Vocab C2

[Pang Murdock]

Retrievea vector for a word in the vocabulary. Words can be looked up by stringor hash value. If the current vectors do not contain an entry for the word, a0-vector with the same number of dimensions(Vocab.vectors_length) as the current vectors is returned.

During serialization, spaCy will export several data fields used to restoredifferent aspects of the object. If needed, you can exclude them fromserialization by passing in the string names via the exclude argument.

The vocab terms are introduced during the class discussions. The students will have 2 quizzes on the terms. Basically the terms are split in half for the quizzes. Each quiz will only be given after the terms are introduced in the discussions.

@thomasb It is important for students to be able to put a term and definition to what they are doing. I have some of the basic programming concepts in vinyl on my classroom walls. It makes for good discussion, connections and decoration.

Explicit Contextualized Vocabulary Instruction - DHH (ECVI-DHH) is an approach to content area vocabulary instruction for use with young Deaf and Hard of Hearing (DHH) students. The explicit and contextualized instructional strategies expose students to new words and gives students practice in using the words expressively. The website will help professionals better understand the purpose of each component of instruction, the steps needed to implement the components, and suggestions for planning their own units of instruction using ECV-DHH.

ECVI-DHH is adapted from PAVEd for Success: Building Vocabulary and Language Development in Young Learners by Hamilton and Schwanenflugel (2013). The primary concept behind this approach to vocabulary instruction is that students need multiple exposures to a word in order to learn and use the word. Researchers who have studied vocabulary acquisition in young hearing students suggest that they need between 12 to 40 exposures to a new word before they learn it (McKeown, Beck, Omanson, & Pople, 1985; Reutzel & Cooter, 2004). The ECVI-DHH instructional program described on this website gives professionals the tools to provide young DHH children repeated exposures and opportunities to use new content-area words that they may not learn incidentally. Learning vocabulary enhances language comprehension which, in turn, enhances reading comprehension.

Note that the ordering in which key value pairs were inserted in the ordered_dict will be respected when building the vocab.Therefore if sorting by token frequency is important to the user, the ordered_dict should be created in a way to reflect this.

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I would suggest just using the reading mode on vocab then. Even if you already know the word seeing it in context and even just reading the sentence is great practice. I sped though n5 vocab that way and it actually increased my reading speed. I already knew 98% of the words so I found this to be the best way.

Desktop Anki with FSRS enabled is probably the most efficient way to review words and flashcards. Simply because right now there is no better algorithm. On top of that the process is just faster and more time efficient because you spend less than 10 seconds on a card and just press a button.

The Custom Vocab module allows you to create a controlled vocabulary and add it to a specific property in a resource template. When using that template for an item, the property will load with a dropdown limited to the options of the controlled vocabulary, rather than a text entry box.

For example, you may want to create an institution-specific list of locations that correspond to different collections on your campus, or a controlled list of people or places related to your holdings. This can help reduce typos and name variations, and can allow you to offer metadata browsing for more fields.

Note that manually entered terms or URIs do not need to be unique when entered; the module will only retain unique entries when saved. If you are entering identical URIs with different labels, only the final entry will be retained and earlier labels will be ignored.

Once you have created at least one vocabulary, the Custom Vocab tab will display a table of your existing vocabularies. The table displays the Label, the buttons for edit, delete, and display information (ellipsis), and the Owner or creator of the vocabulary.

There is also a button to "Import" a vocabulary using a file, in the top right. Note that importing will add a new listing to the table. If you are attmepting to update an existing vocabulary in your installation, do not use the "Import from file" button. Update the vocabulary from its entry in the table.

Clicking the ellipsis will show you the language of a vocabulary as well as a full listing of its terms. There are two buttons that allow you to "Export" a vocabulary, which can then be shared with other Omeka installations, or to "Update" the existing vocabulary from a file. Note that Items-type vocabularies cannot be exported or imported, as these vocabularies work as Omeka resources and could not replicated on another site.

Vocab Victor is designed to supplement classroom instruction. Assign this fun app in lieu of vocabulary lists, flashcards, and worksheets to give students focused instruction that will hold their attention. Victor teaches intermediate-level vocabulary, increasing competence across all four language skills, and helps students build native-like word association networks.

DCAT 2 supersedes DCAT [VOCAB-DCAT-20140116], but it does not make it obsolete. DCAT 2 maintains the DCAT namespace as its terms preserve backward compatibility with DCAT [VOCAB-DCAT-20140116]. DCAT 2 relaxes constraints and adds new classes and properties, but these changes do not break the definition of previous terms.

Any new implementation is expected to adopt DCAT 2, while the existing implementations do not need to upgrade to it, unless they want to use the new features. In particular, current DCAT deployments that do not overlap with the DCAT 2 new features (e.g., data services, time and space properties qualified relations, packaging) don't need to change anything to remain in conformance with DCAT 2.

DCAT enables a publisher to describe datasets and data services in a catalog using a standard model and vocabulary that facilitates the consumption and aggregation of metadata from multiple catalogs. This can increase the discoverability of datasets and data services. It also makes it possible to have a decentralized approach to publishing data catalogs and makes federated search for datasets across catalogs in multiple sites possible using the same query mechanism and structure. Aggregated DCAT metadata can serve as a manifest file as part of the digital preservation process.

This section describes the status of this document at the time of its publication. Other documents may supersede this document. A list of current W3C publications and the latest revision of this technical report can be found in the W3C technical reports index at

This document defines a major revision of the original DCAT vocabulary ([VOCAB-DCAT-20140116]) in response to new use cases, requirements and community experience since that publication. This revision extends the original DCAT standard in line with community practice while supporting diverse approaches to data description and dataset exchange. The main changes to the DCAT vocabulary have been:

This new version of the vocabulary updates and expands the original but preserves backward compatibility. A full list of the significant changes (with links to the relevent github issues) is described in D. Change history.

The exit criteria for CR focussed on v2 new features that replicate features that were included in application profiles of v1 as a way of remedying missing and necessary elements. The exit criteria also included recent commitments by organisations such as EC Joinup to adopt the DCAT v2 model in their work. Implementation will be evidenced by showing use of the new properties/classes (or terms with equivalent meaning) in implementations of catalogs.

Issues, requirements, and features that have been considered and discussed by the Data eXchange Working Group but have not been addressed due to lack of maturity or consensus are collected in GitHub. Those believed to be a priority for a future release are in the milestone DCAT Future Priority Work.

The original DCAT vocabulary was developed and hosted at the Digital Enterprise Research Institute (DERI), then refined by the eGov Interest Group, and finally standardized in 2014 [VOCAB-DCAT-20140116] by the Government Linked Data (GLD) Working Group.

This revised version of DCAT was developed by the Dataset Exchange Working Group in response to a new set of Use Cases and Requirements [DCAT-UCR] gathered from peoples' experience with the DCAT vocabulary from the time of the original version, and new applications that were not considered in the first version. A summary of the changes from [VOCAB-DCAT-20140116] is provided in D. Change history.

DCAT incorporates terms from pre-existing vocabularies where stable terms with appropriate meanings could be found, such as foaf:homepage and dct:title. Informal summary definitions of the externally-defined terms are included in the DCAT vocabulary for convenience, while authoritative definitions are available in the normative references. Changes to definitions in the references, if any, supersede the summaries given in this specification. Note that conformance to DCAT ( 4. Conformance) concerns usage of only the terms in the DCAT vocabulary specification, so possible changes to other external definitions will not affect the conformance of DCAT implementations.

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