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http -> https, fix url encoding, bio image alt tag
This commit is contained in:
Henning Peters
parent
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@ -4,7 +4,7 @@ include ./meta.jade
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+WritePost(Meta)
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section.intro
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p Natural Language Processing moves fast, so maintaining a good library means constantly throwing things away. Most libraries are failing badly at this, as academics hate to editorialize. This post explains the problem, why it's so damaging, and why I wrote #[a(href="http://spacy.io") spaCy] to do things differently.
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p Natural Language Processing moves fast, so maintaining a good library means constantly throwing things away. Most libraries are failing badly at this, as academics hate to editorialize. This post explains the problem, why it's so damaging, and why I wrote #[a(href="https://spacy.io") spaCy] to do things differently.
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p Imagine: you try to use Google Translate, but it asks you to first select which model you want. The new, awesome deep-learning model is there, but so are lots of others. You pick one that sounds fancy, but it turns out it's a 20-year old experimental model trained on a corpus of oven manuals. When it performs little better than chance, you can't even tell from its output. Of course, Google Translate would not do this to you. But most Natural Language Processing libraries do, and it's terrible.
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@ -12,7 +12,7 @@ include ./meta.jade
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p Have a look through the #[a(href="http://gate.ac.uk/sale/tao/split.html") GATE software]. There's a lot there, developed over 12 years and many person-hours. But there's approximately zero curation. The philosophy is just to provide things. It's up to you to decide what to use.
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p This is bad. It's bad to provide an implementation of #[a(href="https://gate.ac.uk/sale/tao/splitch18.html") MiniPar], and have it just...sit there, with no hint that it's 20 years old and should not be used. The RASP parser, too. Why are these provided? Worse, why is there no warning? The #[a(href="http://webdocs.cs.ualberta.ca/~lindek/minipar.htm") Minipar homepage] puts the software in the right context:
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p This is bad. It's bad to provide an implementation of #[a(href="https://gate.ac.uk/sale/tao/splitch18.html") MiniPar], and have it just...sit there, with no hint that it's 20 years old and should not be used. The RASP parser, too. Why are these provided? Worse, why is there no warning? The #[a(href="https://web.archive.org/web/20150907234221/http://webdocs.cs.ualberta.ca/~lindek/minipar.htm") Minipar homepage] puts the software in the right context:
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blockquote
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p MINIPAR is a broad-coverage parser for the English language. An evaluation with the SUSANNE corpus shows that MINIPAR achieves about 88% precision and 80% recall with respect to dependency relationships. MINIPAR is very efficient, #[strong on a Pentium II 300 with 128MB memory], it parses about 300 words per second.
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@ -23,13 +23,13 @@ include ./meta.jade
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h3 Why I didn't contribute to NLTK
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p Various people have asked me why I decided to make a new Python NLP library, #[a(href="http://spacy.io") spaCy], instead of supporting the #[a(href="http://nltk.org") NLTK] project. This is the main reason. You can't contribute to a project if you believe that the first thing that they should do is throw almost all of it away. You should just make your own project, which is what I did.
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p Various people have asked me why I decided to make a new Python NLP library, #[a(href="https://spacy.io") spaCy], instead of supporting the #[a(href="http://nltk.org") NLTK] project. This is the main reason. You can't contribute to a project if you believe that the first thing that they should do is throw almost all of it away. You should just make your own project, which is what I did.
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p Have a look through #[a(href="http://www.nltk.org/py-modindex.html") the module list of NLTK]. It looks like there's a lot there, but there's not. What NLTK has is a decent tokenizer, some passable stemmers, a good implementation of the Punkt sentence boundary detector (after #[a(href="http://joelnothman.com/") Joel Nothman] rewrote it), some visualization tools, and some wrappers for other libraries. Nothing else is of any use.
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p For instance, consider #[code nltk.parse]. You might think that amongst all this code there was something that could actually predict the syntactic structure of a sentence for you, but you would be wrong. There are wrappers for the BLLIP and Stanford parsers, and since March there's been an implementation of Nivre's 2003 transition-based dependency parser. Unfortunately no model is provided for it, as they rely on an external wrapper of an external learner, which is unsuitable for the structure of their problem. So the implementation is too slow to be actually useable.
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p This problem is totally avoidable, if you just sit down and write good code, instead of stitching together external dependencies. I pointed NLTK to my tutorial describing #[a(href="http://spacy.io/blog/parsing-english-in-python/") how to implement a modern dependency parser], which includes a BSD-licensed implementation in 500 lines of Python. I was told "thanks but no thanks", and #[a(href="https://github.com/nltk/nltk/issues/694") the issue was abruptly closed]. Another researcher's offer from 2012 to implement this type of model also went #[a(href="http://arxiv.org/pdf/1409.7386v1.pdf") unanswered].
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p This problem is totally avoidable, if you just sit down and write good code, instead of stitching together external dependencies. I pointed NLTK to my tutorial describing #[a(href="https://spacy.io/blog/parsing-english-in-python/") how to implement a modern dependency parser], which includes a BSD-licensed implementation in 500 lines of Python. I was told "thanks but no thanks", and #[a(href="https://github.com/nltk/nltk/issues/694") the issue was abruptly closed]. Another researcher's offer from 2012 to implement this type of model also went #[a(href="http://arxiv.org/pdf/1409.7386v1.pdf") unanswered].
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p The story in #[code nltk.tag] is similar. There are plenty of wrappers, for the external libraries that have actual taggers. The only actual tagger model they distribute is #[a(href="http://spacy.io/blog/part-of-speech-POS-tagger-in-python/") terrible]. Now it seems that #[a(href="https://github.com/nltk/nltk/issues/1063") NLTK does not even know how its POS tagger was trained]. The model is just this .pickle file that's been passed around for 5 years, its origins lost to time. It's not okay to offer this to people, to recommend they use it.
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p The story in #[code nltk.tag] is similar. There are plenty of wrappers, for the external libraries that have actual taggers. The only actual tagger model they distribute is #[a(href="https://spacy.io/blog/part-of-speech-POS-tagger-in-python/") terrible]. Now it seems that #[a(href="https://github.com/nltk/nltk/issues/1063") NLTK does not even know how its POS tagger was trained]. The model is just this .pickle file that's been passed around for 5 years, its origins lost to time. It's not okay to offer this to people, to recommend they use it.
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p I think open source software should be very careful to make its limitations clear. It's a disservice to provide something that's much less useful than you imply. It's like offering your friend a lift and then not showing up. It's totally fine to not do something – so long as you never suggested you were going to do it. There are ways to do worse than nothing.
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@ -2,7 +2,7 @@ include ../../header.jade
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include ./meta.jade
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mixin Displacy(sentence, caption_text, height)
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- var url = "https://api.spacy.io/displacy/?full=" + sentence.replace(" ", "%20")
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- var url = "https://api.spacy.io/displacy/?full=" + sentence.replace(/\s+/g, "%20")
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.displacy
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iframe.displacy(src="/resources/displacy/robots.html" height=height)
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@ -20,7 +20,7 @@ mixin Displacy(sentence, caption_text, height)
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p A syntactic dependency parse is a kind of shallow meaning representation. It's an important piece of many language understanding and text processing technologies. Now that these representations can be computed quickly, and with increasingly high accuracy, they're being used in lots of applications – translation, sentiment analysis, and summarization are major application areas.
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p I've been living and breathing similar representations for most of my career. But there's always been a problem: talking about these things is tough. Most people haven't thought much about grammatical structure, and the idea of them is inherently abstract. When I left academia to write #[a(href="http://spaCy.io") spaCy], I knew I wanted a good visualizer. Unfortunately, I also knew I'd never be the one to write it. I'm deeply graphically challenged. Fortunately, when working with #[a(href="http://ines.io") Ines] to build this site, she really nailed the problem, with a solution I'd never have thought of. I really love the result, which we're calling #[a(href="https://api.spacy.io/displacy") displaCy]:
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p I've been living and breathing similar representations for most of my career. But there's always been a problem: talking about these things is tough. Most people haven't thought much about grammatical structure, and the idea of them is inherently abstract. When I left academia to write #[a(href="https://spacy.io") spaCy], I knew I wanted a good visualizer. Unfortunately, I also knew I'd never be the one to write it. I'm deeply graphically challenged. Fortunately, when working with #[a(href="http://ines.io") Ines] to build this site, she really nailed the problem, with a solution I'd never have thought of. I really love the result, which we're calling #[a(href="https://api.spacy.io/displacy") displaCy]:
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+Displacy("Robots in popular culture are there to remind us of the awesomeness of unbounded human agency", "Click the button to full-screen and interact, or scroll to see the full parse.", 325)
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- Meta.links[0].name = 'Reddit'
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- Meta.links[0].title = 'Discuss on Reddit'
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- Meta.links[0].url = "https://www.reddit.com/r/programming/comments/3hoj0b/displaying_linguistic_structure_with_css/"
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- Meta.image = "http://spacy.io/resources/img/displacy_screenshot.jpg"
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- Meta.image = "https://spacy.io/resources/img/displacy_screenshot.jpg"
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- Meta.headline = "Statistical NLP in Basic English"
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- Meta.description = "When I was little, my favorite TV shows all had talking computers. Now I’m big and there are still no talking computers, so I’m trying to make some myself. Well, we can make computers say things. But when we say things back, they don’t really understand. Why not?"
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- Meta.date = "2015-08-24"
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- Meta.url = "/blog/eli5-computers-learn-reading/"
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- Meta.url = "/blog/eli5-computers-learn-reading"
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- Meta.links = []
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//- Meta.links[0].id = 'reddit'
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//- Meta.links[0].name = "Reddit"
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@ -33,7 +33,7 @@ include ../header.jade
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+WritePage(Site, Authors.spacy, Page)
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section.intro.profile
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p A lot of work has gone into #[strong spaCy], but no magic. We plan to keep no secrets. We want you to be able to #[a(href="/blog/spacy-now-mit") build your business] on #[strong spaCy] – so we want you to understand it. Tell us whether you do. #[span.social #[a(href="//twitter.com/" + Site.twitter, target="_blank") Twitter] #[a(href="mailto:contact@spacy.io") Contact us]]
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p A lot of work has gone into #[strong spaCy], but no magic. We plan to keep no secrets. We want you to be able to #[a(href="/blog/spacy-now-mit") build your business] on #[strong spaCy] – so we want you to understand it. Tell us whether you do. #[span.social #[a(href="https://twitter.com/" + Site.twitter, target="_blank") Twitter] #[a(href="mailto:contact@spacy.io") Contact us]]
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nav(role='navigation')
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ul
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li #[a.button(href='#blogs') Blog]
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+WritePost(Meta)
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//# AGPL not free enough: spaCy now under MIT, offering adaptation as a service
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p Three big announcements for #[a(href="http://spacy.io") spaCy], a Python library for industrial-strength natural language processing (NLP).
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p Three big announcements for #[a(href="https://spacy.io") spaCy], a Python library for industrial-strength natural language processing (NLP).
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ol
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li The founding team is doubling in size: I'd like to welcome my new co-founder, #[a(href="https://www.linkedin.com/profile/view?id=ADEAAADkZcYBnipeHOAS6HqrDBPK1IzAAVI64ds&authType=NAME_SEARCH&authToken=YYZ1&locale=en_US&srchid=3310922891443387747239&srchindex=1&srchtotal=16&trk=vsrp_people_res_name&trkInfo=VSRPsearchId%3A3310922891443387747239%2CVSRPtargetId%3A14968262%2CVSRPcmpt%3Aprimary%2CVSRPnm%3Atrue%2CauthType%3ANAME_SEARCH") Henning Peters].
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@ -52,7 +52,7 @@ details
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p The part-of-speech tagger uses the OntoNotes 5 version of the Penn Treebank tag set. We also map the tags to the simpler Google Universal POS Tag set.
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p Details #[a(href="https://github.com/honnibal/spaCy/blob/master/spacy/en/pos.pyx#L124") here].
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p Details #[a(href="https://github.com/honnibal/spaCy/blob/master/spacy/tagger.pyx") here].
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details
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summary: h4 Lemmatization
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- Site.name = "spaCy.io"
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- Site.slogan = "Build Tomorrow's Language Technologies"
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- Site.description = "spaCy is a library for industrial-strength text processing in Python. If you're a small company doing NLP, we want spaCy to seem like a minor miracle."
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- Site.image = "http://spacy.io/resources/img/social.png"
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- Site.image_small = "http://spacy.io/resources/img/social_small.png"
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- Site.image = "https://spacy.io/resources/img/social.png"
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- Site.image_small = "https://spacy.io/resources/img/social_small.png"
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- Site.twitter = "spacy_io"
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- Site.url = "http://spacy.io"
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- Site.url = "https://spacy.io"
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-
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- Authors = {"matt": {}, "spacy": {}};
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- Authors.matt.name = "Matthew Honnibal"
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- Authors.matt.bio = "Matthew Honnibal is the author of the <a href=\"http://spacy.io\">spaCy</a> software and the sole founder of its parent company. He studied linguistics as an undergrad, and never thought he'd be a programmer. By 2009 he had a PhD in computer science, and in 2014 he left academia to found Syllogism Co. He's from Sydney and lives in Berlin."
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- Authors.matt.bio = "Matthew Honnibal is the author of the <a href=\"https://spacy.io\">spaCy</a> software and the sole founder of its parent company. He studied linguistics as an undergrad, and never thought he'd be a programmer. By 2009 he had a PhD in computer science, and in 2014 he left academia to found Syllogism Co. He's from Sydney and lives in Berlin."
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- Authors.matt.image = "/resources/img/matt.png"
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- Authors.matt.twitter = "honnibal"
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-
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- Authors.spacy.name = "SpaCy.io"
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- Authors.spacy.bio = "<a href=\"http://spacy.io\">spaCy</a> is a library for industrial-strength natural language processing in Python and Cython. It features state-of-the-art speed and accuracy, a concise API, and great documentation. If you're a small company doing NLP, we want spaCy to seem like a minor miracle."
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- Authors.spacy.bio = "<a href=\"https://spacy.io\">spaCy</a> is a library for industrial-strength natural language processing in Python and Cython. It features state-of-the-art speed and accuracy, a concise API, and great documentation. If you're a small company doing NLP, we want spaCy to seem like a minor miracle."
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- Authors.spacy.image = "/resources/img/social_small.png"
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- Authors.spacy.twitter = "spacy_io"
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+WriteAuthorBio(Author)
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mixin WriteByline(Author, Meta)
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.subhead by #[a(href="//twitter.com/" + Author.twitter, rel="author" target="_blank") #{Author.name}] on #[time #{getDate(Meta.date).fulldate}]
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.subhead by #[a(href="https://twitter.com/" + Author.twitter, rel="author" target="_blank") #{Author.name}] on #[time #{getDate(Meta.date).fulldate}]
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mixin WriteShareLinks(headline, url, twitter, links)
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a.button.button-twitter(href="http://twitter.com/share?text=" + headline + "&url=" + Site.url + url + "&via=" + twitter title="Share on Twitter" target="_blank")
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a.button.button-twitter(href="https://twitter.com/share?text=" + headline.replace(/\s+/g, "%20") + "&url=" + Site.url + url + "&via=" + twitter title="Share on Twitter" target="_blank")
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| Share on Twitter
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if links
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.discuss
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| Discuss on #{link.name}
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mixin TweetThis(text, url)
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p #[span #{text} #[a.share(href='http://twitter.com/share?text="' + text + '"&url=' + Site.url + url + '&via=' + Site.twitter title='Share on Twitter' target='_blank') Tweet]]
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p #[span #{text} #[a.share(href='https://twitter.com/share?text="' + text.replace(/\s+/g, "%20") + '"&url=' + Site.url + url + '&via=' + Site.twitter title='Share on Twitter' target='_blank') Tweet]]
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mixin WriteAuthorBio(Author)
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section.intro.profile
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p #[img(src=Author.image)] !{Author.bio} #[span.social #[a(href="//twitter.com/" + Author.twitter target="_blank") Twitter]]
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p #[img(src=Author.image alt=Author.name)] !{Author.bio} #[span.social #[a(href="https://twitter.com/" + Author.twitter target="_blank") Twitter]]
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- var getDate = function(input) {
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mixin Displacy(sentence, caption_text, height)
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- var url = "https://api.spacy.io/displacy/?full=" + sentence.replace(" ", "%20")
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- var url = "https://api.spacy.io/displacy/?full=" + sentence.replace(/\s+/g, "%20")
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.displacy
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iframe.displacy(src="/resources/displacy/displacy_demo.html" height=height)
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+WritePost(Meta)
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section.intro
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p #[a(href="http://spaCy.io") spaCy] is great for data exploration. Poking, prodding and sifting is fundamental to good data science. In this tutorial, we'll do a broad keword search of Twitter, and then sift through the live stream of tweets, zooming in on some topics and excluding others.
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p #[a(href="https://spacy.io") spaCy] is great for data exploration. Poking, prodding and sifting is fundamental to good data science. In this tutorial, we'll do a broad keword search of Twitter, and then sift through the live stream of tweets, zooming in on some topics and excluding others.
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p An example filter-function:
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