We are excited to announce the first in a new series of posts and a brand new initiative: Data Community DC Videos! We are going to film and publish online videos (and separate audio, resources permitting) as many talks from Data Community DC meetups as possible. Yes, we want you to experience the events in person, but realize that not everyone who wants to be a part of our community can attend every single event.
To kick this off, we have a fantastic video of Dr. Jesse English passionately discussing a brand new, open source framework, WIMs (Weakly Inferred Meanings), a novel approach to creating structured meaning representations for semantic analyses. Whereas a TMR (text meaning representation) requires a large, domain-specific knowledge base and significant computation times, WIMs cover a limited scope of possible relationships. The limitation is intentional, and allows for better performance– but still carries enough relationships for most applications. Additionally, the creation of a bespoke knowledge base and microtheory is not required, the novel pattern matching technique means that available ontologies like WordNet provide enough coverage. WIMs are Open Source and available now, and are truly a break through in semantic processing.
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Why You Should Not Build a Recommendation Engine
Recommendation engines are arguably one of the trendiest uses of data science in startups today. How many new apps have you heard of that claim to “learn your tastes”? However, recommendations engines are widely misunderstood both in terms of what is involved in building a one as well as what problems they actually solve. A true recommender system involves some fairly hefty data science — it’s not something you can build by simply installing a plugin without writing code. With the exception of very rare cases, it is not the killer feature of your minimum viable product (MVP) that will make users flock to you — especially since there are so many fake and poorly performing recommender systems out there.
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