Beware the lure of crowdsourced data

Crowdsourced data can often be inconsistent, messy or downright wrong

We all like something for nothing, that’s why open source software is so popular. (It’s also why the Pirate Bay exists). But sometimes things that seem too good to be true are just that.

Repustate is in the text analytics game which means we needs lots and lots of data to model certain characteristics of written text. We need common words, grammar constructs, human-annotated corpora of text etc. to make our various language models work as quickly and as well as they do.

We recently embarked on the next phase of our text analytics adventure: semantic analysis. Semantic analysis the process of taking arbitrary text and assigning meaning to the individual, relevant components. For example, being able to identify “apple” as a fruit in the sentence “I went apple picking yesterday” but to identify “Apple’ the company when saying “I can’t wait for the new Apple product announcement” (note: even though I used title case for the latter example, casing should not matter)

To be able to accomplish this task, we need a few things:

1) List of every possible person/place/business/thing we care about and the classification they belong to

2) A corpus of text (or corpora) that will allow us to disambiguate terms based on context. In other words, if we see the word “banana” near the word “apple”, we can safely assume we’re talking about fruits and not computers.

Since we’re not Google, we don’t have access to every person’s search history and resulting click throughs (although their n-gram data is useful in some applications). So we have to be clever.

For anyone who’s done work in text analysis, you’ll have heard of Freebase. Freebase is a crowdsourced repository of facts. Kind volunteers have contributed lists of data and tagged meta information about them. For example, you can look up all makes of a particular automotive manufacturer, like Audi. You can see a list of musicians (hundreds of thousands actually), movie stars, TV actors or types of food.

It’s tempting to use data like Freebase. It seems like someone did all the work for you. But once you dig inside, you realize it’s tons of junk, all the way the down.

For example, under the Food category, you’ll see the name of each US state. I didn’t realize I could eat Alaska. Under book authors, you’ll see any athlete who’s ever “written” an autobiography. I highly doubt Michael Jordan wrote his own book, but there it is. LeBron James, NBA all-star for the Miami Heat, is listed as a movie actor.

The list goes on and on. While Freebase definitely does lend itself to being a good starting point, ultimately you’re on your own to come up with a better list of entities either through some mechanical turking or being more clever 🙂

By the way, if you’d like to see the end result of Repustate’s curation process, head on over to the Semantic API and try it out.

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