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Retrieve a 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.

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So wanikani does not really teach vocabulary, and it leaves a ton of gaps especially in terms of knowledge and usefulness in the words. for example, it teaches you stuff like before words like and I am aware that wanikani is for specifically kanji first, but the lack of nuance and word order is not very good when it comes to trying to piece the language together. as well, Wanikani offers 0 information of hiragana only words which is much more of low level japanese than one may initially think.

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.

On one hand, I like the simplicity of Vocab cards (or Anime cards). They're purely for memorization and having an example sentence on the back allows you to see what context they are used in. They're also faster to review and easier to make (you don't necessarily need to find an i+1 sentence). The argument is that, through vocab cards, you are brute-force memorizing the word + meaning, and it's reinforced through actual immersion. All simple and dandy, right?

On the other hand, the idea of learning vocab through Sentence cards sounds appealing. You're always learning and seeing the word in its proper context, which theoretically, should give you a better grasp on the language. However, through sentence cards, you're basically memorizing the overall meaning of the sentence, rather than the individual word itself. I would assume that this would make it more difficult to retain the word when used in other sentences or contexts (however, I don't have the experience to back this up).

Mathematics vocabulary word wall cards provide a display of mathematics content words and associated visual cues to assist in vocabulary development. The cards should be used as an instructional tool for teachers and then as a reference for all students, particularly English learners and students with disabilities.

Hi - Two suggestions: Highlighting/saving to vocab doesn't work for me on the Write It page, but always does on the main lesson page. Secondly, if that doesn't work, reply with what computer and browser you're using, and those with the same platforms might be able to assist. Mac solutions differ from PC solutions, for example.

I don't use this feature, but I do occasionally copy and paste phrases to my own files, so I have noticed the pop-up for saving to vocab. It's occurred to me that when I swipe right to left to highlight, the pop-up doesn't always appear, but I believe that it always does when I sweep left to right. Of course, highlight/copy and paste will work in either case. Also, I don't think the pop-up appears within the context of the exercises - only the lesson, itself. I wouldn't swear to any of this, though.

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.

Using Simple Vocab, you enter the controlled vocabulary for an element as a list in the Configuration setting; when editing an item, this list appears as a drop-down menu replacing the usual text entry box for the element. From the Simple Vocab tab, you can view entered data for specific elements, in order to easily incorporate them into the list.

There are many situations where it would be useful to be able topublishmulti-dimensional data, such as statistics, on the web in such a waythat it can be linked to related data sets and concepts. The Data Cubevocabulary provides a means to do this using the W3C RDF(Resource Description Framework) standard. The model underpinning theData Cube vocabulary iscompatible with the cube model that underlies SDMX (Statistical Dataand Metadata eXchange), an ISO standard for exchanging and sharingstatistical data and metadata among organizations. The Data Cubevocabulary is a core foundation which supports extensionvocabularies to enable publication of other aspects ofstatistical data flows or other multi-dimensional data sets.

At the heart of a statistical dataset is a set of observed valuesorganized along a group of dimensions, together with associated metadata.The Data Cube vocabulary enables such information to be representedusing the W3C RDF(Resource Description Framework) standard and published following theprinciples oflinked data.The vocabulary is based upon the approach used by the SDMX ISO standardfor statistical data exchange. This cube model is verygeneral and so the Data Cube vocabulary can be used for other data setssuch as survey data, spreadsheets and OLAP data cubes [OLAP].

The Data Cube vocabulary is focused purely on thepublication of multi-dimensional data on the web. We envisage a series of modularvocabularies being developed which extend this core foundation. Inparticular, we see the need for an SDMX extension vocabulary to support thepublication of additional context to statistical data (such as the encompassing DataFlows and associated Provision Agreements). Other extensions are possible tosupport metadata for surveys (so called "micro-data", as encompassed by DDI)or publication of statistical reference metadata.

A key component of the SDMX standards package arethe Content-Oriented Guidelines (COGs), a set ofcross-domain concepts, code lists, and categories that supportinteroperability and comparability between datasets by providing ashared terminology between SDMX implementers [COG]. RDF versions of theseterms are available separately for use along with the Data Cubevocabulary, see Content oriented guidelines for further details. These external resources do not form a normative part of the Data Cube Vocabulary specification.

This document describes the Data Cube vocabularyIt is aimed at people wishing to publishstatistical or other multi-dimension data in RDF.Mechanics of cross-format translation from otherformats such as SDMX-ML are not covered here.

In statistical applications it is common to work withslices in which a single dimension is left unspecified. In particular,to refer to such slices in which the single free dimension is time as TimeSeries and to refer slices along non-time dimensions as Sections.Within the Data Cube vocabulary we allow arbitrary dimensionalityslices and do not give different names to particular types of slice. Such sub-classes of slice could be added in extension vocabularies.

In order to illustrate the use of the data cube vocabulary we willuse a small demonstrationdata set extracted fromStatsWales reportnumber 003311 which describes life expectancy broken down by region(unitary authority), age and time. The extract we will use is:

The Data Cube vocabulary represents the dimensions, attributes and measures as RDF properties. Each is an instance of the abstract qb:ComponentProperty class, which in turn has sub-classes qb:DimensionProperty, qb:AttributeProperty and qb:MeasureProperty.

To support this reuse of general statistical concepts the data cube vocabulary provides the qb:concept property which links a qb:ComponentProperty to the concept it represents. We use the SKOS vocabulary [SKOS-PRIMER] to represent such concepts. This is very natural for those cases where the concepts are already maintained as a controlled term list or thesaurus. When developing a data structure definition for an informal data set there may not be an appropriate concept already. In those cases, if the concept is likely to be reused in other guises it is recommended to publish a skos:Concept along with the specific qb:ComponentProperty. However, if such reuse is not expected then it is not required to do so - the qb:concept link is optional and a simple instance of the appropriate subclass of qb:ComponentProperty is sufficient.

Note that in any SDMX extension vocabulary there would be one further item of information to encode about components - the role that they play within the structure definition. In particular, it is sometimes convenient for consumers to be able to easily identify which is the time dimension, which component is the primary measure and so forth. It turns out that such roles are intrinsic to the concepts and so this information can be encoded by providing subclasses of skos:Concept for each role. The particular choice of roles here is specific to the SDMX standard and so is not included within the core Data Cube vocabulary.

In the case of our running example the dimensions can be usefully ordered. There is only one attribute, the unit measure, and this is required. In the interest of illustrating the vocabulary use we will declare that this attribute will be attached at the level of the data set, however normalized representations are in general easier to query and combine.

This approach restricts observations to having a single measured value but allows a data set to carry multiple measures by adding an extra dimension, a measure dimension. The value of the measure dimension denotes which particular measure is being conveyed by the observation. This is the representation approach used within SDMX and an extension vocabulary could introduce a sub-class of qb:DataStructureDefinition which enforces such a single-measure restriction.

To use this representation you declare an additional dimension within the data structure definition to play the role of the measure dimension. For use within the Data Cube vocabulary we provide a single distinguished component for this purpose -- qb:measureType. An extension vocabulary could generalize this through the provision of roles to identify concepts which act as measure types, enabling other measure dimensions to be declared. 17dc91bb1f