Lecture 21 (29/05/2025): Word Sense Disambiguation and Semantic Role Labeling

2025-10-03 · Source: Multilingual Natural Language Processing @ Sapienza · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Novice, quick

Summary

This blog archive provides a chronological listing of content spanning from 2012 to 2026, detailing the number of entries per year and month. The archive shows a consistent output of content, with 2025 currently listing 23 entries, including specific lecture titles like "Lecture 22 (30/5/2025): Open topics" and "Lecture 20 (23/5/2025): Introduction to Machine Tr...". The most active years appear to be 2021 and 2018, both with 27 entries, followed closely by 2024, 2023, 2022, and 2020, each with 25-26 entries. The archive indicates a focus on academic or technical lectures, particularly in areas like Word Sense Disambiguation, Machine Translation, and Semantics, as evidenced by the 2025 entries.

Key takeaway

For an academic or technical content manager, reviewing this archive structure can inform your content planning and organization. You should consider adopting a similar chronological and topical categorization to enhance discoverability and provide a clear overview of your published materials. This approach helps users quickly locate specific lectures or topics across different years.

Key insights

The archive details a consistent, multi-year content output, primarily focused on academic lectures.

In practice

• Track content output by year and month.
• Categorize entries by specific topics.
• Maintain chronological content organization.

Topics

• Natural Language Processing
• Word Sense Disambiguation
• Machine Translation
• Semantics
• Knowledge Representation

Best for: AI Student, AI Engineer, NLP Engineer

Related on AIssential

• Lecture 22 (30/5/2025): Open topics — The archive provides a historical overview of lecture topics and frequency over more than a decade.
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• From TF-IDF to Transformers: Implementing Four Generations of Semantic Search — Semantic search evolved from explicit rules to learned contextual representations, balancing interpretability with semantic depth.
• Semantic Reranking at Inference Time for Hard Examples in Rhetorical Role Labeling — RISE improves Rhetorical Role Labeling on hard examples by semantically reranking low-confidence predictions at inference time.
• DySem: Uncovering Dynamic Semantic Components via Multilingual Consensus for Calculating Semantic Textual Similarity — DySem uses multilingual consensus to find dynamic, sample-specific semantic dimensions for improved STS in LLMs.
• IdioLink: Retrieving Meaning Beyond Words Across Idiomatic and Literal Expressions — Idiom understanding in retrieval requires semantic abstraction beyond lexical overlap.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Multilingual Natural Language Processing @ Sapienza.