Human Language

• Definition: Human language is a complex system of communication used by humans, consisting of spoken and written words, along with rules for combining them.
• Components:
  1. Phonetics and Phonology: Study of speech sounds and their patterns.
  2. Morphology: Analysis of the structure and formation of words.
  3. Syntax: Rules governing sentence structure.
  4. Semantics: Meaning of words and sentences.
  5. Pragmatics: Context-dependent interpretation of language.
  6. Discourse: Study of larger units of language beyond the sentence.
• Challenges in NLP:
  1. Ambiguity: Words or phrases with multiple meanings.
  2. Context Dependency: Interpretation depends on the surrounding context.
  3. Variability: Diverse ways people express the same idea.
• NLP Applications:
  1. Machine Translation: Translating text from one language to another.
  2. Speech Recognition: Converting spoken language into written text.
  3. Sentiment Analysis: Determining the sentiment expressed in text.
  4. Question Answering: Developing systems to answer questions posed in natural language.
  5. Text Generation: Creating human-like text using AI models.
• Importance in AI: Understanding and processing human language is crucial for developing intelligent systems that can interact effectively with users.
• NLP and Deep Learning: Integration of deep learning techniques enhances the ability to model complex language patterns and semantics.
• Ongoing Challenges: Continuous exploration of new techniques to improve language understanding, generation, and reasoning in AI systems.

Meaning

Meaning in NLP refers to the interpretation and understanding of words, phrases, and sentences in a way that captures the intended sense or information conveyed by human language.

WordNet

To demonstrate how to extract usable meaning in a computer using WordNet from the NLTK (Natural Language Toolkit) library in Python, we can focus on synonyms and hypernyms. WordNet is a lexical database of the English language that relates words to one another in terms of synonyms, hypernyms (more abstract terms), hyponyms (more specific terms), etc.

from nltk.corpus import wordnet as wn
poses = {
    "n": "noun",
    "v": "verb",
    "s": "adj (s)",
    "a": "adj",
    "r": "adv"
}

for synset in wn.synsets('good'):
  print(f'{poses[synset.pos()]}: {",".join([l.name() for l in synset.lemmas()])}')noun: good

noun: good,goodness
noun: good,goodness
noun: commodity,trade_good,good
adj: good
adj (s): full,good
adj: good
adj (s): estimable,good,honorable,respectable
adj (s): beneficial,goo
adj (s): effective,good,in_effect,in_force
adv: well,good
adv: thoroughly,soundly

from nltk.corpus import wordnet as wn

panda = wn.synset('panda.n.01')
hyper = lambda s: s.hypernyms()
hs = list(panda.closure(hyper))
for h in hs:
  print(h)

Synset('procyonid.n.01')
Synset('carnivore.n.01')
Synset('placental.n.01')
ynset('mammal.n.01')
Synset('vertebrate.n.01')
Synset('chordate.n.01')
Synset('animal.n.01')
Synset('organism.n.01')
Synset('living_thing.n.01')
Synset('whole.n.02')
Synset('object.n.01')
Synset('physical_entity.n.01')
Synset('entity.n.01')

Problems with wordnet

1. Limited Coverage: Primarily focused on English, lacks coverage for domain-specific or new terms.
2. Lack of Multilingual Support: Limited support for languages other than English.
3. Not Suitable for All NLP Tasks: Limited for tasks requiring deep semantic understanding or handling polysemy.

Researchers often complement WordNet with dynamic embeddings or leverage more advanced techniques for improved performance.

Representing Words as Discrete Symbols:

Examples: