Bioinformatics: Natural Language Processing (NLP) Notes

This is the working notes for the Natural Language Processing (NLP) that is part of the bioinformatics project.

Definition: Natural Language Processing (NLP) is the study of the computational treatment of natural (human) language, i.e., teaching computer how to understand and generate human language.
Research Resource: NLP draws on research in

Basic NLP Pipeline: Language --(Understanding)--> Computer --(Generation)--> Language
Challenges: coming from the text

current events, background events, speculation, property, reference to previous sentence
genres of text: blogs, emails, press releases, chats, debates, etc.
special types of text and terminologies
incomplete sentences
fiction text, rare words,
extremely long and complex sentences
multiple possible interpretations, ambiguous sentences
lexical, structural, scope ambiguity

CL (Computational Linguistics)- more mathematical and formal treatment of linguistics, less confusing on applications
IR (Information Retrieval)- study of finding information and documents (text, speech or video).
SP (Speech Processing)- deal with understanding and generation of spoken signals.
HLT ( Human Language Technology)- uses of the applied component of the NLP.
NLE (Natural Language Engineering)- synonym of HLT.
ML (Machine Learning)- computational, statistical study of learning.

Conference: ACL/NAACL, EMNLP, SIGIR, AAAI/IJCAI, Coling, HLT, EACL/NAACL, AMTA/MT Summit, ICSLP/Eurospeech
Journals: Computational Linguistics, TACL, Natural Language Engineering, Information Retrieval, Information Processing and Management, ACM Trans. on Information Systems, ACM TALIP, ACM TSLP
ACL Anthology
ACL Anthology Network (AAN)

Similar forms does not imply similar meanings (e.g., "Beverly Hills/Sills")
Computer is not good at metaphors (e.g., "Time flies like an arrow.")
Word order is important (e.g., "The box/pen is in the pen/box.")
Not the same semantic relationship (e.g., Mary and Sue are mothers/sisters.")
Not the same order of magnitude due to semantic change (e.g., "Every American has a mother/president.")
Draw on common sense (e.g., "We gave monkeys bananas because they were hungry/over-ripe.")
Syntactic correctness does not guarantee semantic correctness (e.g., "Colorless green ideas sleep furiously.")
Ambiguous words due to multiple meanings (e.g., "ball"), part of speech (e.g., "fly"), different pronunciations (e.g., "address"), noun-noun phrases (e.g., "Science fiction writer"), etc.

Morphological- "Joe is quite impossible/important."
Phonetic- "Joe's finger got number."
Part of Speech- "Joe won the first round."
Syntactic- "Call Joe a taxi."
Pp Attachment- "Joe ate pizza with a for/with meatballs/with Sam/with pleasure."
Sense- "Joe took the bar exam."
Modality- "Joe may win the lottery."
Subjectivity- "Joe believes the stocks will rise."
Cc Attachment- "Joe likes ripe apples and pears."
Negation- "Joe likes his pizza with no cheese and tomatoes."
Referential- "Joe yelled at Mike. He had broken the bike." "Joe yelled at Mike. He was angry at him."
Reflexive- "John brought him/himself a present."
Ellipsis and Parallelism- "Joe gave Mike a beer and James a glass of wine."
Metonymy- "Boston called and left a message for Joe."

Non-standard- "+1-212-772-1220", "A360"
Slang- "friend (verb)", "spam"
Novel words and usages- "yolo", "selfie"
Inconsistencies- "Junior college" vs. "college junior"
Typos and grammatical errors
Parsing problems- "cup holder", "Federal Reserve Board Chairman"
Complex sentences
Counterfactual sentences- " if you were to do..."
Humor and sarcasm
Implicature/inference/world knowledge- "I was late because my car broken down." implies I have a car, I used the car to get to places, etc.
Semantics vs. pragmatics- "Do you know the time?"
Language is even hard for human (both L1 and L2).

Bioinformatics