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Verbal abilities tests usually measure verbal knowledge and verbal reasoning. There are many types of tests available to measure verbal abilities (e.g., synonym, antonym, analogy, reading comprehension, and sentence completion). All sub-types are not used in every test measuring cognitive abilities; two well-known examples of this are the Scholastic Aptitude Test (SAT) and the Graduate Record Examination (GRE). The selection of the types of sub-tests depends on the theory, purpose, and test-taker characteristics chosen by test developers. For example, in 1994 major changes in both content and procedures were implemented in the SAT: antonym items were eliminated from the verbal section, reading comprehension items were made more complex, and sentence completion items were added (Zwick, 2007).

There are several considerations in assessing verbal abilities using word relationship questions: the key answer should be most similar in meaning or most opposite in meaning, excerpted text should not be used when one must analyze word relationships within answer choices, only grade-level appropriate words found within the text should be assessed, and the question should contain clear and sufficient context for determining the meaning of the assessed words (Florida Department of Education, 2010). The clarity of word context differs between synonym and antonym questions, with synonym questions providing a clearer verbal context and more specificity than antonyms (Freedle & Kostin, 1990). Antonyms questions is not merely described set phrases (e.g., long and short, thick and thin) but antonym pairs co-occur across a large range of different phrases (Paradis & Willners, 2011).

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Note. Items 1-10 are synonym items, and items 11-20 are antonym. All loadings are freely estimated. A double-headed arrow on a single variable indicates its variance. For the sake of simplify, measurement error on each item is omitted. The model is identified by fixing the variance of the reference factor and the loading of one item on the non-reference factor to 1.0.

Synonym tests follow the synonymy concept; two words are said to be synonyms if one can be used in a statement in place of the other without changing the meaning of the statement (Jarmasz & Szpakowicz, 2003). There are many strategies at how to choose the best answer, the most common being to interpret the distance between the nodes that correspond to the items being compared: the shorter the path, the more similar the items (Resnik, 1995). Our study findings were supported by the fact that synonym tests are more complicated than antonym tests, as finding an opposite meaning requires strong reasoning skills as well as synonym test. This was why separating the synonym and antonym tests produced the model with the best fit. Synonym items are particularly easy to answer because they are modeled by preference materials of all kinds, in which a main word is paired with the words similar to it. However, the relationships underlying antonym items are quite a bit more varied, and are not systematically compiled (Turney, Littman, Bigham, & Shnayder, 2003).

The strategies for handling antonym questions are quite different than those for handling synonym questions, an opposite meaning can be found in several ways. The usual way is to instantly locate the opposite meaning directly, based on colloquial language. However, this method is hampered by the presence of distractor items in the possible responses, which are deliberately chosen to catch test-takers using this strategy. This example illustrates how the procedures needed to answer antonym-type questions require different cognitive effort than synonym-type questions. Antonym question have different patterns than synonym questions. These patterns can be understood by people who have large vocabularies, good reasoning skills, and the ability to connect these two skills. Test-takers then have to think of synonyms of quick, such as fast, and then find the antonym of that secondary word among the list of possible responses. When individuals are asked to articulate the relationship between the words broad and road, they might consider a number of possibilities. Many possible relationships would need to be considered, depending on the context (Turney et al., 2003), unlike for synonym questions. Antonym items have limited verbal contexts (Freedle & Kostin, 1990), requiring individuals to interpret what context might be appropriate to connect the term in the question and possible answers.

To address both these issues, you can create a synonym, EmpTable in a dedicated or existing schema, RemoteObjects, on Server2 for the Employee table on Server1. Now, the client application only has to use the two-part name, RemoteObjects.EmpTable, to reference the Employee table Server1. Also, if the location of the Employee table changes, you will have to modify the synonym, EmpTable, to point to the new location of the Employee table. Because there is no ALTER SYNONYM statement, you first have to drop the synonym, RemoteObjects.EmpTable, and then re-create the synonym with the same name, but now point the synonym to the new location of the Employee table.

The binding between a synonym and its base object is by name only. All existence, type, and permissions checking on the base object is deferred until run time. Therefore, the base object can be modified, dropped, or dropped and replaced by another object that has the same name as the original base object. For example, consider a synonym, dbo.MyContacts, that references the Person.Contact table in Adventure Works. If the Contact table is dropped and replaced by a view named Person.Contact, MyContacts now references the Person.Contact view.

References to synonyms are not schema-bound. Therefore, a synonym can be dropped at any time. However, by dropping a synonym, you run the risk of leaving dangling references to the synonym that was dropped. These references will only be found at run time.

If you have a default schema that you do not own and want to create a synonym, you must qualify the synonym name with the name of a schema that you do own. For example, if you own a schema S1, but S2 is your default schema and you use the CREATE SYNONYM statement, you must prefix the name of the synonym with the schema S1, instead of naming the synonym by using a single-part name. For more information about how to create synonyms, see CREATE SYNONYM (Transact-SQL).

When you are working with synonyms in the contexts previously stated, the base object is affected. For example, if a synonym references a base object that is a table and you insert a row into the synonym, you are actually inserting a row into the referenced table.

The sys.synonyms catalog view contains an entry for each synonym in a given database. This catalog view exposes synonym metadata such as the name of the synonym and the name of the base object. For more information, see sys.synonyms (Transact-SQL).

By using extended properties, you can add descriptive or instructional text, input masks, and formatting rules as properties of a synonym. Because the property is stored in the database, all applications that read the property can evaluate the object in the same way. For more information, see sp_addextendedproperty (Transact-SQL).

With the above request the word bar gets skipped but a mapping foo => baz is still added. However, if the mappingbeing added was foo, baz => bar nothing would get added to the synonym list. This is because the target word for themapping is itself eliminated because it was a stop word. Similarly, if the mapping was "bar, foo, baz" and expand wasset to false no mapping would get added as when expand=false the target mapping is the first word. However, ifexpand=true then the mappings added would be equivalent to foo, baz => foo, baz i.e, all mappings other than thestop word.

The tokenizer parameter controls the tokenizers that will be used totokenize the synonym, this parameter is for backwards compatibility for indices that created before 6.0.The ignore_case parameter works with tokenizer parameter only.

The synonym rules should not contain words that are removed by a filter that appears later in the chain (like a stop filter).Removing a term from a synonym rule means there will be no matching for it at query time.

Because entries in the synonym map cannot have stacked positions, some token filters may cause issues here.Token filters that produce multiple versions of a token may choose which version of the token to emit when parsing synonyms.For example, asciifolding will only produce the folded version of the token.Others, like multiplexer, word_delimiter_graph or ngram will throw an error.

If you need to build analyzers that include both multi-token filters and synonym filters, consider using the multiplexer filter, with the multi-token filters in one branch and the synonym filter in the other.

A synonym is a word that has the same meaning as another word (or nearly the same meaning). For example, beautiful and attractive are synonyms of each other because they both refer to someone or something that looks good.

Notice how the meanings are not always identical; for example, excellent is a high degree of good, while satisfactory is more like a minimal amount of good. Still, the central idea is the same: All these synonyms refer to something that is positive and not bad.

When discussing synonyms, the term antonym often comes up. While synonyms are words with the same or similar meaning, antonyms are words with opposite meanings. For example, an antonym of good is bad, while a synonym of good is fine. 006ab0faaa