Morphologically, languages are characterized along two dimensions. 
- The first dimension is the number of morphemes per word. For example, isolation languages (Vietnamese and Cantonese) where each word has one morpheme, and polysynthetic languages ( Siberian Yupik aka Eskimo) where a word can correspond to an entire English sentence. 
- The second dimension is the degree in which morphemes are segmentable. For example, agglutinative languages (Turkish) where morphemes have clear boundaries, and fusion languages (Russian) where a single affix may conflate multiple morphemes.

Morphological Topology

Knowledge of the meaningful components of words; more specifically, the way words break down into components that carry meanings like singular vs. plural, I'm, can't, etc.

University of Michigan - Ann Arbor

- Information extraction
- Machine translation
- Discourse, dialogue and pragmatics
- Natural language generation
- Speech recognition
- Lexical semantics
- Phonology / morphology
- Language resources

Natural language processing in ACM Computing Classification

An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
https://www.cs.colorado.edu/~martin/slp2.html

Speech and Language Processing (2nd edition)

NLG, a subfield of artificial intelligence (AI), is a software process that automatically transforms data into plain-English content.

Natural language generation

Speech recognition

Phonology / Phonetics

Lexical semantics

Knowledge about linguistic units larger than a single utterance

Discourse

Knowledge of the relationship of meaning to the goals and intentions of the speaker.

Pragmatics

Information extraction

Because the space of possible target sentences is extremely large, a translation system often assigns scores to candidate outputs and uses a search procedure to find a strong result. If the final translation is poor, optimization verification can help determine whether the problem came from search failing to find the best-scoring candidate or from the scoring function itself ranking the wrong candidates too highly.

Learn Before

Related

Learn After

Learn After

Related