I have been looking for a name for a new project. I want the name to have available domains and social media handles. For months, all those I can think of are taken.
So I generated a list of names with at least a consonant and a vowel and checked if the domains are available (which is very fast). I have about a million possible names.
I would like to sort them by some rank of simplicity. "Aaazq" would be close to the bottom, "Cawel" would be close to the top. I thought of the CVC structure (Consonant-Vowel-Consonant) and wonder if some more sophisticated algorithm exists. I searched for "sonority" but it has a different meaning in linguistics.
How can I automatically rank the simplicity of a random name?
I assume you would judge simplicity as compared to a target language, say English. Something that is 'simple' in English might not be 'simple' in German or Korean, as these languages have very different phonological structures.
I would recommend the following:
collect some data of the language you are using. Just get some novels from Project Gutenberg, for example, or newspaper articles. Whatever you can easily get hold of.
now generate n-grams from this: all sequences of two (bigrams) or three (trigrams) letters. Turn this into a frequency list, so that common n-grams are at the top of the list with a high frequency.
turn your suggested name into n-grams. Count how many times the respective n-gram occurs in your frequency list, and take the average or median of the result
Your examples would do as follows:
aa aa az zq: "aa" is rare ("aardvark") "az" a bit more common ("glaze", "raze"), and "zq" would not exist. So, not a very high score.
ca aw we el: all of these are fairly common in English words, so a reasonably high score.
You could also add a dummy # at the beginning and the end, so in your first example you'd get #a, which is fine, as many English words start with "a", but the final q# bombs out, as there's only words such as "Iraq" which end in a "q".
You can obviously do the same for other languages.
Also, you can reverse the process in a way, and pick random n-grams from your frequency list to generate names: by picking higher-frequency n-grams you will make sure the name is a good match with the phonological structure of your target language.
Note for pedants: I use phonological structure, but it's really its representation in the spelling system that we're dealing with here.
I am using H2O machine learning package to do natural language predictions, including the functions h2o.word2vec and h2o.transform. I need sentence level aggregation, which is provided by the AVERAGE parameter value:
h2o.transform(word2vec, words, aggregate_method = c("NONE", "AVERAGE"))
However, in my case I strongly wish to avoid equal weighting of "the" and "platypus" for example.
Here's a scheme I concocted to achieve custom word-weightings. If H2O's word2vec "AVERAGE" option uses all the words including duplicates that might appear, then I could effect a custom word weighting when calling h2o.transform by adding additional duplicates of certain words to my sentences, when I want to weight them more heavily than other words.
Can any H2O experts confirm that that the word2vec AVERAGE parameter is using all the words rather than just the unique words when computing AVERAGE of the words in sentence?
Alternatively, is there a better way? I tried but I find myself unable to imagine any correct math to multiply the sentence average by some factor, after it was already computed.
Yes, h2o.transform will consider each occurrence of a word for the averaging, not just the unique words. Your trick will therefore work.
There is currently no direct way to provide user defined weights. You could probably do an ugly hack and weight directly the word embeddings but that won't be a straightforward solution I could recommend.
We can add this feature to H2O. I would love to hear what API would work for you (how would you like to provide the weights).
Is it possible to make a dataset atleast? I am doing sentiment analysis and is getting polarity of the message
I was following this tutorial. But it is not the data set required.
http://machinelearningmastery.com/naive-bayes-classifier-scratch-python/
It would be great if anyone could explain the csv file given here.
Basically, the process of converting a collection of text documents into numerical feature vectors is called vectorization. There are several techniques or concepts that can be used to vectorize text documents(for eg. word embeddings, bag of words, etc.).
Bag of words is one of the simplest ways to vectorize text into numerical features. TfIdf is an effective vectorization technique based on the bag of words concept.
On a very basic level, TfIdf uses a set of unigrams or bigrams(n-grams in general) from the entire text corpus and uses them as the features for all your text documents(tweets in your case). So if you imagine your text corpus as a table of numerical values then each row would be a text document(a tweet in your case) and each column would be a unigram(which is basically a word) and the value of each cell (i,j) in the table would depend on the term frequency of unigram j in the tweet i(the number of times that the particular unigram occurs in the tweet) and the inverse of the document frequency of the unigram j(the number of tweets that the particular unigram occurs in all the tweets combined). Hence, you would have a list of tweets as vectors which would have a numerical tfidf values corresponding to each feature(unigram).
For more information on how to implement tfidf look at the following links:
http://scikit-learn.org/stable/modules/feature_extraction.html#the-bag-of-words-representation
http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
I'm using StringToWordVector Naive Bayes and StringToWordVector to classify some text.
I'm also using TD/IDF to put score on words.
Is there a simple way to increase the score of some words (chosen by myself) during the training to increase the weight of this words in the model for a given class?
So if this words are present in a new document the classifier would know there is more chance that the document belongs to this class.
Thanks!
You want to increase the probability that documents containing certain words will be classified as a certain kind of document.
What you can do, is to simply train your classifier with "hand made" documents that contain exactly these words, and then mark these documents as belonging to a specific class.
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!