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Tina specializes in product marketing and in creating content that makes concepts in natural language processing and artificial intelligence accessible, particularly as they relate to multilingual text analytics and how they are used by customers in the field. Through her previous work at Babel Street as a project manager, she has a solid grounding in multilingual search and entity extraction. Previously, she was a journalist in Tokyo and in the U.S. She is fluent in Japanese.

## Posts

- [The Basics of Japanese Names: How They Are Chosen, Written, and Read](https://www.babelstreet.jp/blog/the-basics-of-japanese-names-how-they-are-chosen-written-and-read)
- [How Named Entity Recognition Connects the Dots for Law Enforcement and Intelligence](https://www.babelstreet.jp/blog/how-named-entity-recognition-connects-the-dots-for-law-enforcement-and-intelligence)
- [Challenges of Southeast Asian Languages — Tagalog, Malay, and Indonesian — for Text Analytics](https://www.babelstreet.jp/blog/challenges-of-southeast-asian-languages-tagalog-malay-and-indonesian-for-text-analytics)
- [Seven Tips for Choosing Name Screening Technology](https://www.babelstreet.jp/blog/seven-tips-for-choosing-name-screening-technology)
- [Semantic Search is Remedy for Keyword Inaccuracy in E-discovery](https://www.babelstreet.jp/blog/semantic-search-is-remedy-for-keyword-inaccuracy-in-e-discovery)
- [How to Measure Accuracy of Matching Technology and What to Know Before You Buy](https://www.babelstreet.jp/blog/how-to-measure-accuracy-of-name-matching-technology-and-what-to-know-before-you-buy)
- [Why Japanese Transliteration of Foreign Names is Complex](https://www.babelstreet.jp/blog/why-japanese-transliteration-of-foreign-names-is-complex)
- [Adapt Rosette’s Entity Extraction to Your Content for Increased Accuracy](https://www.babelstreet.jp/blog/adapt-rosettes-entity-extraction-to-your-content-for-increased-accuracy)
- [More than Matching: Match Intelligently Matches Postal Addresses and Dates](https://www.babelstreet.jp/blog/more-than-name-matching-rosette-intelligently-matches-postal-addresses-and-dates)
- [The #1 Obstacle to Accurate Name Screening Isn’t Data: It’s Search](https://www.babelstreet.jp/blog/the-1-obstacle-to-accurate-name-screening-isnt-data-its-search)
- [Evaluating NLP: Annotating Evaluation Data and Scoring Results](https://www.babelstreet.jp/blog/evaluating-nlp-annotating-evaluation-data-and-scoring-results)
- [Evaluating NLP: Assembling a Test Dataset](https://www.babelstreet.jp/blog/evaluating-nlp-assembling-a-test-dataset)
- [Deep Learning Brings Fuzzy English-to-Japanese Matching Into Focus](https://www.babelstreet.jp/blog/deep-learning-brings-fuzzy-english-to-japanese-name-matching-into-focus)
- [What’s the Difference Between Entity Extraction (NER) and Entity Resolution?](https://www.babelstreet.jp/blog/whats-the-difference-between-entity-extraction-ner-and-entity-resolution)