From cb0253e0a38a8597151d5fc357e6317d93975eea Mon Sep 17 00:00:00 2001 From: Dmitrijs Milajevs Date: Sun, 11 May 2014 13:16:29 +0200 Subject: [PATCH 1/6] Initial pygrunn 14 article --- content/articles/016-pygrunn14.rst | 129 ++++++++++++++++++ .../static/article_covers/016-pygrunn14.jpg | Bin 0 -> 87391 bytes content/static/images/016-en_zipf.png | Bin 0 -> 129000 bytes 3 files changed, 129 insertions(+) create mode 100644 content/articles/016-pygrunn14.rst create mode 100644 content/static/article_covers/016-pygrunn14.jpg create mode 100644 content/static/images/016-en_zipf.png diff --git a/content/articles/016-pygrunn14.rst b/content/articles/016-pygrunn14.rst new file mode 100644 index 0000000..6a44240 --- /dev/null +++ b/content/articles/016-pygrunn14.rst @@ -0,0 +1,129 @@ +Pygrunn 2014 +============ + +:date: 2014-05-10 15:56 +:tags: conference, talk, nlp, pygrunn +:category: life +:author: Dmitrijs Milajevs +:template: article_cover +:cover: 016-pygrunn14.jpg + +`Pygrunn `_ is an awesome conference for Python +developers and friends, which takes place in +`Groningen `_. + +As usually, the conference was perfectly organized. This is one of the most +stylish conferences I've ever attended. It constantly grows, and next year the +conference moves to a bigger venue, so keep the beginning of May 2015 free and +attend the event. + +Another positive trend is the growing proportion of science related talks. One +of the topics of the conference became (scientific) code quality and +collaboration between professional developers and scientists. + +Check awesome summaries of talks by +`Reinout van Rees `_ +and +`Maurits van Rees `_. Get the +`#pygrunn `_ tweets and follow +`@pygrunn `_. + + +Computational linguistics 101 +----------------------------- + +`My presentation`__ started as a demonstration of the modern pythonic scientific +tools (my subjective classification): + +__ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/notebooks/pygrunn14.ipynb + +1. Data structures + * `numpy `_ + * `scipy `_ + * `pandas `_ +2. Algorithms + * `scikit-learn `_ + * `nltk `_, + * `textblob `_ + * `gensim `_ +3. Reporting + * `ipython `__ + * `matplotlib `__ + +However, I find the technical talks with a lot of code rather boring, so I +decided to show how these libraries are used to solve simple CL tasks. + +First, I `covered`__ `Zipf's law `_ +and showed that it holds for an English text. As a homework, I asked whether the +same behavior is observed in other languages and what the differences are. + +__ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/notebooks/pygrunn14.ipynb#english-word-frequencies + +I could not resist and presented my `research area`__ :) by extracting co- +occurrence counts and projecting the word vectors to 2 dimensions. I managed to +get a plot where ``girl`` is close to ``boy`` but far to ``manager``. + +__ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/notebooks/pygrunn14.ipynb#distributional-semantics + +Sprint +------ + +`@_spyreto_ `_ and +`Sjoerd de Haan `_ liked the +idea of counting word frequencies among various languages and see the behavior +of the slope. + +Initially, we wanted to take EU directives and compare the official EU languages, +however, the website was down, and we were kindly redirected to +`this page `_ every time we wanted to get a legal +document. + +Luckily, we found an already prepared `word frequencies for a many languages +`_ and reused them. + +The task was to + +* plot the ranked frequency distribution on the log-log scale +* estimate the slope :math:`\alpha`, the ratio of the frequencies between the + neighboring words in the rank. + +`We tried`__ English, Dutch, Russian, Latvian, Spanish and Italian. All languages +obey Zipf's law, at least visually. Here is a plot for English (see `the notebook`__ +for other plots): + +__ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/notebooks/Word%20frequencies.ipynb +__ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/notebooks/Word%20frequencies.ipynb + +.. image:: {filename}/static/images/016-en_zipf.png + :align: center + :alt: English word frequency counts on the log-log scale. + :target: {filename}/static/images/016-en_zipf.png + +The blue line is the provided frequencies, the green is a regression line. +Theory [Li1992]_ says that the slope coefficient should be close to -1. As the +table shows, the values deviate from -1 quite drastically (-1.7 for Spanish). +Also, the `slope estimate`__ for English from the `British National Corpus`__ is +-1.18. + +__ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/notebooks/pygrunn14.ipynb#estimating-the-slope +__ http://www.natcorp.ox.ac.uk/ + +========= ========= =========== +Language Slope Intercept +========= ========= =========== +uk -1.044263 11.212273 +nl -1.566664 19.635268 +ru -1.395736 17.781756 +lv -1.055992 11.541761 +es -1.707326 22.161790 +it -1.601567 20.000540 +========= ========= =========== + +Conclusion +---------- + +.. [Li1992] Li, Wentian. + `Random texts exhibit Zipf's-law-like word frequency distribution.`__ + Information Theory, IEEE Transactions on 38.6 (1992): 1842-1845. + +__ http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.164.8422&rep=rep1&type=pdf \ No newline at end of file diff --git a/content/static/article_covers/016-pygrunn14.jpg b/content/static/article_covers/016-pygrunn14.jpg new file mode 100644 index 0000000000000000000000000000000000000000..845b48b602e45149a299478e4eb94f7e1a389c57 GIT binary patch literal 87391 zcmb5Vc~nx}AOCw8WH3=c8$>M>9H=xi)EomOK_tPco@Pr;G&2WGEh|Ja^a(^HQ*%JX zF`H~KZFsb@)Uq5ibI8n0Ev?jSu;KFkt-J2J_s@IygTp$o&sm4h+55fU@7MnO^6v!ObY>r!;wgYwvGWxXUP&BL#)1@feCKaN)w#1F`j5+ zi8otIFgCVwB(1ewPbQOBSvt9Gv~#uDK(_l|BOoLask2091q!vo&eYh{?*I4pw-LY~ zAOIAAfXo0e1_Z%?{yqeZ0RRk!{O@xApA7_tLbPCTgyv`f8UR5wAE7Wf6b^%Gf#D#{ zPa#k(tm%3f)mPtaUku#9E+wmkK=ZrW)cJYohW)Wb520N3=F2j3vVUCd^5E3_76I8^ zqxSU84uK(j#jK`PoTicg?bIcl}xfU=83jROa|M?c^e`nO3P~X>#YPUhl 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zAP!c&y17Nvto%L`@VN;|*fJE5{9&1qY>k7k^0+I&x5OwM^xy`I_zk3K1`y)jd3vmH z(I`?Zk(>!BNjOXw#PQZlo|VuEx_ekD^{wPH0alO(2M1B!8m0fyS;Qza4>G9oBemZism(P=$HPPnnQ~Px)JmIo}6&A8dyzAj4q&5hOnNe^Wj`n*me+`tj3g8`g(8! z{NyCN?wTr;|GjWfGxT$hw3A0 Date: Sat, 17 May 2014 14:16:52 +0100 Subject: [PATCH 2/6] =?UTF-8?q?PR=20comments:=20Zipf=E2=80=99s=20law=20and?= =?UTF-8?q?=20DS?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- content/articles/016-pygrunn14.rst | 69 ++++++++++++++++++++++--- content/static/images/016-bnc_freq.png | Bin 0 -> 71259 bytes content/static/images/016-ds.png | Bin 0 -> 201021 bytes 3 files changed, 61 insertions(+), 8 deletions(-) create mode 100644 content/static/images/016-bnc_freq.png create mode 100644 content/static/images/016-ds.png diff --git a/content/articles/016-pygrunn14.rst b/content/articles/016-pygrunn14.rst index 6a44240..a29b9a4 100644 --- a/content/articles/016-pygrunn14.rst +++ b/content/articles/016-pygrunn14.rst @@ -53,25 +53,78 @@ __ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/ However, I find the technical talks with a lot of code rather boring, so I decided to show how these libraries are used to solve simple CL tasks. -First, I `covered`__ `Zipf's law `_ -and showed that it holds for an English text. As a homework, I asked whether the -same behavior is observed in other languages and what the differences are. +A universal pattern behind natural languages +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +First, I `covered`__ `Zipf's law `_, +which states that the frequency of any word in a corpus of texts is inversely +proportional to its rank in the frequency table. To show that the law holds for +an English text, I loaded `the BNC frequency list`__ provided by `Adam +Kilgarriff`__ into `Pandas `_ `DataFrame`__ and +plotted the sorted frequencies on the log-log scale. __ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/notebooks/pygrunn14.ipynb#english-word-frequencies +__ http://www.kilgarriff.co.uk/BNClists/lemma.num +__ http://www.kilgarriff.co.uk/bnc-readme.html +__ http://pandas.pydata.org/pandas-docs/version/0.13.1/generated/pandas.DataFrame.html + +.. image:: {filename}/static/images/016-bnc_freq.png + :align: center + :alt: English word frequency counts extracted from the British National Corpus on the log-log scale. + :target: {filename}/static/images/016-bnc_freq.png + +As a homework, I asked whether the same behavior is observed in +other languages and what the differences are. -I could not resist and presented my `research area`__ :) by extracting co- -occurrence counts and projecting the word vectors to 2 dimensions. I managed to -get a plot where ``girl`` is close to ``boy`` but far to ``manager``. +Distributional semantics +~~~~~~~~~~~~~~~~~~~~~~~~ + +I could not resist and `presented`__ my `research area`__ :) by extracting word +co-occurrence counts and projecting the word vectors to 2 dimensions using +`scikit-learn`__ implementation of `manifold learning`__. __ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/notebooks/pygrunn14.ipynb#distributional-semantics +__ http://www.eecs.qmul.ac.uk/~dm303/ +__ http://scikit-learn.org/stable/ +__ http://scikit-learn.org/stable/modules/manifold.html + +In distributional semantics, words are represented as rows in a matrix. The +columns correspond to other words the word co-occurs with. The values of the +matrix are the frequencies the words co-occurred together. For example, here are +the vectors for the words ``idea``, ``notion``, ``boy`` and ``girl``. + +======= ========== ==== ====== +\ philosophy book school +======= ========== ==== ====== +idea 10 47 39 +notion 7 3 15 +boy 0 12 146 +girl 0 19 93 +======= ========== ==== ====== + +So, ``idea`` was seen with ``philosophy`` 10 times in the corpus I used. An +occurrence in this case means that ``philosophy`` was not more than 5 words +further from ``idea``. + +The number patterns for ``boy`` and ``girl`` are much more similar than for +``boy`` and ``notion``, suggesting that ``boy`` is much more similar to ``girl`` +than to ``notion``. + +.. image:: {filename}/static/images/016-ds.png + :align: center + :alt: Word semantic relatedness. + :target: {filename}/static/images/016-ds.png + +I used manifold learning to provide a visualization for a bigger set of words +(and a much bigger set of contexts). Sprint ------ `@_spyreto_ `_ and `Sjoerd de Haan `_ liked the -idea of counting word frequencies among various languages and see the behavior -of the slope. +idea of counting word frequencies among various languages and see how they +compare in relation to Zipf's law. 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b/content/articles/016-pygrunn14.rst @@ -38,16 +38,16 @@ tools (my subjective classification): __ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/notebooks/pygrunn14.ipynb 1. Data structures - * `numpy `_ - * `scipy `_ - * `pandas `_ + * `NumPy `_ + * `SciPy `_ + * `Pandas `_ 2. Algorithms * `scikit-learn `_ - * `nltk `_, - * `textblob `_ + * `NLTK `_, + * `TextBlob `_ * `gensim `_ 3. Reporting - * `ipython `__ + * `IPython `__ * `matplotlib `__ However, I find the technical talks with a lot of code rather boring, so I From fd9af3d0cc2eb85f7f33fe43995e80c8e1c401bf Mon Sep 17 00:00:00 2001 From: Dmitrijs Milajevs Date: Sat, 17 May 2014 19:59:43 +0100 Subject: [PATCH 4/6] More examples and linguistic insights. --- content/articles/016-pygrunn14.rst | 89 ++++++++++++++++------ content/static/images/016-en_bnc_zipf.png | Bin 0 -> 98186 bytes content/static/images/016-en_zipf.png | Bin 129000 -> 134464 bytes 3 files changed, 67 insertions(+), 22 deletions(-) create mode 100644 content/static/images/016-en_bnc_zipf.png diff --git a/content/articles/016-pygrunn14.rst b/content/articles/016-pygrunn14.rst index 796958f..759863c 100644 --- a/content/articles/016-pygrunn14.rst +++ b/content/articles/016-pygrunn14.rst @@ -121,7 +121,7 @@ I used manifold learning to provide a visualization for a bigger set of words Sprint ------ -`@_spyreto_ `_ and +`Spyros Ioakeimidis `_ and `Sjoerd de Haan `_ liked the idea of counting word frequencies among various languages and see how they compare in relation to Zipf's law. @@ -131,21 +131,36 @@ however, the website was down, and we were kindly redirected to `this page `_ every time we wanted to get a legal document. -Luckily, we found an already prepared `word frequencies for a many languages -`_ and reused them. - -The task was to - -* plot the ranked frequency distribution on the log-log scale -* estimate the slope :math:`\alpha`, the ratio of the frequencies between the - neighboring words in the rank. - -`We tried`__ English, Dutch, Russian, Latvian, Spanish and Italian. All languages -obey Zipf's law, at least visually. Here is a plot for English (see `the notebook`__ -for other plots): - -__ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/notebooks/Word%20frequencies.ipynb -__ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/notebooks/Word%20frequencies.ipynb +Luckily, we found an already prepared `word frequencies for many languages +`_ and reused them. We +wrote a simple function to plot the frequency of the words against the rank of +the words in the frequency table. Here is the top 10 most frequently used words +in English, Dutch and Latvian: + +==== ======== ========= ======== ========= ======== ========= +\ English Dutch Latvian +---- ------------------ ------------------ ------------------ +Rank Word Frequency Word Frequency Word Frequency +==== ======== ========= ======== ========= ======== ========= +1 you 6281002 ik 2091479 ir 20182 +2 i 5685306 je 1995150 es 19042 +3 the 4768490 het 1428477 un 12737 +4 to 3453407 de 1399236 tu 12141 +5 a 3048287 is 1202489 tas 8601 +6 it 2879962 dat 1188131 ka 7964 +7 and 2127187 een 1011496 man 7725 +8 that 2030642 niet 997681 to 7535 +9 of 1847884 en 774098 vai 7527 +10 in 1554103 wat 618627 ko 6906 +==== ======== ========= ======== ========= ======== ========= + +If you plot the word rank on the x axis and the word frequency on the y axis on +a log-log scale you should see a straight line. A straight line on a log-log +plot implies that the quantities on the two axis are related trough a power law. +Thus, if our data would fit straight line perfectly, that would mean that the +frequency of a word occurring is exactly proportional to a power of the rank of +that word in the frequency table. This is the content of Zipf's law, but +of course, such laws are never exact. .. image:: {filename}/static/images/016-en_zipf.png :align: center @@ -153,17 +168,27 @@ __ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/ :target: {filename}/static/images/016-en_zipf.png The blue line is the provided frequencies, the green is a regression line. -Theory [Li1992]_ says that the slope coefficient should be close to -1. As the -table shows, the values deviate from -1 quite drastically (-1.7 for Spanish). -Also, the `slope estimate`__ for English from the `British National Corpus`__ is --1.18. -__ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/notebooks/pygrunn14.ipynb#estimating-the-slope -__ http://www.natcorp.ox.ac.uk/ +One thing we can compare amongst languages is how well this plot follows a +straight line. Also the slope of the line contains interesting information. It +tells what kind of power law we are dealing with exactly. + +The slope is related to the morphology of a language. For example, in Latvian, +which has quite rich morphology, the word `"city"` is `"pilsēta"`, but the +English phrase `"in a city"` is `"pilsētā"`. All the occurrences of "`pilsēta`" +in a Latvian text will be distributed over several morphological forms, lowering +the counts. As a result, the slope for a Latvian text will be less steep +comparing to English. + +We `tried`__ English, Ukrainian, Dutch, Russian, Latvian, Spanish and Italian. All +languages obey Zipf's law, at least visually. + +__ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/notebooks/Word%20frequencies.ipynb ========= ========= =========== Language Slope Intercept ========= ========= =========== +en -1.717729 21.934904 uk -1.044263 11.212273 nl -1.566664 19.635268 ru -1.395736 17.781756 @@ -172,9 +197,29 @@ es -1.707326 22.161790 it -1.601567 20.000540 ========= ========= =========== +Theory [Li1992]_ says that the slope coefficient should be close to -1. As the +table below shows, the values deviate from -1 quite drastically (-1.57 for +Dutch, for example). Also, the `slope estimate`__ for English from the `British +National Corpus`__ is -1.18 in contrary to -1.72. Here is the Zipf's law +visualization for English extracted from the BNC. + +__ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/notebooks/pygrunn14.ipynb#estimating-the-slope +__ http://www.natcorp.ox.ac.uk/ + +.. image:: {filename}/static/images/016-en_bnc_zipf.png + :align: center + :alt: Actual and estimated English word frequencies from the BNC. + :target: {filename}/static/images/016-en_bnc_zipf.png + Conclusion ---------- +Pygrunn is a great conference that start attracting not only (professional web) +developers, but also scientists. I was really surprised that my talk got a bit +of attention and people were willing to hack around a linguistic phenomena. I +hope that next year this trend continues. And the two communities become closer +to each other. + .. 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zAP!c&y17Nvto%L`@VN;|*fJE5{9&1qY>k7k^0+I&x5OwM^xy`I_zk3K1`y)jd3vmH z(I`?Zk(>!BNjOXw#PQZlo|VuEx_ekD^{wPH0alO(2M1B!8m0fyS;Qza4>G9oBemZism(P=$HPPnnQ~Px)JmIo}6&A8dyzAj4q&5hOnNe^Wj`n*me+`tj3g8`g(8! z{NyCN?wTr;|GjWfGxT$hw3A0 Date: Tue, 10 Jun 2014 18:50:22 +0100 Subject: [PATCH 5/6] Links --- content/articles/016-pygrunn14.rst | 27 +++++++++++++++------------ 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/content/articles/016-pygrunn14.rst b/content/articles/016-pygrunn14.rst index 759863c..c22b814 100644 --- a/content/articles/016-pygrunn14.rst +++ b/content/articles/016-pygrunn14.rst @@ -37,18 +37,21 @@ tools (my subjective classification): __ http://nbviewer.ipython.org/urls/bitbucket.org/dimazest/phd-buildout/raw/tip/notebooks/pygrunn14.ipynb -1. Data structures - * `NumPy `_ - * `SciPy `_ - * `Pandas `_ -2. Algorithms - * `scikit-learn `_ - * `NLTK `_, - * `TextBlob `_ - * `gensim `_ -3. Reporting - * `IPython `__ - * `matplotlib `__ +1. Data structures: NumPy_, SciPy_, Pandas_ +2. Algorithms: scikit-learn_, NLTK_, TextBlob_, gensim_ +3. Reporting: IPython_, matplotlib_ seaborn_ + +.. _NumPy: http://www.numpy.org/ +.. _SciPy: http://www.scipy.org/scipylib/index.html +.. _Pandas: http://pandas.pydata.org/ +.. _scikit-learn: http://scikit-learn.org/ +.. _NLTK: http://www.nltk.org/ +.. _TextBlob: http://textblob.readthedocs.org +.. _gensim: http://radimrehurek.com/gensim/ +.. _IPython: ttp://ipython.org/ +.. _matplotlib: http://matplotlib.org/ +.. _seaborn: http://www.stanford.edu/~mwaskom/software/seaborn/ + However, I find the technical talks with a lot of code rather boring, so I decided to show how these libraries are used to solve simple CL tasks. From c8c26cfe9754fbe36bf4b38dd22f2faaa9e2acf6 Mon Sep 17 00:00:00 2001 From: Dmitrijs Milajevs Date: Tue, 10 Jun 2014 18:50:43 +0100 Subject: [PATCH 6/6] A nicer explanation about manifold learning --- content/articles/016-pygrunn14.rst | 29 ++++++++++++++++++++++++----- 1 file changed, 24 insertions(+), 5 deletions(-) diff --git a/content/articles/016-pygrunn14.rst b/content/articles/016-pygrunn14.rst index c22b814..ec16354 100644 --- a/content/articles/016-pygrunn14.rst +++ b/content/articles/016-pygrunn14.rst @@ -105,21 +105,40 @@ boy 0 12 146 girl 0 19 93 ======= ========== ==== ====== -So, ``idea`` was seen with ``philosophy`` 10 times in the corpus I used. An -occurrence in this case means that ``philosophy`` was not more than 5 words +So, ``idea`` was seen with ``philosophy`` 10 times in the corpus I used. A +co-occurrence in this case means that ``philosophy`` was not more than 5 words further from ``idea``. The number patterns for ``boy`` and ``girl`` are much more similar than for ``boy`` and ``notion``, suggesting that ``boy`` is much more similar to ``girl`` -than to ``notion``. +than to ``notion``. Clearly, we can select much more words to label rows, making +the similarity reasoning more precise. + +We can reason on word semantic similarity from a geometrical point of view using +a distance measure (for example, Euclidean distance). The closer are two vectors +to each other in the vector space, the closer are the words semantically. + +Unfortunately, it's difficult for humans to reason in more than 3 dimensions. +While the multidimensional space is useful to perform computations, it's useless +to present the patterns words share. If we could imagine the space, we would +discover areas (or directions) that correspond to the girlish/boylish words and +to the more abstract idea/notion. + +To overcome the issue, we can reduce the dimensionality of the space in such a +way that the distance between the elements is respected. Clearly, we can't +completely preserve the distances, but it's possible to respect the distances to +some degree. + +Manifold learning is one of many techniques to perform dimensionality reduction. +If we apply it to the extracted co-occurrence counts for some of the words and +reduce to two dimensions (so we can plot it), we will notice that related words +cluster around each other. .. image:: {filename}/static/images/016-ds.png :align: center :alt: Word semantic relatedness. :target: {filename}/static/images/016-ds.png -I used manifold learning to provide a visualization for a bigger set of words -(and a much bigger set of contexts). Sprint ------