NovelEssay.com Programming Blog

Exploration of Big Data, Machine Learning, Natural Language Processing, and other fun problems.

Extracting important snip-its with C# and Log Likelyhood


How does text summary software pick out the most important ideas to present?

One solution is to use Log Likelyhood to generate a summary of the sentences that contain the important terms and cover the most different topics.


What Log Likelyhood is and why it works can be read here: https://en.wikipedia.org/wiki/Likelihood_function


This article will focus on implementing it in C#. Here's the code I wrote as a translation from the Mahout version. I tried to make it as readable as possible rather than optimizing for performance.

// Log Likelyhood code roughly translated from here:
// http://grepcode.com/file/repo1.maven.org/maven2/org.apache.mahout/mahout-math/0.3/org/apache/mahout/math/stats/LogLikelihood.java#LogLikelihood.logLikelihoodRatio%28int%2Cint%2Cint%2Cint%29
static private double ShannonEntropy(List<Int64> elements)
{
    double sum = 0;
    foreach (Int64 element in elements)
    {
        sum += element;
    }
    double result = 0.0;
    foreach (Int64 element in elements)
    {
        if(element < 0)
        {
            throw new Exception("Should not have negative count for entropy computation (" + element + ")");
        }
        int zeroFlag = (element == 0 ? 1 : 0);
        result += element * Math.Log((element + zeroFlag) / sum);
    }
    return result;
}
/*
    Calculate the Raw Log-likelihood ratio for two events, call them A and B. Then we have:
 	    Event A	Everything but A
        Event B	A and B together (k_11)	B, but not A (k_12)
        Everything but B	A without B (k_21)	Neither A nor B (k_22)
    Parameters:
    k11 The number of times the two events occurred together
    k12 The number of times the second event occurred WITHOUT the first event
    k21 The number of times the first event occurred WITHOUT the second event
    k22 The number of times something else occurred (i.e. was neither of these events
*/
static public double LogLikelihoodRatio(Int64 k11, Int64 k12, Int64 k21, Int64 k22)
{
    double rowEntropy = ShannonEntropy(new List<Int64>() { k11, k12 }) + ShannonEntropy(new List<Int64>() { k21, k22 });
    double columnEntropy = ShannonEntropy(new List<Int64>() { k11, k21 }) + ShannonEntropy(new List<Int64>() { k12, k22 });
    double matrixEntropy = ShannonEntropy(new List<Int64>() { k11, k12, k21, k22 });
    return 2 * (matrixEntropy - rowEntropy - columnEntropy);
}

Now, we have a simple LogLikelihoodRatio function we can call with 4 parameters and get the score result.


Let's say we want to pick out the most important sentences from a particular Wikipedia article in order to summarize it. (See this article for loading Wikipedia in to ElasticSearch: http://blog.novelessay.com/post/loading-wikipedia-in-to-elasticsearch)

Follow these steps:

  1. Pick a Wikipedia article.
  2. Get a Term Frequency dictionary for the whole article.
  3. Parse the article in to sentences.
  4. For each token in each sentence, calculate the Log Likelyhood score with the above LogLikelihoodRatio function.
  5. If the result of LogLikelihoodRatio is less than -10, give that sentence +1 to a weight value.
  6. At the end of each sentence, you have a +X weight value. That can be normalized by the number of words in the sentence.
  7. After you've obtained the weight score from #6 for all of the sentences in an article, you can sort them and pick the most important ones.
For extra credit, you'll want to avoid redundant important sentences. In order to do that, you'll need to score the candidate sentences against the summary's output as you build it.


Here's some code with comments about populating the input values passed to the LogLikelihoodRatio function. Be sure to check the result score is less than -10 before adding a +1 weight.

// http://www.cs.columbia.edu/~gmw/candidacy/LinHovy00.pdf - Section 4.1
Int64 k11 = // frequency of current term in this article
Int64 k12 = // frequency of current term in all of Wikipedia - k11
Int64 k21 = // total count of all terms in this article - k11
Int64 k22 = // total count of all terms in Wikipedia - k12
double termWeight = LogLikelihoodRatio(k11, k12, k21, k22);

if(termWeight < -10)
{
    weightSum++;
}

Obviously, in the above you don't want to be calculating Term Frequency across Wikipedia on-the-fly. K11 and K21 will get calculated as you process an article, but K12 and K22 should be calculated in advance and cached in a lookup dictionary. 


I use LevelDb as my Term Frequency look up dictionary. You can read about using that here: http://blog.novelessay.com/post/fast-persistent-key-value-pairs-in-c-with-leveldb


In order to build your Term Frequency look up dictionary chace, you could process each documents and create your own term frequency output, or use the ElasticSearch plugin for _termList here: https://github.com/jprante/elasticsearch-index-termlist



Text Extraction using C# .Net and Apache Tika


You want to using C# to extract text from documents and web pages. You want it to have high quality and be free. Try the .Net wrapper to the Apache Tika library!


Let's build a sample app and show the use case. First step, start a C# console application with Visual Studio. Use the Nuget package manager and install the TikaOnDotNet.TextExtractor packages.



Then, try this sample code. It shows an example of text extraction examples for a file, Url, and byte array sources.

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using TikaOnDotNet.TextExtraction;

namespace TikaTest
{
    class Program
    {
        static void Main(string[] args)
        {

            TextExtractor textExtractor = new TextExtractor();

            // Fun Utf8 strings found here: http://www.columbia.edu/~fdc/utf8/
            string utf8InputString = @"It's a small village in eastern Lower Saxony. The ""oe"" in this case turns out to be the Lower Saxon ""lengthening e""(Dehnungs-e), which makes the previous vowel long (used in a number of Lower Saxon place names such as Soest and Itzehoe), not the ""e"" that indicates umlaut of the preceding vowel. Many thanks to the Óechtringen-Namenschreibungsuntersuchungskomitee (Alex Bochannek, Manfred Erren, Asmus Freytag, Christoph Päper, plus Werner Lemberg who serves as Óechtringen-Namenschreibungsuntersuchungskomiteerechtschreibungsprüfer) for their relentless pursuit of the facts in this case. Conclusion: the accent almost certainly does not belong on this (or any other native German) word, but neither can it be dismissed as dirt on the page. To add to the mystery, it has been reported that other copies of the same edition of the PLZB do not show the accent! UPDATE (March 2006): David Krings was intrigued enough by this report to contact the mayor of Ebstorf, of which Oechtringen is a borough, who responded:";
            // Convert string to byte array
            byte[] byteArrayInput = Encoding.UTF8.GetBytes(utf8InputString);
            // Text Extraction Example for Byte Array
            TextExtractionResult result = textExtractor.Extract(byteArrayInput);
            Console.WriteLine(result.Text);

            // Text Extraction Example for Uri:
            result = textExtractor.Extract(new Uri("http://blog.novelessay.com"));
            Console.WriteLine(result.Text);

            // Text Extraction Example for File
            result = textExtractor.Extract(@"c:\myPdf.pdf");
            Console.WriteLine(result.Text);

            // Note that result also has metadata collection and content type attributes
            //result.Metadata
            //result.ContentType
        }
    }
}

Notice that the TextExtractionResult has a Metadata collection and also a content type attribute. Here's an example of the metadata provided along with the extracted text. It contains many things including author, dates, keywords, title, and description.


      

I've been very pleased with Tika's quality and ability to handle many different file types. I hope you try it out and enjoy it too.


Fast Persistent Key Value Pairs in C# with LevelDb



Let's say we want to crawl the internet, but we don't want to request any given URL more than once. We need to have a collection of URL keys that we can look up. It would be nice if we could have key-value pairs, so that we can give URL keys a value in case we change our minds and want to allow URL request updates every X days. We want it to handle billions of records and be really fast (and free). This article will show how to accomplish that using LevelDb and its C# wrapper.


First, start a Visual Studio C# project and download the LevelDb.Net nuget package. There are a few different one, but this is my favorite. 


You can also find this LevelDb.Net at this Github location:

https://github.com/AntShares/leveldb


First, I'm going to show how to use LevelDb via C#. Later in this article, code shows how to insert and select a large number of records for speed testing.


Let's create a LevelDb:

            Options levelDbOptions = new Options();
            levelDbOptions.CreateIfMissing = true;
            LevelDB.DB levelDb = LevelDB.DB.Open("myLevelDb.dat", levelDbOptions);

Next, we'll insert some keys:

            levelDb.Put(LevelDB.WriteOptions.Default, "Key1", "Value1");
            levelDb.Put(LevelDB.WriteOptions.Default, "Key1", "Value2");
            levelDb.Put(LevelDB.WriteOptions.Default, "Key1", "Value3");
            levelDb.Put(LevelDB.WriteOptions.Default, "Key2", "Value2");

Then, we'll select some keys:

            LevelDB.Slice outputValue;
            if (levelDb.TryGet(LevelDB.ReadOptions.Default, "Key2", out outputValue))
            {
                Console.WriteLine("Key2: Value = " + outputValue.ToString());// Expect: Value2
            }
            if (levelDb.TryGet(LevelDB.ReadOptions.Default, "Key1", out outputValue))
            {
                Console.WriteLine("Key1: Value = " + outputValue.ToString()); // Expect: Value3
            }
            if (!levelDb.TryGet(LevelDB.ReadOptions.Default, "KeyXYZ", out outputValue))
            {
                Console.WriteLine("KeyXYZ: NOT FOUND.");
            }

LevelDb supports many different types of keys and values (strings, int, float, byte[], etc...).

  1. Open instance handle.
  2. Insert = Put
  3. Select = TryGet

That's it! 

But, how fast is it?

Let's build a collection of MD5 hash keys and insert them:

            List<string> seedHashes = new List<string>();
            for (int idx = 0; idx < 500000; idx++)
            {
                byte[] encodedPassword = new UTF8Encoding().GetBytes(idx.ToString());
                byte[] hash = ((HashAlgorithm)CryptoConfig.CreateFromName("MD5")).ComputeHash(encodedPassword);
                string encoded = BitConverter.ToString(hash).Replace("-", string.Empty).ToLower();
                seedHashes.Add(encoded);
            }

            // Start Insert Speed Tests
            Stopwatch stopWatch = new Stopwatch();
            stopWatch.Start();
            foreach(var key in seedHashes)
            {
                levelDb.Put(LevelDB.WriteOptions.Default, key, "1");
            }
            stopWatch.Stop();
            Console.WriteLine("LevelDb Inserts took (ms) " + stopWatch.ElapsedMilliseconds);


Next, let's select each of the keys we just inserted several times:

            // Start Lookup Speed Tests
            stopWatch.Start();
            for (int loopIndex = 0; loopIndex < 10; loopIndex++)
            {
                for(int seedIndex = 0; seedIndex < seedHashes.Count; seedIndex++)
                {
                    if (!levelDb.TryGet(LevelDB.ReadOptions.Default, seedHashes[seedIndex], out outputValue))
                    {
                        Console.WriteLine("ERROR: Key Not Found: " + seedHashes[seedIndex]);
                    }
                }
            }
            stopWatch.Stop();
            Console.WriteLine("LevelDb Lookups took (ms) " + stopWatch.ElapsedMilliseconds);

On my junky 4 year old desktop, 500,000 inserts took just under 60 seconds and 5 Million selects took just over 2 minutes. Here's the program output:


The complete code sample is below:

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using LevelDB;
using System.Security.Cryptography;
using System.Diagnostics;

namespace LevelDbExample
{
    class Program
    {
        static void Main(string[] args)
        {

            Options levelDbOptions = new Options();
            levelDbOptions.CreateIfMissing = true;
            LevelDB.DB levelDb = LevelDB.DB.Open("myLevelDb.dat", levelDbOptions);

            // Insert some records
            levelDb.Put(LevelDB.WriteOptions.Default, "Key1", "Value1");
            levelDb.Put(LevelDB.WriteOptions.Default, "Key1", "Value2");
            levelDb.Put(LevelDB.WriteOptions.Default, "Key1", "Value3");
            levelDb.Put(LevelDB.WriteOptions.Default, "Key2", "Value2");

            // Select some records
            LevelDB.Slice outputValue;
            if (levelDb.TryGet(LevelDB.ReadOptions.Default, "Key2", out outputValue))
            {
                Console.WriteLine("Key2: Value = " + outputValue.ToString());// Expect: Value2
            }
            if (levelDb.TryGet(LevelDB.ReadOptions.Default, "Key1", out outputValue))
            {
                Console.WriteLine("Key1: Value = " + outputValue.ToString()); // Expect: Value3
            }
            if (!levelDb.TryGet(LevelDB.ReadOptions.Default, "KeyXYZ", out outputValue))
            {
                Console.WriteLine("KeyXYZ: NOT FOUND.");
            }

            // Build a collection of hash keys
            List<string> seedHashes = new List<string>();
            for (int idx = 0; idx < 500000; idx++)
            {
                byte[] encodedPassword = new UTF8Encoding().GetBytes(idx.ToString());
                byte[] hash = ((HashAlgorithm)CryptoConfig.CreateFromName("MD5")).ComputeHash(encodedPassword);
                string encoded = BitConverter.ToString(hash).Replace("-", string.Empty).ToLower();
                seedHashes.Add(encoded);
            }

            // Start Insert Speed Tests
            Stopwatch stopWatch = new Stopwatch();
            stopWatch.Start();
            foreach(var key in seedHashes)
            {
                levelDb.Put(LevelDB.WriteOptions.Default, key, "1");
            }
            stopWatch.Stop();
            Console.WriteLine("LevelDb Inserts took (ms) " + stopWatch.ElapsedMilliseconds);

            // Start Lookup Speed Tests
            stopWatch.Start();
            for (int loopIndex = 0; loopIndex < 10; loopIndex++)
            {
                for(int seedIndex = 0; seedIndex < seedHashes.Count; seedIndex++)
                {
                    if (!levelDb.TryGet(LevelDB.ReadOptions.Default, seedHashes[seedIndex], out outputValue))
                    {
                        Console.WriteLine("ERROR: Key Not Found: " + seedHashes[seedIndex]);
                    }
                }
            }
            stopWatch.Stop();
            Console.WriteLine("LevelDb Lookups took (ms) " + stopWatch.ElapsedMilliseconds);

            return;
        }
    }
}

Using C# Nest with ElasticSearch Carrot2 Clustering Plugin


ElasticSearch Carrot2 Clustering Plugin:

https://github.com/carrot2/elasticsearch-carrot2

This article will walk you through setting up ElasticSearch and Carrot2 clutering, so that you can implement something awesome like clustering topics on the publicly available Hillary Clinton email data set. Then, use foam tree to visually display it like this:



On to the example!

Let's say we want to get clusters for our Wikipedia index that we have loaded in to ElasticSearch, and we want to be able to also get clusters based on queries.


First, we'll want to build a SearchDescriptor based on query input. Let's just use a simple query string example for now. Here's example code to build a SearchDescriptor (which uses a special ignore "redirect" string that is custom for our Wikipedia index):

public static SearchDescriptor<Page> GetDocumentSearchDescriptorFromSearchParameters(string queryString, bool queryAnd, string ignoreQuery)
{
	string ignoreA = "#redirect";
	string ignoreB = "redirect";

	var searchDescriptor = new SearchDescriptor<Page>()
			.Query(q =>
				q.QueryString(p => p.Query(queryString).DefaultOperator(queryAnd ? Operator.And : Operator.Or))
				&& !q.Term(p => p.text, ignoreA)
				&& !q.Term(p => p.text, ignoreB)
				&& !q.QueryString(p => p.Query(ignoreQuery).DefaultOperator(queryAnd ? Operator.And : Operator.Or))
			);
	return searchDescriptor;
}

Next, we need to build our ElasticSearch query with connection strings along with size and from values. Then, we use the Nest client serializer to convert our request to JSON:

public static EsCarrotClusters GetSearchCarrotClusters(string esUrl, string queryString, int from, int size, bool queryAnd, string ignoreQuery)
{
	ConnectionSettings Settings = new ConnectionSettings(new Uri(esUrl));
	ElasticClient Client = new ElasticClient(Settings);
	var searchDescriptor = GetDocumentSearchDescriptorFromSearchParameters(queryString, queryAnd, ignoreQuery);
	searchDescriptor.Fields(f => f.Add("text"));
	searchDescriptor.Size(size);
	searchDescriptor.From(from);

	var jsonQuery = Encoding.UTF8.GetString(Client.Serializer.Serialize(searchDescriptor));
	jsonQuery = jsonQuery.Replace("\"query\": {},", "");
	jsonQuery = "{ \"search_request\" : " + jsonQuery;
	jsonQuery += ", \"query_hint\" : \"";
	jsonQuery += queryString == null ? "" : queryString;
	jsonQuery += "\",\"field_mapping\":{\"title\":[\"fields.text\"],\"content\":[]}}";

	string esClusterQueryRequestJson = jsonQuery;

	EsCarrotClusters clusters = null;

	string json = GetEsJsonFromAPI(esUrl, "_search_with_clusters", "", esClusterQueryRequestJson);
	if (json.Length > 0)
	{
		try
		{
			clusters = JsonConvert.DeserializeObject<EsCarrotClusters>(json);


Example calling code that uses "cats and dogs" as a query string input:

EsCarrotClusters esCarrotClusters = EsHttpWebRequestApi.GetSearchCarrotClusters("http://localhost:9200/mywiki", "cats and dogs", 0, 10, true, "");
Special Note:
The GetEsJsonFromAPI function simlpy does a HttpWebRequest POST to the ElasticSearch Uri with the JSON content written to the stream like this:
            using (var streamWriter = new StreamWriter(request.GetRequestStream()))
            {
                streamWriter.Write(esRequestJson);
                streamWriter.Flush();
                streamWriter.Close();
            }


Lastly, you'll want to see my EsCarrotClusters classes, so you can deserialize the HttpWebRequest's response back to a C# friendly object. Enjoy:

    public class Shards
    {
        public int total { get; set; }
        public int successful { get; set; }
        public int failed { get; set; }
    }

    public class Fields
    {
        public List<string> filename { get; set; }
    }

    public class Hit
    {
        public string _index { get; set; }
        public string _type { get; set; }
        public string _id { get; set; }
        public double _score { get; set; }
        public Fields fields { get; set; }
    }

    public class Hits
    {
        public int total { get; set; }
        public double max_score { get; set; }
        public List<Hit> hits { get; set; }
    }

    public class Cluster
    {
        public int id { get; set; }
        public double score { get; set; }
        public string label { get; set; }
        public List<string> phrases { get; set; }
        public List<string> documents { get; set; }
        public bool? other_topics { get; set; }
    }

    public class Info
    {
        public string algorithm { get; set; }
        [JsonProperty("search-millis")]
        public string searchmillis { get; set; }
        [JsonProperty("clustering-millis")]
        public string clusteringmillis { get; set; }
        [JsonProperty("total-millis")]
        public string totalmillis { get; set; }
        [JsonProperty("include-hits")]
        public string includehits { get; set; }
        [JsonProperty("max-hits")]
        public string maxhits { get; set; }
    }

    public class EsCarrotClusters
    {
        public int took { get; set; }
        public bool timed_out { get; set; }
        public Shards _shards { get; set; }
        public Hits hits { get; set; }
        public List<Cluster> clusters { get; set; }
        public Info info { get; set; }
    }


After I run this against my Wikipedia ElasticSearch index for "cats and dogs", I get clusters like these:

  • Polynueropath in Dogs and Cats
  • Album Cats
  • Canine
  • Domestic Cats
  • Missing Disease

Notice that you also are given a Score property, which you can use to weight topics or visually show them differently to the user.



Querying Wikipedia in ElasticSearch with C# Nest client



This article assumes that you've already loaded the Wikipedia articles in to your local ElasticSearch as described in this previous article. Please follow the instructions in this article on how to load your ElasticSearch with the entire content of Wikipedia:

http://blog.novelessay.com/post/loading-wikipedia-in-to-elasticsearch


Start a Visual Studio C# console application project, and install the ElasticSearch Nest Nuget package. 


In your code, create a Nest ElasticClient instance that is configured for your ElasticSearch instance. We are using localhost:9200 and the index named "mywiki" as the location of our Wikipedia data. 

var node = new Uri("http://localhost:9200");
var settings = new ConnectionSettings(
    node,
    defaultIndex: "mywiki"
).SetTimeout(int.MaxValue);
ElasticClient esClient = new ElasticClient(settings);


The Wikipedia index schema has a particular field format. We'll need a Page class like this for Nest to map fields in to:

public class Page
{
    public List<string> category { get; set; }
    public bool special { get; set; }
    public string title { get; set; }
    public bool stub { get; set; }
    public bool disambiguation { get; set; }
    public List<string> link { get; set; }
    public bool redirect { get; set; }
    public string text { get; set; }
}


Now, we can start querying our Wikipedia ElasticSearch index using our Nest client. Here's a simple example that pulls down the first 10 Wikipedia articles:

var result = esClient.Search<Page>(s => s
    .From(0)
    .Size(10)
    .MatchAll()
    );

You can check the response for errors and loop through the Page hits like this:

if (result.IsValid)
{
    foreach (var page in result.Hits)
    {
        // page.Source.text contains the wikipedia article text

After this, you can loop through all Wikipedia documents by changing the arguments passed to From and Size in the ElasticSearch query call.


Here's a query example that emulates a Google-like search via the use of a QueryString. Notice the use for Operator.And. I suggest you change it to Operator.Or and observe the difference effect on your results.

var result = esClient.Search<Page>(s => s
    .Take(10)
    .Query(q => q
        .QueryString(p => p.Query("cats dogs birds").DefaultOperator(Operator.And))
    )
);


If you're ready to start getting fancy, you can write a function that builds a Nest SearchDescriptor based on your query criteria. Then use the SearchDescriptor in your query to ElasticSearch. I wanted to search Wikipedia without getting redirect link results, so I set some ignore options in the example below that exclude #redirect terms for my search descriptors.

public static SearchDescriptor<Page> GetDocumentSearchDescriptorFromSearchParameters(string queryString, bool queryAnd, string ignoreQuery)
{
    string ignoreA = "#redirect";
    string ignoreB = "redirect";

    var searchDescriptor = new SearchDescriptor<Page>()
            .Query(q =>
                q.QueryString(p => p.Query(queryString).DefaultOperator(queryAnd ? Operator.And : Operator.Or))
                && !q.Term(p => p.text, ignoreA)
                && !q.Term(p => p.text, ignoreB)
                && !q.QueryString(p => p.Query(ignoreQuery).DefaultOperator(queryAnd ? Operator.And : Operator.Or))
            );
    return searchDescriptor;
}

If this SearchDescriptor example is a little confusing, stay tuned for the ElasticSearch Wikipedia clustering future article that I intend to write. In the mean time, you should be set up to query your Wikipedia ElasticSearch index with the C# Nest client.