Semantics and Context in Natural Language Processing (NLP) Ari Rappoport The Hebrew University

Form vs. Meaning

• • • • Solid NLP progress with statistical learning OK for classification, search, prediction – Prediction: language models in speech to text But all this is just form (lexicon, syntax) Language is for expressing meanings (semantics) – (i) Lexical, (ii) sentence, (iii) interaction

(i) Lexical Semantics

• • • • Relatively context independent Sensory feature conjunction: “house”, “hungry”, “guitar” – Non-linguistic “semantic” machine learning: face identification in photographs Categories: is-a: “a smartphone is a (type of) product”, “iPhone is a (type of) smartphone” Configurations: part-of: “engine:car”, places

Generic Relationships

• • Medicine : Illness – Hunger : Thirst – Love : Treason – Law : Anarchy – Stimulant : Sensitivity “You use X in a way W, to do V to some Z, at a time T and a place P, for the purpose S, because of B, causing C, …”

Flexible Patterns

• • • • • • X w v Y : “countries such as France” – Davidov & Rappoport (ACL, EMNLP, COLING, etc) Content words, High frequency words Meta-patterns: CHHC, CHCHC, HHC, etc. Fully unsupervised, general Efficient hardware filtering, clustering Categories, SAT exams, geographic maps, numerical questions, etc.

Ambiguity

• • • • • Relative context independence does not solve ambiguity Apple: fruit, company Madrid: Spain, New Mexico Which one is more relevant? Context must be taken into account – Language use is always contextual

(ii) Sentence Semantics

• • The basic meaning expressed by (all) languages: argument structure “scenes” Dynamic or static relations between participants; elaborators; connectors; embeddings: – “John kicked the red ball” – “Paul and Anne walked slowly in the park” – “She remembered John’s singing”

Several Scenes

• • • Linkers: cause, purpose, time, conditionality – “He went there to buy fruits”, “Before they arrived, the party was very quiet”, “If X then Y” Ground: referring to the speech situation – “In my opinion, machine learning is the greatest development in computer science since FFT” [and neither were done by computer scientists] “Career”, “Peace”, “Democracy”

Sentence Semantics in NLP

• • • • Mostly manual: FrameNet, PropBank Unsupervised algorithms – Arg. identification, Abend & Rappoport (ACL 2010) Question Answering – Bag of words (lexical semantics) Machine Translation – Rewriting of forms (alignment, candidates, target language model)

Extreme Semantic Application

• • • Tweet Sentiment Analysis – Schwartz (Davidov, Tsur) & Rappoport 2010, 2013 Coarse semantics: 2 categories (40) Short texts, no words lists; fixed patterns

(iii) Interaction Semantics

• • • ‘Understanding’ means having enough information to DO something – The brain’s main cycle Example: human-system interaction Full context dependence – Relevance to your current situation

Interaction Examples

• • • Searching “Argo”, did you mean – The plot? Reviews? Where and/or when to watch? “Chinese restaurant” – The best in the country? In town? The nearest to you? The best deal? There are hints: – Location (regular, irregular); time (lunch?)

Interaction Directions

• • • • Extending flexible patterns: – Include Text-Action H and C items (words, actions) Action: – represented as User Interface operations Shortcut: bag of words (lexical semantics) + “current context”. Ignore sentence semantics Noise, failure (Siri, maps,…)

Summary

• • • • • Lexical, sentence, and interaction semantics Applications are possible using all levels As relevance to life grows, so do requirements from algorithms Both sentence and interaction semantics necessary for future smart applications Current focus: sentence semantics