Modeling Revolutionary Discourse

Modeling Revolutionary Discourse

As part of our lead work on ARTFL’s NEH funded Intertextual Bridges project, we are pleased to release a prototype build of the Newberry Library’s French Revolution Collection (FRC), which integrates topic model browsing and search, relevancy searching, and full PhiloLogic4 services, in a set of interrelated functions. This post will describe the current state of this work, document some of the functionalities, and provide an outline of our next steps of development.

In 2017, the Newberry library released digital copies of more than 35,000 pamphlets totalling approximately 850,000 pages of it’s extremely rich holdings related to the French Revolution. Shortly thereafter, ARTFL project released versions of the Newberry FRC under PhiloLogic4 of this unparalleled resource. In a subsequent post, we described the collection, some of the capabilities of this initial installation and preliminary results using the tools deployed in this build.

We have two builds of the FRC under PhiloLogic. The first is simply a load of the entire collection of 38,377 documents as it was downloaded towards the end of 2017. We applied some error correction functions, which we recently modified slightly applied to the installation (search form). The bulk of our work has been aimed at the FRC collection for works from 1787-1799 with the aim to improve the data and metadata as well as remove duplicate documents. The 2017 release of the FRC at ARTFL contained 26,455 documents, where duplicates were identified by metadata comparison. Using data generated our new sequence alignment package TextPair, which identified both similar passages and possibly duplicated documents, we further reduced the collection to 25,935 documents. 

We currently have three entry points to collection. The basic component which underlies the whole system is PhiloLogic, our corpus query engine which houses the words index, the structure and the metadata of the collection:
          https://artflsrv03.uchicago.edu/philologic4/frc1787-99rev2b/
To facilitate the discovery of documents relevant to search queries, we added on a ranked-relevance engine, called Whoosh, which is built on top of the PhiloLogic index:
          https://artflsrv03.uchicago.edu/mark/frc/frc1787-99.whoosh.html
Finally, as an additional way of exploring the topics and discourses that run through the FRC, we built a topic-modeling browser called TopoLogic, which also leverages the PhiloLogic instance:
          https://artflsrv03.uchicago.edu/topic-modeling-browser/frc1787_99/.
While all three systems have specific capabilities and reporting features and function as discrete units, because they share a single data feed model (built from the PhiloLogic index), they are designed to be interoperable, and hence provide links across one another. It is our belief that there is no all-encompassing algorithmic approach to text analysis, and that topic-modeling provides one view that may be worth exploring, but no more so than other methods.

TopoLogic is the latest entry in our quest to build value-added services on top of the standard PhiloLogic index, and leverage topic-modeling techniques to offer an alternate way of exploring text collections. Topic-modeling, the algorithmic technique which we use for this new navigational tool, is an unsupervised machine learning approach designed to facilitate the exploration of large collections of texts where no topical information is provided. As such, this computational method can be a truly useful way of gaining a sense of the topical structure of a corpus -- i.e. to find out what's in there -- and how words are clustered together to form meaningful discourses.

TopoLogic builds upon the topics and semantic fields generated by the algorithm to provide a web-based navigation system which lets users explore topics and discourses across time, as well as word usage within different contexts. The interaction of the three different schemes allows the user to navigate between alternative ways of considering topics across the collection. The following slides are designed to give some idea of how users may navigate between topics, word searches and other capabilities provided by these different systems.



In our experience, there are a number of caveats to consider when using this algorithmic approach to text analysis. First, while topic-modeling is able to uncover relationships between words and documents without a training corpus (thus its unsupervised nature), it does require a certain number of priors, such as the number of topics to uncover, in order to function. In other words, the user of such method needs to determine (through trial and error) what that user deems to be the more meaningful representation of the corpus. Our experience has shown us that slight changes in the underlying texts (such as adding or removing a couple texts), or in the preprocessing steps (such as removing additional function words), can lead to drastically different results. All in all, we have always taken a very measured approach to our interpretation of topic models, and we strongly discourage against relying upon them as the sole source for text analysis.

The systems complement each other by providing checks on the results of particular functions. For example, in slide X above, we present the top 50 documents for topic 19 as measured by topic weight. In using a rank relevancy search for the top 10 tokens for topic 19, we arrive at a rather different list. The differences are due to the interaction of weighting schemes and relevancy measures. Both are useful approaches, but do, by design, deliver somewhat different results.

It is our pleasure to acknowledge that the Newberry Library has released this extraordinary resource under the Open Data Commons Attribution License, ODC-BY 1.0. We believe that this splendid collection and the Newberry’s release of all of the data will facilitate a generation of ground-breaking work in Revolutionary studies. If you find the collection useful, please do contact the Newberry Library to congratulate them on this wonderful initiative and how their efforts contribute to your research. Clovis & Mark

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Mapping Encyclopédie classes of knowledge to LDA generated topics

As was described in my previous blog entry, I've been working on comparing the results given by LDA generated topics with the classes of knowledge identified by the philosophes in the Encyclopédie. My initial experiment was to try to see if out of 5000 articles belonging to 100 classes of knowledge, with 50 articles per class, I would find those 100 topics using an LDA topic modeler. My conclusion was that it didn't find all of them, but still found quite a few. Since then, I have played a bit more with this dataset and have come up with better results.

Since a topic modeler will give you the topic proportion per article (I just use the top three), what I tried to do this time was to draw up a table with each class of knowledge, and what the topic modeler identified in terms of topics for each class of knowledge. Before looking at this, it's important to keep in mind that in the sample of articles I used, there are 50 articles per class of knowledge. Therefore, the closer the number of the dominant topic in a class of knowledge gets to 50, the better the topic modeler will have done in identifying the class of knowledge and in reproducing the human classification.

Of course, the classification of articles in the Encyclopédie can be at times a little puzzling. The articles were written by a large number of people and therefore the classification is not always consistent. With that in mind, one should not expect to get perfect matches using a topic modeler. Moreover, since the topic modeler will assume that each article is about N number of topics, the calculation might be further off.
For my experiment, I settled on 107 topics, of which I eliminated 7, which were identified as stopwords lists. When looking at the results of this experiment, there are 41 classes of knowledge in which we find 40 or more articles grouped within the same LDA topic. This means that 41% of the classes of knowledge were identified with a great level of accuracy. If we look at topics that have more than 25 articles matching the same class of knowledge we get up to 83 classes (or 83%).

If we look at those results, there are strange flaws, such as physique and divination that don't seem to be identified. This might be due to a miscalculation, but I have yet to figure out what it could be. Highly specialized classes, such as corroyerie, poésie, or astronomie get excellent matches, which is to be expected.
This experiment also gave us an idea of what the percentage of LDA topics are to be considered as stopwords lists. Between 5 and 10% of the topics should be discarded when using an LDA classifier.
Finally, we should consider that LDA generated topics do not systematically match human identified topics. An unsupervised model is bound to give different results, it would be interesting to see how well supervised LDA (sLDA) would do in our particular test case.

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Do LDA generated topics match human identified topics?

I've been experimenting lately on how LDA generated topics and the Encyclopédie classes of knowledge match. The experiment was conducted in the following way:

- I chose 100 classes of knowledge in the Encyclopédie, and picked 50 articles of each.

- I then ran a first LDA topic trainer choosing 100 topics.

- I then proceeded to identify each generated topic and name after the Encyclopédie classes of knowledge.

- My plan was then to look at the topic proportions per article and see if the top topic would correspond to its class of knowledge. Would the computer manage to classify the articles in the same way the encyclopedists had?

I was not able to get that far when choosing 100 topics for my first LDA run. This is because LDA will always generate a couple topics which aren't really topics, but are just lists of very common words and they just happen to be used in the same documents. Therefore, one should always disregard these topics and focus on the others. What this means is that I had to add a couple more topics to my LDA run in order to get 100 identifiable topics. So I settled with 103 topics. I found 3 distributions of words which were unidentifiable, so I dismissed them.

The results show that LDA topics and the Encyclopédie classes of knowledge do not match (see links to results below). Some do very well, like Artillerie, for which the corresponding distribution of words is :

canon piece poudre artillerie boulet fusil ligne calibre mortier bombe feu charge culasse livre met chambre pouce lumiere roue affut diametre coup batterie levier bouche ame flasque balle tourillon tire

Other distribution of words make sense in themselves but do not match any of the original classes of knowledge. For instance, there is no topic on 'teinture', 'peinture'. What we get instead is a mixture of both classes of knowledge which could be identified as colors :

couleur rouge blanc bleu tableau jaune verd peinture ombre teinture noir toile tableaux nuance papier etoffe bien teint peintre pinceau trait teinturier melange veut figure teindre feuille beau sert colle

Now the topic modeler is not wrong here. It's telling us that these words tend to occur together, which is true. Another significant example is the one with 'Boutonnier', 'Soie', and 'Rubanier' :

soie fil rouet corde brin tour main bouton gauche longueur boutonnier droite attache bout fils tourner sert molette noeud cordon doigt piece emerillon moule broche ouvrage ruban rochet branche aiguille

What we get here is a topic about the art of making clothes, which is more general than 'Boutonnier' or 'Rubanier'.

For this to actually work, the philosophes would have had to have been extremely rigorous in their choice of vocabulary, because this is what LDA expects. Also, another problem is that LDA considers that each document is a mixture of topics, and not made out of one topic. So if one document is exclusively focused on one topic, LDA will still try to extract a certain number of topics out of it. If this is the case, then you are going to get some topics which are mere subdivisions of the class of knowledge in this document. The reason why our experiment broke down could be that the LDA topic trainer created new subdivisions for some classes of knowledge, or regrouped several classes of knowledge. These are all valid as topics, but do not correspond to human identified topics.

Link to results

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back to comparing similar documents

I mentioned a little while ago some work I did on comparing one document with the rest of the corpus it belongs to ( the examples I used in that blog post will not give the same results anymore, the results might not be as good, I haven't optimized the new code for the Encyclopédie yet). The idea behind it was to use the topic proportions for each article generated from LDA, and come up with a set of calculations to decide which document(s) was closest to the original document. The reason why I'm mentioning here once more is that I've been through that code again, cleaned it up quite a bit, improved its performance, tweaked the calculations. Basically, I made it usable for other people but myself. Last time I built a basic search form to use with Encyclopédie articles. This time I'm going to show the command line version, which has a couple more options than the web version.
In the web version, I was using both the top three topics in each document, and their individual proportion within that document. For instance, Document A would have topic 1, 2 and 3 as its main topics. Topic1 would have a proportion of 0.36, Topic2 0.12, Topic3 0.09. In the command line version, there's the option of only using the topics, without the proportion. The order of importance of each topic is of course still respected. Depending on the corpus you're looking at, you might want to use one model rather than the other. It does give different results. One could of course tweak this some more and decide to only take the proportion of the prominent topic, therefore giving it more importance. There is definitely room for improvement.
There was also another option that was left out of the web version. By default, I set a tolerance level, that is the score needed by each document in order to be given as a result of the query. In the command line version, I made it possible to define this tolerance in order to get more or fewer results. This option is currently only possible with the refined model (the one with topic proportions). The code is currently living in
robespierre:/Users/clovis/LDA_scripts/
It's called compare_to_all.pl. There's some documentation in the header to explain how to use it. It's fairly simple. I might do some more work on it, and will update the script accordingly.
There are other applications of this script besides using on a corpus made of well defined documents. One could very well imagine applying this to a corpus subdivided in chunks of text using a text segmentation algorithm. On could then try to find passages on the same topic(s) using a combination of LDA and this script. The Archives parlementaires could be a good test case.
Another option would be to run every document of a corpus against the whole corpus and store all the results in a SQL database. This would allow having a corpus where each document can be linked to various others according to the mixture of topics they are made of.
I will try to give more concrete results some time soon.

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Supervised LDA: Preliminary Results on Homer

While Clovis has been running LDA tests on Encyclopédie texts using the Mallet code, I have been running some tests using the sLDA algorithm. After a few minor glitches, Richard and I managed to get the sLDA code, written by Chong Wang and David Blei, from Blei's website up and running.

Unlike LDA, sLDA (Supervised Latent Dirichlet Allocation), requires a training set of documents paired with corresponding class labels or responses. As Blei suggests, these can be categories, responses, ratings, counts or many other things. In my experiments on Homeric texts, I used only two classes, corresponding to Homer's two major works: the Iliad and the Odyssey. Akin to LDA, topics are inferred from the given texts and a model is made of the data. This model, having seen the class labels of the texts it was trained on, can then be used to infer the class labels of previously unseen data.

For my experiments, I modified the xml versions of the Homer texts that we have on hand using a few simple perl scripts. Getting the xml transformed into an acceptable format for Wang's code required a bit of finagling, but was not too terrible. My scripts first took the xml and split it into books (the 24 books of the Iliad and likewise for the Odyssey), then stripped the xml tags from the text. Saving out four books from each text for applying the inference step, I took the rest of the books and output the corresponding data file necessary for input into the algorithm (data format here).

I played around a bit with leaving out words that occurred extremely frequently or extremely rarely. For the results I am posting here, the English vocabulary was vast and I cut it down to words that occurred between 10 and 60 times. This probably cuts it down too much though, so it would be good to try some variations. Richard has suggested also cutting out the proper nouns before running sLDA in order to focus more on the semantic topics. For the Greek vocabulary, I used the words occurring between 3 and 100 times, after stripping out the accents.

Running the inference part of sLDA on the 8 books that I had saved out seemed to work quite well. It got all 8 correctly labeled as to whether they belonged to the Iliad or to the Odyssey. In a reverse run, the inference was able to again achieve 100 percent accuracy on labeling the 40 books after having been trained on only the 8 remaining books.

The raw results of the trials give a matrix of betas with a column for each word, and a row for each topic. These betas thus give a log based weighting of each word in each topic. Following this are the etas, with a column for each topic and a row for each class. These etas give the weightings of each topic in each class, as far as I understand it. Richard and I slightly altered the sLDA code to output an eta for each class, rather than one less than the number of classes as it was giving us. As far as we understand the algorithm as presented in Blei's paper, it should be giving us an eta for each class. Our modification didn't seem to break anything, so we are assuming that it worked, as the results seem to be looking nice. Using the final model data, I have a perl script that outputs the top words in each topic along with the top topics in each class. These are the results that I am giving below.

Results of my sLDA Experiments on Homer:

English Text: 10 Topics Greek Text: 10 Topics

25 Topics 25 Topics

50 Topics 50 Topics

75 Topics 75 Topics

100 Topics 100 Topics

150 Topics 150 Topics

200 Topics 200 Topics

250 Topics 250 Topics

Also, samples of the output from Blei and Wang's code, corresponding to the English Text with 100 topics:

Final Model: gives the betas and the etas which I used to output my results
Likelihood: the likelihood of these documents, given the model
Gammas
Word-assignments

Inferred Labels: Iliad has label '0', Odyssey has label '1'.
Inferred Likelihood: the likelihood the previously unseen texts
Inferred Gammas

I have not played around much with the gammas, but they seem to give a weighting of each topic in each document. Thus you could figure out for which book of the Iliad or the Odyssey a specific topic was the most prevalent. It would be interesting to see if this correctly pinpoints which book the cyclops comes in for instance, as this is a fairly easily identifiable topic in most of the trials.

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Classifying the Echo de la Fabrique

I've been working lately on trying to classify the Echo de la Fabrique, a 19th century newspaper, using LDA. The official website is located at http://echo-fabrique.ens-lsh.fr/. The installation I used is strictly meant for experimentation on topic modeling.
The dataset I used is significantly smaller than the Encyclopédie, which means that the algorithm has fewer articles with which to generate topics. This makes the whole process trickier since choosing the right number of topics suddenly becomes more important. I suspect that adding more articles to this dataset will yield better results. I settled for 55 topics, and found a name corresponding to the general idea conveyed by each distribution of words. I then proceeded to add those topics to each tei file and loaded it into philologic. I chose to include 4 topics per article, or fewer if topics didn't reach the mark of 0.1.
The work I've done so far on LDA has already shown several things about its accuracy in generating meaningful topics and in properly classifying text. It tends to work really well with topics that are concept driven. For instance, in the Echo de la Fabrique , the topic 'justice' works really well. Same thing goes with 'Hygiène' associated with words like 'choléra' or 'eau'. On the other hand, there are some distribution of words which were not identifiable as topics. Those topics have been marked as 'Undetermined' with a number such as 'Undetermined1' to distinguish each undetermined topic. And then, there are also topics like 'Petites annonces' or 'Misère ouvrière ' which are not as concept driven, and therefore are subject to more inaccuracies. Once again, I believe that having more articles from the same source would partially improve this problem : more documents, more training for the topic modeler, reduced dependency on concepts.
Each topic has a number attached to it. This number represents the importance of the topic for each article. To get the most prominent topic, search for e.g. 'justice 1', 'justice 2' for the second topic, 'justice 3' for the third topic, and 'justice 4' for the fourth topic. If you want a search for all four, just type 'justice'. Note that the classification tends to be more accurate with the first topic than with the other three, but that 's not always the case.
Anyway, without further ado, here is the search form:
https://artflsrv03.uchicago.edu/philologic4/echofabrique/ (Update: this is under PhiloLogic4 and has only Topics 1 enabled at this time.)
Please let me know if you have any comments, suggestions. Any feedback is much appreciated.

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Finding related articles using topic modeling

While still working on the topic inferencer, I started hacking at another possibility which is made possible by topic modeling, that is finding closely related texts within a corpus. There are several ways of doing this. What I chose to do was to consider the top three topics in each article and their respective proportion, and weigh it against the whole corpus. Here's a link to a search form where you can search for similar articles in the Encyclopedie :
http://robespierre.uchicago.edu/topic_modeling/search.form.html
In order to use it, you should paste the url of the article you're looking at. You'll then get a list of links to various articles that should be similar in content to the one you selected. A lot of tinkering can be done with the calculation of similarity, therefore I very well might have made some bad jugement here and there. This is therefore work in progress, therefore you might get strange results. But if you go through the whole list of results you might see some interesting things.
I would like to give you two examples I've tried that work really well. The first one is the article Economie by Rousseau ( which gives very good results), and if you look at link 24, which is according to my (flawed) calculation the 24th closest article, you'll see an example of an article that would have been hard to find and link to Rousseau. The second example is Question by Jaucourt. Among the top 20, a lot concern various methods of torture, spread out in different classes of knowledge. Let me know what you think.

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Topic inference using the Encyclopédie trained model

While trying to use the Encyclopédie trained topic model on the Mémoires de Trévoux, something quite unexpected happened, the topic modeler was finding it hard to find topics that matched the Trévoux articles. You can see those results here:
http://robespierre.uchicago.edu/topic_modeling/inference/encyclo2trevoux.txt
Since the topic inference feature in mallet is relatively new, I though of creating a model out of the Trévoux, and then compare the topic proportion generated from the topic trainer with the one generated using the model. So basically, I tested the model against the corpus of articles from which it originated. In all likelihood, the results were going to be excellent. Well, they weren't, therefore showing that the topic inferencer is not yet operational (it is a new feature after all). On the other hand, I did notice something, that if you compare the results, you'll notice that the same topics (mostly) are prominent in both, only the proportion measure is off, approximately divided by ten when using topic inference. Here are those results:
when using topic training:
http://robespierre.uchicago.edu/topic_modeling/inference/proportions.txt
when using topic inference:
http://robespierre.uchicago.edu/topic_modeling/inference/proportions_itself.txt
The question is, can I trust those results. My initial analysis tends to show that it does work, but it's definitely not as accurate as the first experiments I did with topic modeling. Some more digging is needed, eventually getting in touch with the Mallet developers.

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Proportions of topics in Encyclopédie articles

This is a follow-up to my previous blog entry about topic modeling in the Encyclopédie. As the title of this post suggests, I will be showing here the proportions of topics per article. Instead of just posting those results without any further comment, I would like to focus on 12 random articles to see what kind of results one could get. My feeling about this is that the best results are in the 300 topic model. What do you think? Note that there is still a lot of room for some refinement.

Examples from the 42 topic model :
http://docs.google.com/View?id=dgrbcw9z_69gk9w5tgc
Examples from the 100 topic model:
http://docs.google.com/View?id=dgrbcw9z_70c2n79kgv
Examples from the 150 topic model:
http://docs.google.com/View?id=dgrbcw9z_71cx73tsch
Examples from the 200 topic model:
http://docs.google.com/View?id=dgrbcw9z_724t5x9mfm
Examples from the 250 topic model:
http://docs.google.com/View?id=dgrbcw9z_73fvznkb7j
Examples from the 300 topic model:
http://docs.google.com/View?id=dgrbcw9z_74chqfgsct
Examples from the 350 topic model:
http://docs.google.com/View?id=dgrbcw9z_75chsw8gcp

If you wish to look yourself at the results, here they are, the first number is the topic with the proportion measure in parentheses. The article number is the div number of the article :

http://robespierre.uchicago.edu/topic_modeling/topics_in_articles_42.txt
http://robespierre.uchicago.edu/topic_modeling/topics_in_articles_100.txt
http://robespierre.uchicago.edu/topic_modeling/topics_in_articles_150.txt
http://robespierre.uchicago.edu/topic_modeling/topics_in_articles_200.txt
http://robespierre.uchicago.edu/topic_modeling/topics_in_articles_250.txt
http://robespierre.uchicago.edu/topic_modeling/topics_in_articles_300.txt
http://robespierre.uchicago.edu/topic_modeling/topics_in_articles_350.txt

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Preliminary results on topic modeling in the Encyclopédie

Following up on Mark's comments on topic modeling using Latent Dirichlet Allocation, or LDA, I went on to explore some implementations of this algorithm to see what type of results we would get on some of the data sets we have. I first started using David Blei's code, but it ended being to complex to use, the documentation was very elusive. So I starting to look at another tool, Mallet, which also includes an implementation of LDA.
Here are the first results I've come up with when running it against the Encyclopédie. The main issue when using topic modeling is, as described in this article, coming up with the right number of topics as the results differ quite a bit depending on this number. I haven't quite settled yet for a particular number. Below are the topics I've come up with. Let me know what you think, which version(s) seems the more accurate. I would argue that the question comes down to how focused do we want each topic to be, or how broad do we want those topics to be without losing any accuracy. Please let me know if there are some words you think I could eliminate (less noise, more accuracy). Several hints would be useful, such as pinpointing a topic that doesn't make sense, a word that seems out of place somewhere (probably some noise to be eliminated during another run). Note that the list of words that I delete from the articles (so far a little over 300) could very well be used for other 18th century French texts, if not for different periods from 1650 to today with some tweaks here and there. Thanks.

Version with 42 topics:
http://robespierre.uchicago.edu/topic_modeling/42topics-encyclo.txt
Version with 100 topics:
http://robespierre.uchicago.edu/topic_modeling/100topics-encyclo.txt
Version with 150 topics:
http://robespierre.uchicago.edu/topic_modeling/150topics-encyclo.txt
Version with 200 topics:
http://robespierre.uchicago.edu/topic_modeling/200topics-encyclo.txt
Version with 250 topics:
http://robespierre.uchicago.edu/topic_modeling/250topics-encyclo.txt
Version with 300 topics:
http://robespierre.uchicago.edu/topic_modeling/300topics-encyclo.txt
Version with 350 topics:
http://robespierre.uchicago.edu/topic_modeling/350topics-encyclo.txt

These results are just the preliminary step. The interesting part is the topics proportions per document. I'll show some results in another post.

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Fast Latent Dirichlet Allocation

Porteous, Ian, David Newman, Alexander Ihler, Arthur Asuncion, Padhraic Smyth, and Max Welling. "Fast collapsed gibbs sampling for latent dirichlet allocation." KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, USA: ACM, 2008, 569-577. (Link)

This describes Fast LDA and suggests that this may be helpful in "real time" topic modeling of a few thousand documents returned by a search engine. The introduction to section 3 gives a nice "intuitive" description of LDA, helpful for those, like me, who are significantly math challenged, as well as some algorithm descriptions. The paper has links to code and David Newman has posted links to some earlier code which may be of considerable interest. Newman has done some interesting work on topic modeling of 18th century American newpapers (link and link).

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Dynamic Topic Models

I just had a look at David Hall, Daniel Jurafsky, and Christopher Manning. "Studying the History of Ideas Using Topic Models." Proceedings from the EMNLP 2008: Conference on Empirical Methods in Natural Language Processing. October 2008. [link] This is a very interesting article, using Latent Dirichlet Allocation [link wikipedia] and some extensions, examining changing publication trends in computational linguistics. As noted on the Wikipedia entry, this approach [LDA] is described in David Blei, Andrew Y. Ng, and Michael I. Jordan. "Latent Dirichlet Allocation." Journal of Machine Learning Research 3 (January 2003) [link]. David Blei has released code [link] and has a number of samples, a listserv, etc. on his site. He also gave a great presentation of his work as a Google talk "Modeling Science: Dynamic Topic Models of Scholarly Research" in May 2007 [link video and paper]. This appears to be a powerful technique, which has the ability to handle changing vocabularies over a century of scientific writing.

In trying to run it on OS-X, I am able to currently get topics for the sample AP collection provided by Blei, but not able to get inferences as it throws malloc errors. I'm looking at the mailing list to see if there are any hints about OS-X.

Blei lists several implementations on his site, including one part of Mallet, which I think we installed here at one point. See also http://gibbslda.sourceforge.net/
for another implementation and some samples run on large Wikipedia and Medline (abstract) collections.

Also noticed a Ruby module described at
http://mendicantbug.com/2008/11/17/lda-in-ruby/

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