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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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Chunking01:12

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Chunking is a powerful cognitive technique that improves short-term memory retention by organizing information into smaller, more manageable units. The brain, limited by working memory capacity, can more easily process and store information when it is divided into "chunks" rather than presented as discrete, unrelated elements. Chunking is especially useful when dealing with large amounts of information, such as numerical sequences, words, or complex ideas.
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Related Experiment Video

Updated: Apr 27, 2026

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks
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SkipCor: skip-mention coreference resolution using linear-chain conditional random fields.

Slavko Žitnik1, Lovro Šubelj2, Marko Bajec2

  • 1Laboratory for Data Technologies, University of Ljubljana and Optilab d.o.o., Ljubljana, Slovenia.

Plos One
|June 24, 2014
PubMed
Summary

This study introduces SkipCor, a novel coreference resolution system that treats the task as sequence labeling. SkipCor achieves state-of-the-art results with efficient training and inference.

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Area of Science:

  • Natural Language Processing
  • Information Extraction
  • Machine Learning

Background:

  • Coreference resolution identifies mentions referring to the same entity, a key task in Information Extraction.
  • Existing methods include supervised, unsupervised, pairwise, and sequence-based models.
  • There is a need for scalable and efficient coreference resolution systems.

Purpose of the Study:

  • To present SkipCor, a novel system for coreference resolution.
  • To reformulate coreference resolution as a sequence labeling task.
  • To develop a scalable and efficient system with fast training and inference.

Main Methods:

  • SkipCor utilizes linear-chain conditional random fields.
  • The system is designed for high scalability and straightforward parallelization.
  • No existing models share similarities with this sequence labeling approach.

Main Results:

  • SkipCor was evaluated on ACE 2004, CoNLL 2012, and SemEval 2010 benchmark datasets.
  • The system significantly outperforms two baseline systems with identical features.
  • Performance is comparable to current state-of-the-art coreference resolution methods.

Conclusions:

  • The sequence labeling approach with linear-chain CRFs is effective for coreference resolution.
  • SkipCor offers a scalable, efficient, and parallelizable solution.
  • The proposed method achieves competitive results against established benchmarks.