection of fine-grained frameworks and corresponding annotations made and collected by the community. For deployment, Thresh offers multiple options for any scale of annotation projects from small manual inspections to large crowdsourcing ones.
  arXiv  Detail & Related papers  (2023-08-14T06:09:51Z)
• Text Descriptions are Compressive and Invariant Representations for Visual Learning [63.3464863723631]
  We show that an alternative approach, in line with humans' understanding of multiple visual features per class, can provide compelling performance in the robust few-shot learning setting. In particular, we introduce a novel method, textit SLR-AVD (Sparse Logistic Regression using Augmented Visual Descriptors). This method first automatically generates multiple visual descriptions of each class via a large language model (LLM), then uses a VLM to translate these descriptions to a set of visual feature embeddings of each image, and finally uses sparse logistic regression to select a relevant subset of these features to classify
  arXiv  Detail & Related papers  (2023-07-10T03:06:45Z)
• Incorporating Graph Information in Transformer-based AMR Parsing [34.461828101932184]
  LeakDistill is a model and method that explores a modification to the Transformer architecture. We show how, by employing word-to-node alignment to embed graph structural information into the encoder at training time, we can obtain state-of-the-art AMR parsing.
  arXiv  Detail & Related papers  (2023-06-23T12:12:08Z)
• Gloss-Free End-to-End Sign Language Translation [59.28829048788345]
  We design the Gloss-Free End-to-end sign language translation framework (GloFE) Our method improves the performance of SLT in the gloss-free setting by exploiting the shared underlying semantics of signs and the corresponding spoken translation. We obtained state-of-the-art results on large-scale datasets, including OpenASL and How2Sign.
  arXiv  Detail & Related papers  (2023-05-22T09:57:43Z)
• Adapting the LodView RDF Browser for Navigation over the Multilingual Linguistic Linked Open Data Cloud [77.34726150561087]
  The paper is dedicated to the use of LodView for navigation over the multilingual Linked Open Data cloud. We define the class of Pubby-like tools that LodView belongs to, and clarify the relation of this class to the classes of dereferenciation tools, RDF browsers and LOD visualization tools.
  arXiv  Detail & Related papers  (2022-08-28T21:47:59Z)
• Generalized Funnelling: Ensemble Learning and Heterogeneous Document Embeddings for Cross-Lingual Text Classification [78.83284164605473]
  emphFunnelling (Fun) is a recently proposed method for cross-lingual text classification. We describe emphGeneralized Funnelling (gFun) as a generalization of Fun. We show that gFun substantially improves over Fun and over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-09-17T23:33:04Z)
• PAWLS: PDF Annotation With Labels and Structure [4.984601297028257]
  We present PDF with Labels and Structure (PAWLS), a new annotation tool for the PDF document format. PAWLS supports span-based textual annotation, N-ary relations and freeform, non-textual bounding boxes. A read-only PAWLS server is available at https://pawls.apps.allenai.org/.
  arXiv  Detail & Related papers  (2021-01-25T18:02:43Z)
• DART: Open-Domain Structured Data Record to Text Generation [91.23798751437835]
  We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs) We propose a procedure of extracting semantic triples from tables that encode their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and dialogue-act-based meaning representation tasks.
  arXiv  Detail & Related papers  (2020-07-06T16:35:30Z)
• Building a Hebrew Semantic Role Labeling Lexical Resource from Parallel Movie Subtitles [4.089055556130724]
  We present a semantic role labeling resource for Hebrew built semi-automatically through annotation projection from English. This corpus is derived from the multilingual OpenSubtitles dataset and includes short informal sentences. We provide a fully annotated version of the data including morphological analysis, dependency syntax and semantic role labeling in both FrameNet and PropBank styles. We train a neural SRL model on this Hebrew resource exploiting the pre-trained multilingual BERT transformer model, and provide the first available baseline model for Hebrew SRL as a reference point.
  arXiv  Detail & Related papers  (2020-05-17T10:03:42Z)
• Introduction of Quantification in Frame Semantics [0.0]
  This master report introduces wrappings as a way to envelop a sub-FS and treat it as a node. It provides a workable and tractable tool for higher-order relations with FS.
  arXiv  Detail & Related papers  (2020-01-25T15:52:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.