Natural Language Morphology Queries in Perseus

Natural language queries are now possible on Perseus under Philologic. Previously, Richard had implemented searching for various parts of speech in various forms. For instance, as noted in the About page for Perseus, a search for 'pos:v*roa*' will return all the instances of perfect active aorist verbs in the selected corpus. Now, a search for 'form:could-I-please-have-some-perfect-active-optatives?' will return the same results. In fact, searching for 'form:perf-act-opt', 'form:perfect-active-optative', 'form:perfection-of-action-optimizations', or 'form:perfact-actovy-opts-pretty-please' will all accomplish this same task. Note that the dashes are necessary between the words, otherwise a search for plural nouns written as 'form:plural nouns' will actually be searching for any plural word followed by the word "nouns", which will fail. I carefully chose shorter forms of all the keywords, such as "impf" and "ind" for "imperfect" and "indicative" so that a search including any word starting with "ind" will match indicatives regardless of what follows the 'd'. Hopefully, there are no overlapping matches (such as using "im" to abbreviate "imperfect" which would also match "imperative"). If you do encounter any, please let me know. Potentially, we could put a list of acceptable abbreviations somewhere, although they are fairly straightforward and typing the full term out is always a fail-safe method.

Basically, the modified crapser script simply translates searches beginning with "form:" into the corresponding "pos:" search. Using a hash of regular expressions and string searching, it simply returns the corresponding code. In the previous example, the search is actually looking for "pos:....roa..". Notice that it fills in the empty space of the code with dots, allowing them to be anything. I implemented an alternative filler, the dash, so that when you search for something like "form:perf-act-opt-exact", you will actually be searching for "pos:----roa--" (and your search will fail because there are no terms that are only and exactly perfect active optative without other specifications).

One limitation that this method of natural language querying has is that it cannot match the versatility of the "pos:" searches. That is, because it selects either dots or dashes as fillers, you cannot get a mixture of them in your search. You cannot run a search such as "pos:v-.sroa---". However, this limitation will likely have little effect for the average user and the user needing such a search can still obtain it using the "pos:" method. An alternative method involving drop down input boxes for each slot of the code would enable the full power of the pos searches, but it would also be potentially more tedious to implement and potentially tedious to use as well. Such a input form would require the user to know more about the encoding than the "form:" searching I implemented does. For example, a user would need to know that "verb" is required in the first slot, even if "aorist optative" makes that the only possibility. Whereas searching for 'form:aorist-optative' works without the user ever needing to know that a 'v' is required in the first slot.

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Encyclopédie: Similar Article Identification II

After doing a series of revisions as part of my last post this subject (link), I thought it might be helpful to provide an update posting. We have been interested in teasing out how the VSM handles small vs large articles and to get some sense of why various similar articles are selected. Over the weekend, I reran the vector space similarity function on 39,218 articles, taking some 29 hours. I excluded some 150 surface forms of words in a stopword list, all sequences of numbers (and roman numerals), as well as features (in this case word stems) found in more than 1568 and less than 35 articles. This last step removed features like blanch, entend, mort, and so on. Thus, I removed some 600 features, leaving 10,157 features used for the calculation. Here is the search form:

Headword: (e.g. tradition)
Author: (e.g. Holbach)
Classification: (e.g. Horlogerie)
English Class: (e.g. Clockmaking)
Size (words): (e.g. 250- or 250-1000)
Show Top: articles (e.g. 10 or 50)

The number of matching terms for small articles can be, of course, very small. For example, article "Tout-Bec" (62 words) is left with four stems [amer 1|oiseau 2|ornith 1|bec 3]. The first most of the most similar articles is Rhinoceros (Hist. nat. Ornith.) -- remember, only the main article here -- matches on three stems:

word               frq1     frq2
bec                 3        5
oiseau              2        2
ornith              1        1

Are these similar? Well, both very small articles refer to kinds of rare birds that are notable by their beaks, one with a very large beak and one that looks like it has two or more beaks. It is also important to note that "ornith" (the class of knowledge) in both is picked up by this example. The next article down (Pipeliene) matches on:

amer                1        1
bec                 3        1
oiseau              2        2

The third most similar in this example is "Connoissance des Oiseaux par le bec & par les pattes.", a plate legend, with as you expect, lots of beaks. This matches on two stems, bec and oiseau.

It seems that the size of the query article, now that I have eliminated many function words and other extraneous data, carries a significant impact. The larger the article, the more possible matches you will get (Zipf's Law applies). Longer articles will tend to be most similar to other longer articles, and shorter will match better to shorter. So, similarity would appear to be a function of relative frequencies of common features and the length of the articles. We saw this in our original examination of the Encyclopédie and the Dictionnaire de Trévoux, and had built in some restrictions in terms of size as well as comparing articles with the same first letter rather than all to all. As far as I can tell, the kind of more of feature pruning shown here does not have a significant impact on larger articles.

User feedback might be significant in determining just how many features and what kinds of features are required to get more interesting matches. For any pair, we could store the VSM score, the sizes, and the matching features along with the user rating of the match. That might generate some actionable data for future applications.

[Aside: In some cases, similar passages lead to possibly related plates and legends. Cadrature, for example, links to numerous plate legends dealing with clockmaking.]

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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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Index Design Notes 1: PhiloLogic Index Overview

I've been playing around with some perl code in response to several questions about the structure of PhiloLogic's main word index--I'll post it soon, but in the meantime, I thought I'd try to give a conceptual overview of how the index works. As you may know, PhiloLogic's main index data structure is a hash table supporting O(1) lookup of any given keyword. You may also know that PhiloLogic only stores integers in the index: all text objects are represented as hierarchical addresses, something like a normalized, fixed-width Xpointer.

Let's say we can represent the position of some occurrence of the word "cat" as
0 1 2 -1 1 12 7 135556 56
which could be interpreted as
document 0,
book 1,
chapter 2,
section ,
paragraph 1,
sentence 12,
word 7,
byte 135556,
page 56, for example.

A structured, positional index allows us to evaluate phrase queries, positional queries, or metadata queries very efficiently. Unfortunately, storing each of these 9 numbers as 32-bit integers would take 36 bytes of disk space, for every occurence of the word. In contrast, it's actually possible to encode all 9 of the above numbers in just 39 bits, if we store them efficiently--that's a 93% saving. The document field has the value 0, which we can store in a single bit, whereas byte position, our most expensive, can be stored in just 18 bits. The difficulty being that the simple array of integers becomes a single long bit string stored in a hash. First we encode each number in binary, like so
0 1 01 11 1 0011 111 001000011000100001 000111

but this is only 18 bits, so we have to pad it off with 6 extra bits to get an even byte alignment, and then we can store it in our hash table under "cat".

Now, suppose that we use somthing like this format to index a set of small documents with 10,000 words total. We can expect, among other things, a handful of occurrences of "cat", and probably somewhere around a few hundred occurrences of the word "the". In a GDBM table, duplicate keywords aren't permitted--there can be exactly one record of "cat". For a database this size, it would be feasible to append every occurrence into a single long bit string Let's say our text structures require 50 bits to encode, and that we have 5 occurrences of cat. We look up "cat" in GDBM, and get a packed bit string 32 bytes, or 256 bits long. we can divide that by the size of a single occurrence, so we know that we have 5 occurrences and 6 bits of padding.

"The", on the other hand, would be at least on the order of few kilobytes, maybe more. 1 or 2 K of memory is quite cheap on a modern machine, but as your database scales into the millions of words, you could have hundreds of thousands, even millions of occurrences of the most frequent words. At some point, you will certainly not want to have to load megabytes of data into memory at once for each key-word lookup. Indeed, in a search for "the cat", you'd prefer not to read every occurrence of "the" in the first place.

Since PhiloLogic currently doesn't support updating a live database, and all word occurrences are kept in sorted order, it's relatively easy for us to devise an on-disk, cache-friendly data structure that can meet our requirements. Let's divide up the word occurences into 2-kilobyte blocks, and keep track of the first position in each block. Then, we can rapidly skip hundreds of occurrences of a frequent word, like "the", when we know that the next occurence of "cat" isn't in the same document!

Of course, to perform this optimization, we would need to know the frequency of all terms in a query before we scan through them, so we'll have to add that information to the main hash table. Finally, we'd prefer not to pay the overhead of an additional disk seek for low-frequency words, so we'll need a flag in each key-word entry to signal whether we have:
1) a low frequency word, with all occurences stored inline
or
2) a high frequency word, stored in the block tree.

Just like the actual positional parameters, the frequencies and tree headers can also be compressed to an optimal size on a per-database level. In philologic, this is stored in databasedir/src/dbspecs.h, a c header file that is generated at the same time as the index, then compiled into a custom compression/decompression module for each loaded database, which the search engine can dynamically load and unload at run time.

In a later post, I'll provide some perl code to unpack the indices, and try to think about what a clean search API would look like.

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Encyclopédie: Similar Article Identification

The Vector Space Model (VSM) is a classic approach to information retrieval. We integrated this as a standard function in PhiloMine and have used it for a number of specific research projects, such as identifying borrowings from the Dictionnaire de Trévoux in the Encyclopédie, which is described in our forthcoming paper "Plundering Philosophers" and related talks[1]. While originally developed by Gerard Salton[2] in 1975 as a model for classic information retrieval, where a user submits a query and gets results in an ranked relevancy list, the algorithm is also very useful to identify similar blocks of text, such as encyclopedia articles or other delimited objects. Indeed, this kind of use of the VSM was proposed by Salton and Singhal[3] in a paper presented months before Salton's death. They demonstrated the use of VSM to produce links between parts of documents, forming a type of automatic hypertext:

The capability of generating weighted vectors for arbitrary texts also makes it possible to decompose individual documents into pieces and explore the relationships between these text pieces. [...] Such insights can be used for picking only the "good" parts of the document to be presented to the reader.

Salton and Singhal further argued that manual link creation would be impractical for huge amounts of text, but these conclusions may have had limited influence given the general interest at that time in human generated hypertext links on the WWW.

Based on earlier work using PhiloMine, we have seen a number of "interesting" -- and at times unexpected -- connections between articles in the Encyclopédie, often drawing connections between previously unrelated articles, if by unrelated we mean having different authors, classes of knowledge and few cross-references (renvois) between them. One might consider this kind of similarity measure between articles as a kind of intertextual discovery tool, where the system would propose articles possibly related to a specific article.

The Vector Space Model functions by comparing a query vector to all of the vectors in a corpus, making it an expensive calculation, not always suitable to real time use. In this experiment, I have recast the VSM implementation in PhiloMine to function as a batch job to generate a database of 27,753 Encyclopédie articles (those with 100 or more words) with the 20 most similar articles for each article. To do this, I pruned features (word stems) which more than 8,325 and less than 41 articles, resulting in a vector size of 10,431 features. I used a standard French word stemmer to reduce lexical variation and a Log Normalization function to handle variations in article sizes. The task took about 17 hours to run.

Update (December 7): I have replaced the VSM build above with the same on 39,200 articles -- all articles with 60 or more words -- which took about 29 hours to run. I pruned features found in more than 11,200 documents and less than 50, leaving 9,710 features. This may change some results by adding more small articles. Note, this is about as large a VSM task as can be performed in memory using perl hashes, since anything large runs out of memory. If we want to go larger, probably store vectors on disk and TIE them to perl hashes.

The results for a query shows the 20 most similar articles, ranked by the similarity score, where an exact match is equal to 1. For example, the article OUESSANT (Modern Geography) -- based on 27,000 articles -- is related to the articles VERTU [0.274], Luxe [0.267], ECONOMIE ou OECONOMIE [0.265], POPULATION [0.263], CHRISTIANISME [0.261], SOCIÉTÉ [0.256], AVERTISSEMENT DES ÉDITEURS (suite) [0.255], MANICHÉISME [0.254], CYNIQUE, secte de philosophes anciens [0.254], Gout [0.250], EDUCATION [0.248] and so on. This reflects the discussion of the moral conditions of the inhabitants of the small island off the coast of Brittany.

You can give it a try using this form (again now for 39,200 articles):

Headword: (e.g. tradition)
Author: (e.g. Holbach)
Classification: (e.g. Horlogerie)
English Class: (e.g. Clockmaking)
Size (words): (e.g. 250- or 250-1000)
Show Top: articles (e.g. 10 or 50)

[Dec 9: I added word count info for each article. You can restrict searches to articles in ranges of size. Also, now storing 50 top matches, which you can limit. Showing matching articles which are smaller than source article. Dec 10: added function to display matching stems for any pairwise comparison for inspection.]

There are a number of other options that I might add to the VSM calculations, including using TF-IDF as an alternative normalization weighting scheme and use of virtual normalization to again reduce lexical variations and improve the performance of the stemming algorithm. I have also thought of using Latent Semantic Analysis as another way to handle similarity weighting, but given that we have many query terms, it is not clear that LSA would help all that much.

In a real production environment, I think we will add a "similar article link" from articles in the Encyclopédie. We have talked about having users rank the quality of the similarity performance. The scores assigned are somewhat helpful in ranking, but not in assessing an absolute number, since they can vary by the size of the input article. VSM is an unsupervised learning model. It is not clear to me that we could integrate user evaluations in any systematic fashion, but this is certainly an interesting subject of further consideration.

As always, please let me know what you think. I have a couple of general queries. I have used main and sub articles (as well plate legends, etc.) as units of similarity calculation. Should I use main entries only? I also limited this to articles with more than 100 words. At 50 words, we have some 43,000 articles. Should I do this for a full implementation?

References

[1] See Timothy Allen, Stéphane Douard, Charles Cooney, Russell Horton, Robert Morrissey, Mark Olsen, Glenn Roe, and Robert Voyer, "Plundering Philosophers: Identifying Sources of the Encyclopédie", Journal of the Association for History and Computing (forthcoming 2009). Also, see Ceglowski, Maxiej. 2003: "Building a Vector Space Search Engine in Perl", Perl.com [http://www.perl.com/pub/a/2003/02/19/engine.html].

[2] Salton, G., A. Wong, and C. S. Yang. 1975: "A Vector Space Model for Automatic Indexing," Communications of the ACM 18/11: 613-620.

[3] Singhal, A. and Salton, G. 1995: "Automatic Text Broswing Using Vector Space Model" in Proceedings of the Dual-Use Technologies and Applications Conference 318-324.

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Frequencies in the Greek and Latin texts

Earlier this year Mark built a frequency query for the French texts (affectionately named wordcount.pl)

Kristin has now implemented this for our Greek and Latin texts. If you wonder what's new about this: Word count for individual documents has always been there in PhiloLogic loads, but the difference here is that you can see frequencies over the entire corpus, or a subset of works/authors.

You can find the forms here:
http://perseus.uchicago.edu/LatinFrequency.html
http://perseus.uchicago.edu/GreekFrequency.html

Update: Forms moved to the 'production site', perseus.uchicago.edu. You can now specify genre as well. Stay tuned for further stats, meant to provide a friendly reminder of Zipf's Law.

Note: the counts are raw frequency counts, without lemmatization.

I have edited the search form a tiny bit - let me know if you encounter any problems.

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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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Section Highlighting in Philologic

In many of the Perseus texts currently loaded under philologic, the section labels would overlap and be unreadable. These labels come from the milestone tags in the xml text and are placed along the edge of the text. One particularly problematic text in this regard was the New Testament, as the sections were verses and were thus often small sections of text.

In order to fix the overlapping issue, I wrote a little bit of javascript to hide the tags which would be placed in the same position as a previous tag. I also added a function to recalculate this if the window is resized. My main function is fairly simple:

function killOverlap (){
$lastOffset = 0;
$(".mstonecustom").each(function (i) {
if (this.offsetTop == $lastOffset){
this.className = "mstonen2";
}
else {
$lastOffset = this.offsetTop;
}});}

I also added a function which highlights a section when you hover over its milestone label along the side of the text. This seems useful to me, as often it is helpful to know where a section starts and ends. This was a slightly more complex problem. I had to alter the citequery3.pl script in order to add a span tag and some ids in order to get the javascript to work. The javascript was then fairly simple:

function highlight(){
$(".mstonecustom").hover(
function () {
myid = jq("text" + $(this).attr('id'));
$("w", myid).css({"font-weight" : "bolder"});},
function () {
myid = jq("text" + $(this).attr('id'));
$("w", myid).css({"font-weight" : "normal"});})}

In order for it to work though, you have to alter the citequery3.pl script with this:

my $spanid = $citepoints{$offsets[$offset]};
$spanid =~ s/.*\.([0-9]+)\.([0-9]+)$/a$1b$2/;
#...
$tempstring =~ s/(^<[^>]+>)/$1<span class="mstonecustom" id="$spanid">$citepoints{$offsets[$offset]}<\/span>/;
#... {
$tempstring =~ s/<span class="mstonecustom" id="$spanid">$citepoints{$offsets[$offset]}<\/span>//;}

$milesubstrings[$offset] = "<span class=" . $citeunits{$offsets[$offset]} . " id="text">" . $tempstring . "<\/span>";

That's about it. It may come in useful again someday. For an example, take a look at this.

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Towards PhiloLogic4

Earlier this year I wrote a long discussion paper called "Renovating PhiloLogic" which provided an overview of the system architecture, a frank review of the strengths and (many) failings of the current implementation of the 3 series of PhiloLogic, and proposed a general design model for what would effectively be a complete reimplementation of the system, retaining only selected portions of the existing code base. While we are still discussing this, often in great detail, a few general objectives for any future renovation have emerged, including:

• service oriented architecture;
• release of new system in perl module libraries;
• multiple database query support, and,
  
• options for advanced or extended indexing models.

I will be putting together a public version of this discussion draft in the near future and will blog it when I have something ready.

Before sallying forth to do start working on a PhiloLogic4, there are a number of preliminary steps that Richard and I agree are required in order to 1) support the existing PhiloLogic3 series, and 2) clear the existing (messy) code base of some of the most egregious sections of the system, most notably the loader. Some of these are simply housekeeping and updates, some of these are patches and bug fixes, and some others are clean-ups which should streamline the current system and help in any redevelopment.

We will start by retasking one of our current machines, a 32 bit OS-X installation, to be the primary PhiloLogic development machine. We will also get the Linux branch on a 32 bit Linux machine (flavor to be determined). There is a known 64 bit installation problem which we will address at the end of this initial process. When we reach the right step, we will install it all on 64 bit machines and fix it then, hopefully with much less effort on a streamlined version, while releasing upgraded 32 bit versions on the way. The other element for our consideration is the degree to which we can merge the OS-X and Linux branches of the system. Right now, we have two completely distinct branches. It would be much better to have one, which we think may be accomplished in a couple of different ways.

We are currently thinking of 4 distinct steps, which should each result in new maintenance releases of PhiloLogic3.

Step One

Apply the most recent OS-X Leopard patch kit to both the OS-X and Linux branches as required and feasible. This is the patch kit that Richard and I assembled for the migration to our new servers and has some nifty little extensions. We will also be updating the PhiloLogic code release site (Google Code) and retooling the new PhiloLogic site, which will then be referred from the existing location (philologic.uchicago.edu). Maintenance release when done. [MVO]

Step Two

The PhiloLogic loader currently using a GNU Makefile scheme to load databases. This made good sense many years ago, when loads could take many hours (or days), but is probably no longer needed. There are also many places where we use various utilities (sed, gawk, gzip, etc.) which add complications and make the entire scheme more brittle. Our current thinking is to fold all of the Makefile functions into a revised version of philoload, but may determine a better way to proceed once we get into it. We're planning a maintenance release of this when done. [MVO]

Step Three

The current PhiloLogic loader performs a number of C compiles, many of which are no longer needed. For example, the system still compiles the search2 binaries. These were left in Philologic3 in order to have backwards compatibility. We need to keep the ability to generate the correct pack and unpack libraries which are used by search3. Once we have cleared out all unnecessary C compiles, we will investigate a couple of known bugs in search3, and attempt to resolve these. Again, once done, we would do a maintenance release. [RW and MVO]

Step Four

As noted above, some users have reported 64 bit compile problem on either installation or load. Once we have the loader streamlined, eliminating as much of the old C compiles are possible, we will investigate this problem. We're hoping that this will be easily remedied and, even better, could be resolved in a combined release which would merge the current OS-X and Linux branches. This would be the terminal release of the PhiloLogic3 series. Any future releases would be only for bug fixes.

We hoping that these steps will result in a stable terminal release of the PhiloLogic3 series, which will be easier to install and use. It will also result in significant streamlining which will help in any future Philologic renovation or a new PhiloLogic4 series.

This is an initial plan, so please do post your comments, suggestions, and complaints.

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Encyclopédie under KinoSearch

One of the things that I have wanted to do for a while is to examine implementations of Lucene, both as a search tool to complement PhiloLogic and possibly as a model for future PhiloLogic renovations. Late this summer, Clovis identified a particular nice open source, perl implementation of Lucene called KinoSearch. This looks like it will fit both bills very nicely indeed. As a little experiment, I loaded 73,000 articles (and other objects) from the Encyclopédie, and cooked up a super simple query script. This allows you to type in query words and get links to articles sorted by their relevancy to your query (the italicized number next to the headword). At this time, I am limiting to the top 100 "hits". Words should be lower case, accents are required, and words should be separated by spaces. Try it:

Query Words: or
Require all words

Here are a couple of examples which you can block copy in:
artisan laboureur ouvrier paysan
malade symptome douleur estomac
peuple pays nation ancien république décadence

The first thing to notice is search speed. Lucene is known to be robust, massively scalable, and fast. The KinoSearch implementation is certainly very fast. A six term search returns in a real .35 seconds and less than 1/10 of a second of system time, using time on the command line. I did not time the indexing run, but think 10 minutes or so. [Addition: by reading 147 TEI files rather than 77,000 split files, the loading indexing time for the Encyclopédie is falls to (using time) real 2m45.9s, user 2m33.8s sys 0m11.1s.]


The KinoSearch developer, Marvin Humphrey, has a splendid slide show, outlining how it works, with specific reference to the kind of parameters, such as stemmers and stopwords, that one needs to consider as well as an overview of the indexing scheme. Clovis and I thought this might be the easiest way to begin working with Lucene, since it is a perl module with C components, so it is easy to install and get running. Given the performance and utility of KinoSearch, I suspect that we will be using it extensively for projects where ranked relevancy results are of interest. These might include structured texts, such as newspaper and encyclopedia articles, and possibly large collections of uncorrected OCR materials which may not suitable for text analysis applications supported by PhiloLogic. Also, on first review, the code base is very nicely designed and, since it has many of the same kinds of functions as PhiloLogic, strikes me as being a really fine model of how we might want to renovate PhiloLogic.

For this experiment, I took the articles as individual documents in TEI, which Clovis had prepared for other work. For each article, I grabbed the headword and PhiloLogic document id, which are loaded as fielded data. The rest of the article is stripped of all encoding and loaded in. It would be perfectly simple to read the data from our normal TEI files. We could see simply adding a script that would load source data from a PhiloLogic database build, to add a different kind of search, which would need to have a different search box/form.

I have not played at all with parameters and I can imagine that we would want to perform some functions, such as using simple rules for normalization, on input, since it uses a stemmer package also by M Humphrey. Please email me, post comments, or add a blog entry here if you see problems, particularly search oddities, have ideas about other use cases, or more general interface notions. I will be writing a more generalized loader and query script -- with paging, numbers of hits per page, filtering by minimum relvancy scores and looking at a version of the Philologic object fetch which would try to high-light matching terms -- and moving that over to our main servers.

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