23 Matching Annotations
  1. Nov 2022
    1. Our annotators achieve thehighest precision with OntoNotes, suggesting thatmost of the entities identified by crowdworkers arecorrect for this dataset.

      interesting that the mention detection algorithm gives poor precision on OntoNotes and the annotators get high precision. Does this imply that there are a lot of invalid mentions in this data and the guidelines for ontonotes are correct to ignore generic pronouns without pronominals?

    2. an algorithm with high precision on LitBank orOntoNotes would miss a huge percentage of rele-vant mentions and entities on other datasets (con-straining our analysis)

      these datasets have the most limited/constrained definitions for co-reference and what should be marked up so it makes sense that precision is poor in these datasets

    3. Procedure: We first launch an annotation tutorial(paid $4.50) and recruit the annotators on the AMTplatform.9 At the end of the tutorial, each annotatoris asked to annotate a short passage (around 150words). Only annotators with a B3 score (Bagga

      Annotators are asked to complete a quality control exercise and only annotators who achieve a B3 score of 0.9 or higher are invited to do more annotation

    4. Annotation structure: Two annotation ap-proaches are prominent in the literature: (1) a localpairwise approach, annotators are shown a pairof mentions and asked whether they refer to thesame entity (Hladká et al., 2009; Chamberlain et al.,2016a; Li et al., 2020; Ravenscroft et al., 2021),which is time-consuming; or (2) a cluster-basedapproach (Reiter, 2018; Oberle, 2018; Bornsteinet al., 2020), in which annotators group all men-tions of the same entity into a single cluster. InezCoref we use the latter approach, which can befaster but requires the UI to support more complexactions for creating and editing cluster structures.

      ezCoref presents clusters of coreferences all at the same time - this is a nice efficient way to do annotation versus pairwise annotation (like we did for CD^2CR)

    5. owever, these datasets vary widelyin their definitions of coreference (expressed viaannotation guidelines), resulting in inconsistent an-notations both within and across domains and lan-guages. For instance, as shown in Figure 1, whileARRAU (Uryupina et al., 2019) treats generic pro-nouns as non-referring, OntoNotes chooses not tomark them at all

      One of the big issues is that different co-reference datasets have significant differences in annotation guidelines even within the coreference family of tasks - I found this quite shocking as one might expect coreference to be fairly well defined as a task.

    6. Specifically, our work investigates the quality ofcrowdsourced coreference annotations when anno-tators are taught only simple coreference cases thatare treated uniformly across existing datasets (e.g.,pronouns). By providing only these simple cases,we are able to teach the annotators the concept ofcoreference, while allowing them to freely interpretcases treated differently across the existing datasets.This setup allows us to identify cases where ourannotators disagree among each other, but moreimportantly cases where they unanimously agreewith each other but disagree with the expert, thussuggesting cases that should be revisited by theresearch community when curating future unifiedannotation guidelines

      The aim of the work is to examine a simplified subset of co-reference phenomena which are generally treated the same across different existing datasets.

      This makes spotting inter-annotator disagreement easier - presumably because for simpler cases there are fewer modes of failure?

    7. this work, we developa crowdsourcing-friendly coreference annota-tion methodology, ezCoref, consisting of anannotation tool and an interactive tutorial. Weuse ezCoref to re-annotate 240 passages fromseven existing English coreference datasets(spanning fiction, news, and multiple other do-mains) while teaching annotators only casesthat are treated similarly across these datasets

      this paper describes a new efficient coreference annotation tool which simplifies co-reference annotation. They use their tool to re-annotate passages from widely used coreference datasets.

  2. Feb 2021
  3. Apr 2020
  4. Jan 2020
    1. Annotation extends that power to a web made not only of linked resources, but also of linked segments within them. If the web is a loom on which applications are woven, then annotation increases the thread count of the fabric. Annotation-powered applications exploit the denser weave by defining segments and attaching data or behavior to them.

      I remember the first time I truly understood what Jon meant when he said this. One web page can have an unlimited number of specific addresses pointing into its parts--and through annotation these parts can be connected to an unlimited number of parts of other things. Jon called it: Exploding the web! How far we've come from Vannevar Bush's musings...

    1. The Web Annotation Data Model specification describes a structured model and format to enable annotations to be shared and reused across different hardware and software platforms.

      The publication of this web standard changed everything. I look forward to true testing of interoperable open annotation. The publication of the standard nearly three years ago was a game changer, but the game is still in progress. The future potential is unlimited!

  5. Sep 2019
    1. On the other hand, a resource may be generic in that as a concept it is well specified but not so specifically specified that it can only be represented by a single bit stream. In this case, other URIs may exist which identify a resource more specifically. These other URIs identify resources too, and there is a relationship of genericity between the generic and the relatively specific resource.

      I was not aware of this page when the Web Annotations WG was working through its specifications. The word "Specific Resource" used in the Web Annotations Data Model Specification always seemed adequate, but now I see that it was actually quite a good fit.

  6. Jul 2019
    1. driven by data—where schools use data to identify a problem, select a strategy to address the problem, set a target for improvement, and iterate to make the approach more effective and improve student achievement.

      Gates data model.

  7. Jun 2018
    1. About 600,000 people visit News Genius a month, Lehman said, a figure that had grown 10 times since before President Donald Trump was inaugurated. And the number of people who annotate a post on Genius each month is now at 10,000, up 30 percent from the start of the year. “More people are using News Genius now than ever,” Lehman said. Meanwhile, overall traffic to the website and apps has grown to 62 million a month.
    2. Soon after, Genius made a definitive push to realize Andreessen’s vision. By 2015, Genius claimed 40 million visitors to its website a month, 1 million of whom had annotated a post.
  8. Sep 2016
    1. Research: Student data are used to conduct empirical studies designed primarily to advance knowledge in the field, though with the potential to influence institutional practices and interventions. Application: Student data are used to inform changes in institutional practices, programs, or policies, in order to improve student learning and support. Representation: Student data are used to report on the educational experiences and achievements of students to internal and external audiences, in ways that are more extensive and nuanced than the traditional transcript.

      Ha! The Chronicle’s summary framed these categories somewhat differently. Interesting. To me, the “application” part is really about student retention. But maybe that’s a bit of a cynical reading, based on an over-emphasis in the Learning Analytics sphere towards teleological, linear, and insular models of learning. Then, the “representation” part sounds closer to UDL than to learner-driven microcredentials. Both approaches are really interesting and chances are that the report brings them together. Finally, the Chronicle made it sound as though the research implied here were less directed. The mention that it has “the potential to influence institutional practices and interventions” may be strategic, as applied research meant to influence “decision-makers” is more likely to sway them than the type of exploratory research we so badly need.

  9. Jun 2016
    1. dynamic documents

      A group of experts got together last year at Daghstuhl and wrote a white paper about this.

      Basically the idea is that the data, the code, the protocol/analysis/method, and the narrative should all exist as equal objects on the appropriate platform. Code in a code repository like Github, Data in a data repo that understands data formats, like Mendeley Data (my company) and Figshare, protocols somewhere like protocols.io and the narrative which ties it all together still at the publisher. Discussion and review can take the form of comments, or even better, annotations just like I'm doing now.

  10. Apr 2016
  11. Jan 2016
    1. Set Semantics¶ This tool is used to set semantics in EPUB files. Semantics are simply, links in the OPF file that identify certain locations in the book as having special meaning. You can use them to identify the foreword, dedication, cover, table of contents, etc. Simply choose the type of semantic information you want to specify and then select the location in the book the link should point to. This tool can be accessed via Tools->Set semantics.

      Though it’s described in such a simple way, there might be hidden power in adding these tags, especially when we bring eBooks to the Semantic Web. Though books are the prime example of a “Web of Documents”, they can also contribute to the “Web of Data”, if we enable them. It might take long, but it could happen.

  12. Dec 2015
  13. Oct 2015
    1. why not annotate, say, the Eiffel Tower itself

      As long as it has some URI, it can be annotated. Any object in the world can be described through the Semantic Web. Especially with Linked Open Data.

  14. Aug 2015
    1. I feel that there is a great benefit to fixing this question at the spec level. Otherwise, what happens? I read a web page, I like it and I am going to annotate it as being a great one -- but first I have to find out whether the URI my browser is used, conceptually by the author of the page, to represent some abstract idea?