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Natural Language Processing
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Introduction

Computational Linguistics

AKA, Natural Language Processing (NLP), Language Engineering, ...
Domain: The set of problems involving the interpretation and generation of human language text and speech
Poperties:
  • As with applied science: the proof is in the pudding
  • Sometimes at odds with theoretical linguistics
    • Need not model human abilities and human methods
    • Need not correspond to published linguistic theories
    • Sometimes draws on linguistic theories and/or studies of human processing
  • Broad and changing domain influenced by available funding

Computational Linguistics Applications

  • Machine translation: Methods are not at all based on how humans translate; Effective for gisting text, generating 1st draft translations, but not for high-level translation; Works better for “controlled languages” – technical manuals (Microsoft, Catterpiller, etc.); Systran; Google;
  • Spoken Language: dictation (IBM ViaVoice, Dragon Naturally Speaking), Telephone-based customer support (phone mazes)
  • Information Retrieval: Finding documents based on a query, e.g., Web Searches
  • Information Extraction: Dealtime, Google Products, Monster.com (job search); Some open source tools: OpenNLP, AliasI; Tools on NYU website include: NYU NLP Products, NYU Grishman Jet, NYU NLP ICE, NYU NLP Termolator.
  • Question Answering: ask.com, Wolfram Alpha, MIT start: http://start.csail.mit.edu/
  • Summarization: http://textsummarization.net/text-summarizer
  • Spelling/Grammar Checking, etc. https://languagetool.org/
  • Other NLP demos: https://towardsdatascience.com/the-best-nlp-tools-of-early-2020-live-demos-b6f507b17b0a

Lowest Level Syntactic Processing (text)

  • Tokenization and Segmentation: Given a sentence, determine the words or word-like units that it consists of. NLTK command: nltk.word_tokenize('this is a sentence')
  • Part of Speech Tagging (modified PTB): Apply a set of part of speech tags to a set of tokens. NLTK command: nltk.pos_tag(tokens)
  • Named Entity Tagging (with a little semantics): Mark boundaries of names of type PERSON, ORGANIZATION, FACILITY, GPE, LOCATION, … NLTK command: nltk.chunk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(test_sentence)
  • Chunking: mark verb groups and/or noun groups, convenient approximations of syntactic units. NLTK see following lines of code.
sentence = 'The book with the blue cover will end up on the shelf.'
chunks = r"""
NG: {(<DT|JJ|NN>)*(<NN|NNS>)}
VG: {<MD|VB|VBD|VBN|VBZ|VBP|VBG>*<VB|VBD|VBN|VBZ|VBP|VBG><RP>?}
"""
chunks_grammar = nltk.RegexpParser(chunks)
chunks_grammar.parse(nltk.pos_tag(nltk.word_tokenize(sentence)))

Parsing: High Level Syntactic Processing

(S (NP (DT the) (NN book)
(PP (IN with)
(NP (DT the)
(JJ blue)
(NN cover))))
(VP (VBZ is)
(PP (IN on)
(NP (DT the) (NN shelf)))))

Semantics

A wide range of topics loosely referring to "meaning".
Some Example Topics which may be part of Semantics:
  • Word Sense Disambiguation
  • Predicate Argument Structure
  • Anaphora
  • Discourse Argument Structure
  • "Semantic Parsing"

Word Sense Disambiguation

For interesting characterizations of word senses (and relation between senses), use WordNet (online or download it): wordnet.princeton.edu.
Difficult sense disambiguation: Example: senses 2, 6 and 9 for word "bank" are arguably not distinct. Lexicographers are acutely aware of the merging vs. splitting problem of enumerating senses. CL systems usually collapse some WordNet distinctions.

Predicate / Argument Structure

For thousands of years, linguists have employed systems to characterize predictable paraphrases, e.g., Pāṇini, a Sanskrit linguist from the 4rth Century BC
In 21st Century CL, semantic role labeling is popular
Semantic Role Labeling

Anaphora

Coreference
  • Though Big Blue won the contract, this official is suspicious of IBM.
  • Mary could not believe what she heard.
Other Varieties
  • John ate a sandwich and Mary ate one also. [type coref]
  • The amusement park is very dangerous. The gate has sharp edges. The rides have not been inspected for years. [Bridging Anaphora]
  • This book is valuable, but the other book is not. [Other coref]

Discourse Argument Structure

Adverbs, Subordinate/Coordinate, Conjunctions, among other words link clauses
Discourse Argument Structure

Semantic Parsing (e.g. GLARF)

Means different things to different researchers, but my version of semantic parsing is called GLARF.
One representation of the sentence that includes as much information as possible: lexical categories, predicate argument structure, discourse annotation, etc.

Example

A representation of the sentence:
  • Afterwards, she decided to perform the operation.
  • When it occurs after the sentence: The doctor ran some tests
GLARF Example

Role of Manual Annotation

Used to create, test and fine-tune task definitions/guidelines.
  • For a task to be well-defined, several annotators must agree on classification most of the time.
  • If humans cannot agree, it is unlikely that a computer can do the task at all
  • Popular, but imperfect measurement of agreement:
Kappa=Percent(Actual_Agreement)Prob(Chance_Agreement)1Prob(Chance_Agreement)Kappa={{Percent(Actual\_Agreement) - Prob(Chance\_Agreement)}\over{1 - Prob(Chance\_Agreement)}}
Used to create answer keys to score system output.
  • One set of measures are: recall, precision and f-score:
Recall=CorrectAnswer_KeyRecall={{\lvert Correct \rvert}\over{\lvert Answer\_Key \rvert}}
Precision=CorrectSystem_OutputPrecision={{\lvert Correct \rvert}\over{\lvert System\_Output \rvert}}
F_Score=112(1Precision+1Recall)=2PrecisionRecallPrecision+RecallF\_Score={1\over{{1\over2}*({1\over{Precision}}+{1\over{Recall}})}}={2{{Precision}*{Recall}\over{Precision+Recall}}}

Manual Annotation in Supervised Statistical ML

Divide the corpus into sub-corpora
  • A training corpus is used to acquire statistical patterns
  • A test corpus is used to measure system performance
  • A development corpus is similar to a test corpus
    • Systems are “tuned” to get better results on the dev corpus
    • Test corpora are only used infrequently to insure accuracy/fairness: The system should not be tuned to get better results
  • More annotated text often yield better results
  • Different genres may have different properties
    • Systems can “train” separately on different genres
    • Systems can “train” on one diverse corpus