Frequently Asked Questions

  • Do SAL codes really make a difference in translation?

    SAL codes are used primarily for analysis of the source sentence. If that analysis is incorrect, target translation will obviously suffer. For examples of how SAL codes affect analysis see How is SAL used.

    For examples of how poor SAL codes adversely affect translation, see here. (to be developed).

  • Are some SAL codes more critical than others?

Concrete Noun Superset

  • What is the difference between “agents” and “functionals”?

    Functional things tend to be passive, i.e. typically do not act of their own accord and generally require an agent to use them. Hence they are more instrumental in nature. Agents typically do work in and of themselves. See CONCRETE Count Noun Superset [mnemonic: CO] [3].

  • How can we distinguish between “concrete nouns” and “mass nouns”?

    Mass nouns, unlike count nouns, occur in the singular after quantifiers like any, enough, little, some, more, etc; e.g., some wine, enough sand, more gold. Unlike mass nouns, count nouns following one of these quantifiers require a plural form. See MASS Noun Superset [mnemonic: MA] [11].

  • Should company names like Microsoft be coded “product name” under “Concrete Superset,” or as “human collective” under the “Animate Superset”?

    Things that are brand names or product names should be coded with as “product/brand names”. This is a very restricted set. See product/brand names [mnemonic: CObrand] [3 34 209].

  • How is the category “concretizations of Mass noun” used?

    A noun phrase the first word of which is a mass noun but which does not function as a mass noun because of the head word which is not mass. See concretization of mass nouns [mnemonic: COmass] [3 34 210].

Mass Noun Superset

  • What is the difference between “concrete nouns” and “mass nouns”?

    Mass nouns, unlike count nouns, occur in the singular after quantifiers like any, enough, little, some, more, etc; e.g., some wine, enough sand, more gold. Unlike mass nouns, count nouns following one of these quantifiers require a plural form. See MASS Noun Superset [mnemonic: MA] [11].

Animate Superset

  • Are “proper product names” like Microsoft to be coded as “human collective” or as “concrete product names”?

    The proper organization names set should be used. See proper organization names [mnemonic: ANorg] [5 94 193].

  • Why is language included in the human “people/language” set?

    This is because the designations of some languages simultaneously denote the people who speak them. Examples: Albanian, Armenian, Bulgarian, Chinese, English, German, Japanese, Macedonian, Norwegian, Russian, Spanish. On the other hand, words like British, Canadian, Argentinean, European do not describe languages and should be coded people/place under professions/designations. See people/language [mnemonic: ANlang] [5 91 206].

Place Superset

  • What is the principle for assigning the “remote agentive” code?

    This code may overlay the subset of any noun which is NOT already a member of some subset and which functions as a strong agentive, e.g. business office. See remote agentive subset [mnemonic: REagen] [228]

Information Superset

  • What is the difference between “instructional data” and “recorded data”?

    The instructional data set includes nouns that designate policy, directions, orders, commands, etc. Words in this set tend to have an agentive character. On the other hand, terms like web page, web site should be coded as recorded data. See instructional/legal [mnemonic: INinst] [12 74].

Abstract Superset

  • What are “strong verbals”?

    Strong verbals are a broad class of nouns or noun phrases that denote an action, process, behavior or result of same. See strong verbals: actions/processes/behavior or the result of same [mnemonic: ABstrvb] [6 41 749].

  • How can we determine which preposition governs nouns in the “strong verbal” subsets?

  • What is the principal difference between “verbal” set and the “non-verbal” abstract noun set?

    Verbal abstracts describes procedures, behavior, actions and persons/things as agents. Non-verbal abstracts describes qualities, conditions, and relationships of things or persons as non-agents (patients). This set also includes concepts relating to classifications of things/persons and source or origin of things/persons. See ABSTRACT Noun Superset [mnemonic: AB ] [6]

  • Can you clarify when the governance of the preposition “of” is correctly assigned and incorrectly assigned in the case of “strong verbals”?

    No governance code is assigned for of because, with rare exceptions, an of after a strong verbal is always genitival, introducing either the object or the subject of the strong verbal and NOT a true complement of the verb. E.g., removal of waste, transfer of goods, etc. See Strong Verbals (with Prepositional Governance) [ABstrv].

  • How do “strong verbals” differ from “time events”?

    Time events comprise abstract nouns that denote events or happenings in time. Words like miracle, phenomenon do not satisfy the fixed/focused time conditions and should be coded as strong verbals. See time events [mnemonic: ABtime] [6 41 732].

Aspective Superset

  • When it is appropriate to use the “aspective” code and when it is not?

    The ASPECTIVE Noun Superset includes words that are aspects of something else; for example, piece, set or layer. ASPECTIVE Nouns are easy to recognize because they invite a second noun to follow them, usually preceded by “of” in English. For example, a piece of something; a set of something; a layer of something. See ASPECTIVE Noun Superset [mnemonic: AS] [2].

Miscellaneous Questions

  • Can you explain the “apposition-inviting” code?

    This code may overlay the subset of any noun which is NOT already a member of some subset and which invites complementation. See apposition-inviting subset [mnemonic: APinvit] [986].

  • Can you explain the “mass-tending” code?

    This code may overlay the subset of any noun which is not coded a mass noun. It signifies that a noun so coded may sometimes act like a MASS Noun. Examples of this are words such as space or room. See remote mass subset [mnemonic: REmass] [855].

Errors in translation when words are miscoded