Ways of creating new words in English

Word formation processes: Ways of creating new words in English

1. Affixation:  adding a derivational affix to a word. Examples: abuser, refusal, untie, inspection, pre-cook.
2. Compounding: joining two or more words into one new word. Examples: skateboard, whitewash, cat lover, self-help, red-hot, etc.
3. Zero derivation: (also called conversion or functional shift): Adding no affixes; simply using a word of one category as a word of another category. Examples: Noun-verb: comb, sand, knife, butter, referee, proposition.
4. Stress shift: no affix is added to the base, but the stress is shifted from one syllable to the other. With the stress shift comes a change in category.

Noun            Verb
cómbine      combíne
ímplant         implánt
réwrite          rewríte
tránsport      transpórt

Noun              Adjective
cóncrete        concréte
ábstract         abstráct
 
5. Clipping: shortening of a polysyllabic word. Examples: bro (< brother), pro (< professional), prof (< professor), math (< mathematics), veg (< 'vegetate', as in veg out in front of the TV),  sub (< substitute or submarine).
6. Acronym formation: forming words from the initials of a group of words that designate one concept. Usually, but not always, capitalized. An acronym is pronounced as a word if the consonants and vowels line up in such a way as to make this possible, otherwise it is pronounced as a string of letter names. Examples: NASA (National Aeronautics and Space Administration), NATO (North Atlantic Treaty Organization), AIDS (Acquired Immune Deficiency Syndrome), scuba (self-contained underwater breathing apparatus), radar (radio detecting and ranging), NFL (National Football League), AFL-CIO (American Federation of Labor-Congress of Industrial Organizations).
7. Blending: Parts (which are not morphemes!) of two already-existing words are put together to form a new word. Examples: motel (motor hotel) brunch (breakfast & lunch), smog (smoke & fog), telethon (television & marathon), modem (modulator & demodulator), Spanglish (Spanish & English).
8. Backformation: A suffix identifiable from other words is cut off of a base which has previously not been a word; that base then is used as a root, and becomes a word through widespread use. Examples: pronunciate (< pronunciation < pronounce), resurrect (< resurrection), enthuse (< enthusiasm), self-destruct (< self-destruction < destroy), burgle (< burglar), attrit (< attrition), burger (< hamburger). This differs from clipping in that, in clipping, some phonological part of the word which is not interpretable as an affix or word is cut off (e.g. the '-essor' of 'professor' is not a suffix or word; nor is the '-ther' of 'brother'. In backformation, the bit chopped off is a recognizable affix or word ('ham ' in 'hamburger'), '-ion' in 'self-destruction'. Backformation is the result of a false but plausible morphological analysis of the word; clipping is a strictly phonological process that is used to make the word shorter. Clipping is based on syllable structure, not morphological analysis. It is impossible for you to recognize backformed words or come up with examples from your own knowledge of English, unless you already know the history of the word. Most people do not know the history of the words they know; this is normal.
9. Adoption of brand names as common words: a brand name becomes the name for the item or process associated with the brand name. The word ceases to be capitalized and acts as a normal verb/noun (i.e. takes inflections such as plural or past tense). The companies using the names usually have copyrighted them and object to their use in public documents, so they should be avoided in formal writing (or a lawsuit could follow!) Examples: xerox, kleenex, band-aid, kitty litter.
10. Onomatopoeia (pronounced: 'onno-motto-pay-uh'): words are invented which (to native speakers at least) sound like the sound they name or the entity which produces the sound. Examples: hiss, sizzle, cuckoo, cock-a-doodle-doo, buzz, beep, ding-dong.
11. Borrowing: a word is taken from another language. It may be adapted to the borrowing language's phonological system to varying degrees. Examples: skunk, tomato (from indigenous languages of the Americas), sushi, taboo, wok (from Pacific Rim languages), chic, shmuck, macho, spaghetti, dirndl, psychology, telephone, physician, education (from European languages), hummus, chutzpah, cipher, artichoke (from Semitic languages), yam, tote, banana (from African languages).

Basic Notions of Semantics

              A perennial problem in semantics is the delineation of its subject matter. The term meaning can be used in a variety of ways, and only some of these correspond to the usual understanding of the scope of linguistic or computational semantics. We shall take the scope of semantics to be restricted to the literal interpretations of sentences in a context, ignoring phenomena like irony, metaphor, or conversational implicature .

               A standard assumption in computationally oriented semantics is that knowledge of the meaning of a sentence can be equated with knowledge of its truth conditions: that is, knowledge of what the world would be like if the sentence were true. This is not the same as knowing whether a sentence is true, which is (usually) an empirical matter, but knowledge of truth conditions is a prerequisite for such verification to be possible. Meaning as truth conditions needs to be generalized somewhat for the case of imperatives or questions, but is a common ground among all contemporary theories, in one form or another, and has an extensive philosophical justification, e.g

                        A semantic description of a language is some finitely stated mechanism that allows us to say, for each sentence of the language, what its truth conditions are. Just as for grammatical description, a semantic theory will characterize complex and novel sentences on the basis of their constituents: their meanings, and the manner in which they are put together. The basic constituents will ultimately be the meanings of words and morphemes. The modes of combination of constituents are largely determined by the syntactic structure of the language. In general, to each syntactic rule combining some sequence of child constituents into a parent constituent, there will correspond some semantic operation combining the meanings of the children to produce the meaning of the parent.

Practical Applications of Semantics

                      Some natural language processing tasks (e.g., message routing, textual information retrieval, translation) can be carried out quite well using statistical or pattern matching techniques that do not involve semantics in the sense assumed above. However, performance on some of these tasks improves if semantic processing is involved. (Not enough progress has been made to see whether this is true for all of the tasks).

Some tasks, however, cannot be carried out at all without semantic processing of some form. One important example application is that of database query, of the type chosen for the Air Travel Information Service (ATIS) task [DAR89]. For example, if a user asks, ``Does every flight from London to San Francisco stop over in Reykyavik?'' then the system needs to be able to deal with some simple semantic facts. Relational databases do not store propositions of the form every X has property P and so a logical inference from the meaning of the sentence is required. In this case, every X has property P is equivalent to there is no X that does not have property P and a system that knows this will also therefore know that the answer to the question is no if a non-stopping flight is found and yes otherwise.

               Any kind of generation of natural language output (e.g., summaries of financial data, traces of KBS system operations) usually requires semantic processing. Generation requires the construction of an appropriate meaning representation, and then the production of a sentence or sequence of sentences which express the same content in a way that is natural for a reader to comprehend, e.g., [MKS94]. To illustrate, if a database lists a 10 a.m.\ flight from London to Warsaw on the 1st--14th, and 16th--30th of November, then it is more helpful to answer the question What days does that flight go? by Every day except the 15th instead of a list of 30 days of the month. But to do this the system needs to know that the semantic representations of the two propositions are equivalent.