Natural language processing (NLP) strives to bridge the gap between the complex world of human language and the structured world of machines. Coreference, the phenomenon where words or phrases refer back to previously mentioned entities, plays a crucial role in this endeavor. Understanding and resolving coreference is essential for tasks like machine translation, question answering, and sentiment analysis, as it allows machines to grasp the relationships between elements in a sentence and build a coherent understanding of ...
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Natural language processing (NLP) strives to bridge the gap between the complex world of human language and the structured world of machines. Coreference, the phenomenon where words or phrases refer back to previously mentioned entities, plays a crucial role in this endeavor. Understanding and resolving coreference is essential for tasks like machine translation, question answering, and sentiment analysis, as it allows machines to grasp the relationships between elements in a sentence and build a coherent understanding of the text. The Challenge of Coreference Human brains excel at coreference resolution. We effortlessly infer who "he," "she," or "it" refers to based on context and world knowledge. However, for machines, coreference presents a significant challenge. Consider the sentence: "The scientist discovered a new element. She named it after her hometown.
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