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Related Concept Videos

Components of Language01:24

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Related Experiment Video

Updated: Jul 15, 2025

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks
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Conserving Semantic Unit Information and Simplifying Syntactic Constituents to Improve Implicit Discourse Relation

Zhongyang Fang1, Yue Cong1, Yuhan Chai1

  • 1The Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China.

Entropy (Basel, Switzerland)
|September 28, 2023
PubMed
Summary

Implicit discourse relation recognition (IDRR) is challenging due to missing connectives. New semantic unit embedding methods improve IDRR performance by preserving text meaning.

Keywords:
implicit discourse relation recognitionphrase extractionrelation extractionshallow discourse parsing

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Implicit discourse relation recognition (IDRR) is a difficult task in shallow discourse parsing.
  • The absence of explicit connectives necessitates preserving semantic completeness for accurate relation prediction.
  • Current word-level embeddings may cause semantic loss by segmenting meaningful phrases.

Purpose of the Study:

  • To propose and evaluate novel methods for segmenting sentences into complete semantic units for improved IDRR.
  • To investigate the impact of semantic unit embedding versus word-level embedding on IDRR performance.

Main Methods:

  • Developed three sentence segmentation methods: corpus-based (baseline), constituent parsing tree-based, and dependency parsing tree-based.
  • Implemented semantic unit embedding within a state-of-the-art IDRR model.
  • Compared the performance of the proposed methods against traditional word-level embeddings.

Main Results:

  • Semantic unit embedding effectively preserves sentence semantics compared to word-level embeddings.
  • The proposed segmentation and embedding methods enhance the performance of IDRR models.
  • Dependency and constituent parsing tree-based methods offer flexible and automatic sentence segmentation.

Conclusions:

  • Embedding at the semantic unit level is crucial for conserving semantic information in text.
  • The developed methods provide a more effective approach to implicit discourse relation recognition.
  • This research contributes to advancing the field of discourse parsing and natural language understanding.