Linguistic Semantic Analysis Task

A Linguistic Semantic Analysis Task is a semantic analysis task that is a linguistic analysis task (of the concepts and semantic relations mentioned within a linguistic artifact to extract complex meanings).

• AKA: Semantic Indexing Task, Semantic Analysis Task, Linguistic Meaning Analysis Task.
• Context:
  • Input: One or more Linguistic Artifacts, such as a corpus, sentence, or text document.
  • Output: A set of concepts and semantic relations between them, often as structured representations.
    • Optionally, references to concept mentions and semantic relation mentions in the source text.
    • Optionally, a semantic theory for a natural language or language fragment.
  • It can typically extract semantic content from linguistic expressions using semantic analysis algorithms.
  • It can typically identify semantic properties such as reference, predication, and modification.
  • It can typically represent meaning structure through formal notations or computational models.
  • ...
  • It can often require the mapping of concepts to an ontology (See: normalization task).
  • It can often involve lexical hierarchy analysis including hyponymy, hypernymy, meronymy, and polysemy.
  • It can often address semantic ambiguity through context-sensitive interpretation.
  • It can often detect collocation patterns and idioms with non-compositional meanings.
  • ...
  • It can range from being a Word-level Semantic Analysis Task to being a Discourse-level Semantic Analysis Task, depending on its linguistic scope.
  • It can range from being a Shallow Semantic Analysis Task to being a Deep Semantic Analysis Task, depending on its analysis depth.
  • It can range from being a Lexical Semantic Analysis Task to being a Compositional Semantic Analysis Task, depending on its semantic focus.
  • It can range from being a Structural Semantic Analysis Task to being a Interpretive Semantic Analysis Task, depending on its representation goal.
  • ...
  • It can be implemented by semantic analysis systems that apply semantic analysis algorithms to linguistic data.
  • It can serve practical applications including information retrieval, question answering, and knowledge discovery.
  • It can contribute to language understanding technology for natural language processing systems.
  • ...
• Examples:
  • Linguistic Semantic Analysis Task Scope Categories, such as:
    • Word-level Semantic Analysis Tasks, such as:
      • Word Sense Disambiguation Task for lexical ambiguity resolution.
      • Semantic Role Labeling Task for predicate-argument identification.
      • Lexical Semantic Analysis Task for word meaning extraction.
    • Phrase-level Semantic Analysis Tasks, such as:
      • Compositional Semantics Task for phrase meaning construction.
      • Multiword Expression Analysis Task for non-compositional phrase identification.
      • Semantic Collocation Analysis Task for semantic restriction detection.
    • Sentence-level Semantic Analysis Tasks, such as:
      • Semantic Parsing Task for logical form extraction.
      • Propositional Content Analysis Task for truth-conditional content identification.
      • Semantic Dependency Analysis Task for meaning relation extraction.
    • Discourse-level Semantic Analysis Tasks, such as:
      • Coreference Resolution Task for entity reference tracking.
      • Discourse Relation Analysis Task for semantic coherence modeling.
      • Textual Entailment Task for inference detection.
  • Linguistic Semantic Analysis Method Categories, such as:
    • Shallow Semantic Analysis Tasks, such as:
      • Named Entity Recognition Task for entity mention detection.
      • Semantic Frame Analysis Task for event structure identification.
      • Topic Modeling Task for thematic content extraction.
    • Statistical Semantic Analysis Tasks, such as:
      • Latent Semantic Analysis Task for distributional meaning modeling.
      • Supervised Semantic Indexing Task for semantic similarity computation.
      • Neural Semantic Indexing Task for semantic embedding learning.
    • Deep Semantic Analysis Tasks, such as:
      • Logical Form Extraction Task for formal meaning representation.
      • Knowledge Graph Construction Task for semantic network building.
      • Semantic Annotation Task for ontological concept mapping.
  • ...
• Counter-Examples:
  • Image Semantic Analysis Task, which processes visual content rather than linguistic content.
  • Linguistic Syntactic Analysis Task, which focuses on grammatical structure rather than meaning.
  • Linguistic Pragmatics Analysis Task, which addresses intended meaning in context rather than literal content.
  • Linguistic Stylistic Analysis Task, which examines expressive features rather than semantic content.
  • Linguistic Discourse Analysis Task, which studies communication patterns rather than meaning representation.
• See: Linguistic Semantic Analysis Benchmark Task, Semantic Parsing, Semantic Class, Information Extraction, Latent Semantic Analysis, Shallow Semantic Analysis, Meaning (Linguistics), Idiom, Figure of Speech, Knowledge Engineering Task, Semantic Web, Semantic Network.

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References

2021

• (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Semantic_analysis_(linguistics) Retrieved:2021-5-23.
  • In linguistics, semantic analysis is the process of relating syntactic structures, from the levels of phrases, clauses, sentences and paragraphs to the level of the writing as a whole, to their language-independent meanings. It also involves removing features specific to particular linguistic and cultural contexts, to the extent that such a project is possible. The elements of idiom and figurative speech, being cultural, are often also converted into relatively invariant meanings in semantic analysis. Semantics, although related to pragmatics, is distinct in that the former deals with word or sentence choice in any given context, while pragmatics considers the unique or particular meaning derived from context or tone. To reiterate in different terms, semantics is about universally coded meaning, and pragmatics, the meaning encoded in words that is then interpreted by an audience. Semantic analysis can begin with the relationship between individual words. This requires an understanding of lexical hierarchy, including hyponymy and hypernymy, meronomy, polysemy, synonyms, antonyms, and homonyms. It also relates to concepts like connotation (semiotics) and collocation, which is the particular combination of words that can be or frequently are surrounding a single word. This can include idioms, metaphor, and simile, like, "white as a ghost." With the availability of enough material to analyze, semantic analysis can be used to catalog and trace the style of writing of specific authors.

2014

• (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Semantic_analysis_(computational) Retrieved:2014-11-3.
  • Semantic Analysis (computational) is a composite of the “Semantic Analysis” and the "Computational" components.

    "Semantic Analysis" refers to a formal analysis of meaning, and "computational" refer to approaches that in principle support effective implementation.

2014

• (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Semantic_analysis_(linguistics) Retrieved:2014-11-3.
  • In linguistics, semantic analysis is the process of relating syntactic structures, from the levels of phrases, clauses, sentences and paragraphs to the level of the writing as a whole, to their language-independent meanings. It also involves removing features specific to particular linguistic and cultural contexts, to the extent that such a project is possible. The elements of idiom and figurative speech, being cultural, are often also converted into relatively invariant meanings in semantic analysis.

    With the availability of enough material to analyze, semantic analysis can be used to catalog and trace the style of writing of specific authors.

2009

• http://www.bambooweb.com/articles/s/e/Semantic_analysis.html
  • In linguistics, semantic analysis is the process of unpacking clause, sentence and paragraph structure, and even the structure of the work as a whole, to remove features specific to the language in which it is written and also the culture in which it was intended to be read. Figures of speech, being cultural, must also be eliminated.
• http://www.cs.mu.oz.au/~jingy/download/conversion_report.pdf
  • Semantic analysis is the process of transforming the surface structure ofnatural language inputs into the inputs'deep structure in an unambiguous formal or semi-formal representation.

2005

• (Copestake et al., 2005) ⇒ Ann Copestake, Dan Flickinger, Carl Pollard, and Ivan A. Sag. (2005). “Minimal Recursion Semantics: an Introduction.” In: Research on Language and Computation 3(4).

2003

• 2003_TheKernelTextUnderstandingSystem.
  • “The lexical semantic structure of a verb is represented in the style of Jackendovian Lexical Conceptual Structures" … "The result of the semantic analysis is a set of partially instantiated semantic predicates similar to a frame representation, a representational device typical of much work in lexical semantics."

2000

• (Jurafsky & Martin, 2000) ⇒ Daniel Jurafsky, James H. Martin. (2000). “Speech and Language Processing.” Prentice Hall.

1993

• (Riloff, 1993) ⇒ Ellen Riloff. (1993). “Learning of Lexico-Semantic Patterns.

1981

• (Palmer, 1981) ⇒ Martha Palmer. (1981). “A case for rule-driven semantic analysis.” In: Proceedings of the 19th Annual Meeting of the Association for Computational Linguistics. 1981
  • semantic analysis, is the process of extracting the information from a set of narrative descriptions for modeling the real world.

1974

• (Montague, 1974) ⇒ Richard Montague. (1974). “Formal Philosophy.” In: Selected Papers of Richard Montague, edited by Richmond H. Thomason. Yale University Press

1970

• (Montague, 1970) ⇒ Richard Montague. (1970). “Universal Grammar.” In: Theoria 36: 373±98.

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