Finding Video Games with Sense2Vec

2023-10-11 · Source: Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, extended

Summary

The article discusses using Sense2Vec for detecting video games in text, particularly for pre-filling annotation interfaces. It highlights challenges in named entity recognition (NER) for video games, such as common abbreviations (e.g., "KOTOR" for "Knights of the Old Republic"), long titles, and domain specificity. Sense2Vec, a Python library from Explosion, offers contextualized word and phrase embeddings trained on Reddit data, which is beneficial for handling internet slang and abbreviations. The author demonstrates Sense2Vec's online demo and its integration with Prodigy for NER annotation. A pragmatic approach involves scanning a dataset for phrases, then using cosine similarity with Sense2Vec embeddings to identify potential video game mentions. These are then used as patterns for pre-highlighting in Prodigy, significantly improving annotation efficiency, despite limitations like embeddings last updated in 2019.

Key takeaway

For NLP engineers building custom NER models, if you're struggling with domain-specific entities, especially those with common abbreviations or long titles, consider Sense2Vec. Its Reddit-trained, contextualized phrase embeddings can significantly boost your annotation efficiency by pre-highlighting potential entities. You should integrate Sense2Vec with spaCy and Prodigy to generate robust pattern files, reducing manual labeling effort for complex entity types. Be aware that embeddings from 2019 might miss newer entities.

Key insights

Sense2Vec's contextualized phrase embeddings, trained on Reddit, effectively identify domain-specific entities like video games and their abbreviations.

Principles

• Domain-specific embeddings improve NER accuracy.
• Contextualized phrase vectors capture nuanced meaning.
• Reddit-trained data handles internet slang and abbreviations.

Method

Use Sense2Vec to generate similar phrases from seed terms, or scan your dataset for phrases, then use cosine similarity with Sense2Vec embeddings to create patterns for pre-highlighting in NER annotation tools like Prodigy.

In practice

• Use `sense2vec.teach` for terminology list generation.
• Integrate Sense2Vec with spaCy for phrase detection.
• Generate patterns for NER pre-highlighting in Prodigy.

Topics

• Named Entity Recognition
• Sense2Vec
• Word Embeddings
• Prodigy
• Text Annotation
• Phrase Embeddings

Best for: Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai.