I'm trying to figure out what are the ways (and which of them the best one) of extraction of Values for predefined Keys in the unstructured text?
Input:
The doctor prescribed me a drug called favipiravir.
His name is Yury.
Ilya has already told me about that.
The weather is cold today.
I am taking a medicine called nazivin.
Key list: ['drug', 'name', 'weather']
Output:
['drug=favipiravir', 'drug=nazivin', 'name=Yury', 'weather=cold']
So, as you can see, in the 3d sentence there is no explicit key 'name' and therefore no value extracted (I think there is the difference with NER). At the same time, 'drug' and 'medicine' are synonyms and we should treat 'medicine' as 'drug' key and extract the value also.
And the next question, what if the key set will be mutable?
Should I use as a base regexp approach because of predefined Keys or there is a way to implement it with supervised learning/NN? (but in this case how to deal with mutable keys?)
You can use a parser to tag words. Your problem is similar to Named Entity Recognition (NER). A lot of libraries, like NLTK in Python, have POS taggers available. You can try those. They are generally trained to identify names, locations, etc. Depending on the type of words you need, you may need to train the parser. So you'll need some labeled data also. Check out this link:
https://nlp.stanford.edu/software/CRF-NER.html
I want to store arbitrary key value pairs. For example,
{:foo "bar" ; string
:n 12 ; long
:p 1.2 ; float
}
In datomic, I'd like to store it as something like:
[{:kv/key "foo"
:kv/value "bar"}
{:kv/key "n"
:kv/value 12}
{:kv/key "p"
:kv/value 1.2}]
The problem is :kv/value can only have one type in datomic. A solution is to to split :kv/value into :kv/value-string, :kv/value-long, :kv/value-float, etc. It comes with its own issues like making sure only one value attribute is used at a time. Suggestions?
If you could give more details on your specific use-case it might be easier to figure out the best answer. At this point it is a bit of a mystery why you may want to have an attribute that can sometimes be a string, sometimes an int, etc.
From what you've said so far, your only real answer it to have different attributes like value-string etc. This is like in a SQL DB you have only 1 type per table column and would need different columns to store a string, integer, etc.
As your problem shows, any tool (such as a DB) is designed with certain assumptions. In this case the DB assumes that each "column" (attribute in Datomic) is always of the same type. The DB also assumes that you will (usually) want to have data in all columns/attrs for each record/entity.
In your problem you are contradicting both of these assumptions. While you can still use the DB to store information, you will have to write custom functions to ensure only 1 attribute (value-string, value-int, etc) is in use at one time. You probably want custom insertion functions like "insert-str-val", "insert-int-val", etc, as well as custom read functions "read-str-val" etc al. It might be also a good idea to have a validation function that could accept any record/entity and verify that exactly one-and-only-one "type" was in use at any given time.
You can emulate a key-value store with heterogenous values by making :kv/key a :db.unique/identity attribute, and by making :kv/value either bytes-typed or string-typed and encoding the values in the format you like (e.g fressian / nippy for :db.types/bytes, edn / json for :db.types/string). I advise that you set :db/index to false for :kv/value in this case.
Notes:
you will have limited query power, as the values will not be indexed and will need to be de-serialized for each query.
If you want to run transaction functions which read or write the values (e.g for data migrations), you should make your encoding / decoding library available to the Transactor as well.
If the values are large (say, over 20kb), don't store them in Datomic; use a complementary storage service like AWS S3 and store a URL.
Now I have a file recording the entries of a lookup table. If the number of entries is small, I can simply load this file into an STL map and perform search in my code. But what if there are many many entries? If I do it in the way above, it may cause error such as out of memory. I'm here to listen to your advice...
P.S. I just want to perform search without loading all entries into memory.
Can Key-value database solve this problem?
You'll have to load the data from hard drive eventually but sure if a table is huge it won't fit into memory to do a linear search through it, so:
think if you can split the data into a set of files
make an index table of what file contains what entries (say the first 100 entries are in "file1_100", second hundred is in "file101_201" an so on)
using index table from step 2 locate the file to load
load the file and do a linear search
That is a really simplified scheme for a typical database management system so you may want to use one like MySQL, PostgreSQL, MsSQL, Oracle or any one of them.
If that's a study project then after you're done with the search problem, consider optimizing linear operations (by switching to something like binary search) and tables (real databases use balanced tree structures, hash tables and like).
One method would be to reorganize the data in the file into groups.
For example, let's consider a full language dictionary. Usually, dictionaries are too huge to read completely into memory. So one idea is to group the words by first letter.
In this example, you would first read in the appropriate group based on the letter. So if the word you are searching for begins with "m", you would load the "m" group into memory.
There are other methods of grouping such as word (key) length. There can also be subgroups too. In this example, you could divide the "m" group by word lengths or by second letter.
After grouping, you may want to write the data back to another file so you don't have to modify the data anymore.
There are many ways to store groups on the file, such as using a "section" marker. These would be for another question though.
The ideas here, including from #047, are to structure the data for the most efficient search, giving your memory constraints.
I'm currently migrating XML from one CMS to another and needs to convert some text to elements. Because of how the system works, some editors can only enter escaped text. The challange is to replace some of these escaped elements and convert them into valid XML elements.
Source file:
<p>Press the <button-name>Select key </button-name>to show more information.</p>
<p>Press the <button-name>Back key</button-name> to save the
values.</p>
<p>When the storage is completed, the <product-name/> machine
displays:</p>
<p><attention>
<display-text translate="no">STORAGE COMPLETED
Press BACK to exit</display-text>
</attention></p>
What I want to do
Replace <button-name> with <gui>
Replace <button-name> with <kt.in name="custom-name"/>
Keeping other escaped elements.
XML I want
<p>Press the <gui>Select key</gui>to
show more information.</p>
<p>Press the <gui>Back key</gui>
to save the calibrations values.</p>
<p>When the storage is completed, the <kt.in name="custom-name"/> machine
displays:</p>
<p><attention> <display-text translate="no">STORAGE COMPLETED
Press BACK to exit</display-text>
</attention></p>
I tried using a string-based search-and-replace but as I want proper an XML element as output this wouldn't do it.
This is probably only going to work by string-based-search-and-replace - depending on the amount of text "tags" you want to switch to xml. The bigger problem I see is actually keeping it all in a proper XML-Element.
I dont think that you could keep this without writing a small tool that will read the strings between the elements of the text e.g.
<button-name>
and copy them into the right variables of an Object which you then parse back as XML conform Element.
It doesnt really depend on the language you prefer since there should be plenty of object-xml parsers available
For just changing the tags you could also switch the encoding of the text as
< would turn into -> <
and then filter any content in between the <> to exchange the ones you want e.g. button-name to gui
hope I could give you an idea..
I need to create a search index for a collection of HTML pages.
I have no experience in implementing a search index at all, so any general information how to build one, what information to store, how to implement advanced searches such as "entire phrase", ranking of results etc.
I'm not afraid to build it myself, though I'd be happy to reuse an existing component (or use one to get started with a prototype). I am looking for a solution accessible from C++, preferrably without requiring additional installations at runtime. The content is static (so it makes sense to aggregate search information), but a search might have to accumulate results from multiple such repositories.
I can make a few educated guesses, though: create a map word ==> pages for all (relevant) words, a rank can be assigned to the mapping by promincence (h1 > h2 > ... > <p>) and proximity to top. Advanced searches could be built on top of that: searching for phrase "homo sapiens" could list all pages that contain "homo" and "sapiens", then scan all pages returned for locations where they occur together. However, there are a lot of problematic scenarios and unanswered questions, so I am looking for references to what should be a huge amount of existing work that somehow escapes my google-fu.
[edit for bounty]
The best resource I found until now is this and the links from there.
I do have an imlementation roadmap for an experimental system, however, I am still looking for:
Reference material regarding index creation and individual steps
available implementations of individual steps
reusable implementations (with above environment restrictions)
This process is generally known as information retrieval. You'll probably find this online book helpful.
Existing libraries
Here are two existing solutions that can be fully integrated into an application without requiring a separate process (I believe both will compile with VC++).
Xapian is mature and may do much of what you need, from indexing to ranked retrieval. Separate HTML parsing would be required because, AFAIK, it does not parse html (it has a companion program Omega, which is a front end for indexing web sites).
Lucene is a index/searching Apache library in Java, with an official pre-release C version lucy, and an unofficial C++ version CLucene.
Implementing information retrieval
If the above options are not viable for some reason, here's some info on the individual steps of building and using an index. Custom solutions can go from simple to very sophisticated, depending what you need for your application. I've broken the process into 5 steps
HTML processing
Text processing
Indexing
Retrieval
Ranking
HTML Processing
There are two approaches here
Stripping The page you referred to discusses a technique generally known as stripping, which involves removing all the html elements that won't be displayed and translating others to their display form. Personally, I'd preprocess using perl and index the resulting text files. But for an integrated solution, particularly one where you want to record significance tags (e.g. <h1>, <h2>), you probably want to role your own. Here is a partial implementation of a C++ stripping routine (appears in Thinking in C++ , final version of book here), that you could build from.
Parsing A level up in complexity from stripping is html parsing, which would help in your case for recording significance tags. However, a good C++ HTML parser is hard to find. Some options might be htmlcxx (never used it, but active and looks promising) or hubbub (C library, part of NetSurf, but claims to be portable).
If you are dealing with XHTML or are willing to use an HTML-to-XML converter, you can use one of the many available XML parsers. But again, HTML-to-XML converters are hard to find, the only one I know of is HTML Tidy. In addition to conversion to XHTML, its primary purpose is to fix missing/broken tags, and it has an API that could possibly be used to integrate it into an application. Given XHTML documents, there are many good XML parsers, e.g. Xerces-C++ and tinyXML.
Text Processing
For English at least, processing text to words is pretty straight forward. There are a couple of complications when search is involved though.
Stop words are words known a priori not to provide a useful distinction between documents in the set, such as articles and propositions. Often these words are not indexed and filtered from query streams. There are many stop word lists available on the web, such as this one.
Stemming involves preprocessing documents and queries to identify the root of each word to better generalize a search. E.g. searching for "foobarred" should yield "foobarred", "foobarring", and "foobar". The index can be built and searched on roots alone. The two general approaches to stemming are dictionary based (lookups from word ==> root) and algorithm based. The Porter algorithm is very common and several implementations are available, e.g. C++ here or C here. Stemming in the Snowball C library supports several languages.
Soundex encoding One method to make search more robust to spelling errors is to encode words with a phonetic encoding. Then when queries have phonetic errors, they will still map directly to indexed words. There are a lot of implementations around, here's one.
Indexing
The map word ==> page data structure is known as an inverted index. Its inverted because its often generated from a forward index of page ==> words. Inverted indexes generally come in two flavors: inverted file index, which map words to each document they occur in, and full inverted index, which map words to each position in each document they occur in.
The important decision is what backend to use for the index, some possibilities are, in order of ease of implementation:
SQLite or Berkly DB - both of these are database engines with C++ APIs that integrated into a project without requiring a separate server process. Persistent databases are essentially files, so multiple index sets can be search by just changing the associated file. Using a DBMS as a backend simplifies index creation, updating and searching.
In memory data structure - if your using a inverted file index that is not prohibitively large (memory consumption and time to load), this could be implemented as a std::map<std::string,word_data_class>, using boost::serialization for persistence.
On disk data structure - I've heard of blazingly fast results using memory mapped files for this sort of thing, YMMV. Having an inverted file index would involve having two index files, one representing words with something like struct {char word[n]; unsigned int offset; unsigned int count; };, and the second representing (word, document) tuples with just unsigned ints (words implicit in the file offset). The offset is the file offset for the first document id for the word in the second file, count is the number of document ids associate with that word (number of ids to read from the second file). Searching would then reduce to a binary search through the first file with a pointer into a memory mapped file. The down side is the need to pad/truncate words to get a constant record size.
The procedure for indexing depends on which backend you use. The classic algorithm for generating a inverted file index (detailed here) begins with reading through each document and extending a list of (page id, word) tuples, ignoring duplicate words in each document. After all documents are processed, sort the list by word, then collapsed into (word, (page id1, page id2, ...)).
The mifluz gnu library implements inverted indexes w/ storage, but without document or query parsing. GPL, so may not be a viable option, but will give you an idea of the complexities involved for an inverted index that supports a large number of documents.
Retrieval
A very common method is boolean retrieval, which is simply the union/intersection of documents indexed for each of the query words that are joined with or/and, respectively. These operations are efficient if the document ids are stored in sorted order for each term, so that algorithms like std::set_union or std::set_intersection can be applied directly.
There are variations on retrieval, wikipedia has an overview, but standard boolean is good for many/most application.
Ranking
There are many methods for ranking the documents returned by boolean retrieval. Common methods are based on the bag of words model, which just means that the relative position of words is ignored. The general approach is to score each retrieved document relative to the query, and rank documents based on their calculated score. There are many scoring methods, but a good starting place is the term frequency-inverse document frequency formula.
The idea behind this formula is that if a query word occurs frequently in a document, that document should score higher, but a word that occurs in many documents is less informative so this word should be down weighted. The formula is, over query terms i=1..N and document j
score[j] = sum_over_i(word_freq[i,j] * inv_doc_freq[i])
where the word_freq[i,j] is the number of occurrences of word i in document j, and
inv_doc_freq[i] = log(M/doc_freq[i])
where M is the number of documents and doc_freq[i] is the number of documents containing word i. Notice that words that occur in all documents will not contribute to the score. A more complex scoring model that is widely used is BM25, which is included in both Lucene and Xapian.
Often, effective ranking for a particular domain is obtained by adjusting by trial and error. A starting place for adjusting rankings by heading/paragraph context could be inflating word_freq for a word based on heading/paragraph context, e.g. 1 for a paragraph, 10 for a top level heading. For some other ideas, you might find this paper interesting, where the authors adjusted BM25 ranking for positional scoring (the idea being that words closer to the beginning of the document are more relevant than words toward the end).
Objective quantification of ranking performance is obtained by precision-recall curves or mean average precision, detailed here. Evaluation requires an ideal set of queries paired with all the relevant documents in the set.
Depending on the size and number of the static pages, you might want to look at an already existent search solution.
"How do you implement full-text search for that 10+ million row table, keep up with the load, and stay relevant? Sphinx is good at those kinds of riddles."
I would choose the Sphinx engine for full text searching. The licence is GPL but the also have a commercial version available. It is meant to be run stand-alone [2], but it can also be embedded into applications by extracting the needed functionality (be it indexing[1], searching [3], stemming, etc).
The data should be obtained by parsing the input HTML files and transforming them to plain-text by using a parser like libxml2's HTMLparser (I haven't used it, but they say it can parse even malformed HTML). If you aren't bound to C/C++ you could take a look at Beautiful Soup.
After obtaining the plain-texts, you could store them in a database like MySQL or PostgreSQL. If you want to keep everything embedded you should go with sqlite.
Note that Sphinx doesn't work out-of-the-box with sqlite, but there is an attempt to add support (sphinx-sqlite3).
I would attack this with a little sqlite database. You could have tables for 'page', 'term' and 'page term'. 'Page' would have columns like id, text, title and url. 'Term' would have a column containing a word, as well as the primary ID. 'Page term' would have foreign keys to a page ID and a term ID, and could also store the weight, calculated from the distance from the top and the number of occurrences (or whatever you want).
Perhaps a more efficient way would be to only have two tables - 'page' as before, and 'page term' which would have the page ID, the weight, and a hash of the term word.
An example query - you want to search for "foo". You hash "foo", then query all page term rows that have that term hash. Sort by descending weight and show the top ten results.
I think this should query reasonably quickly, though it obviously depends on the number and size of the pages in question. Sqlite isn't difficult to bundle and shouldn't need an additional installation.
Ranking pages is the really tricky bit here. With a large sample of pages you can use links quite a lot in working out ranks. Other wise you need to check how words seem to be placed, and also making sure your engine doesn't get fooled by 'dictionary' pages.
Good luck!