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Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and word dependencies, and indicate which noun phrases refer to the same entities. It provides the foundational building blocks for higher level text understanding applications.
package edu.stanford.nlp.sentiment;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.Serializable;
import java.util.Random;
import edu.stanford.nlp.trees.PennTreebankLanguagePack;
import edu.stanford.nlp.trees.TreebankLanguagePack;
import edu.stanford.nlp.util.StringUtils;
public class RNNOptions implements Serializable {
// TODO [2014]: This should really be a long
/**
* The random seed the random number generator is initialized with.
*/
public int randomSeed = new Random().nextInt();
/**
* Filename for the word vectors
*/
public String wordVectors;
/**
* In the wordVectors file, what word represents unknown?
*/
public String unkWord = "UNK";
/**
* By default, initialize random word vectors instead of reading
* from a file
*/
public boolean randomWordVectors = true;
/**
* Size of vectors to use. Must be at most the size of the vectors
* in the word vector file. If a smaller size is specified, vectors
* will be truncated.
*/
public int numHid = 25;
/**
* Number of classes to build the RNN for
*/
public int numClasses = 5;
public boolean lowercaseWordVectors = false;
public boolean useTensors = true;
// TODO: add an option to set this to some other language pack
public TreebankLanguagePack langpack = new PennTreebankLanguagePack();
/**
* No syntactic untying - use the same matrix/tensor for all categories.
* This results in all nodes getting the same matrix (and tensor,
* where applicable)
*/
public boolean simplifiedModel = true;
/**
* If this option is true, then the binary and unary classification
* matrices are combined. Only makes sense if simplifiedModel is true.
* If combineClassification is set to true, simplifiedModel will
* also be set to true. If simplifiedModel is set to false, this
* will be set to false.
*/
public boolean combineClassification = true;
public RNNTrainOptions trainOptions = new RNNTrainOptions();
public static final String[] DEFAULT_CLASS_NAMES = { "Very negative", "Negative", "Neutral", "Positive", "Very positive" };
public static final String[] BINARY_DEFAULT_CLASS_NAMES = { "Negative", "Positive" };
public String[] classNames = DEFAULT_CLASS_NAMES;
public static final int[][] APPROXIMATE_EQUIVALENCE_CLASSES = { {0, 1}, {3, 4} };
public static final int[][] BINARY_APPROXIMATE_EQUIVALENCE_CLASSES = { {0}, {1} }; // almost an owl
/**
* The following option represents classes which can be treated as
* equivalent when scoring. There will be two separate scorings,
* one with equivalence used and one without. Default is set for
* the sentiment project.
*/
public int[][] equivalenceClasses = APPROXIMATE_EQUIVALENCE_CLASSES;
public static final String[] DEFAULT_EQUIVALENCE_CLASS_NAMES = { "Negative", "Positive" };
public String[] equivalenceClassNames = DEFAULT_EQUIVALENCE_CLASS_NAMES;
public RNNTestOptions testOptions = new RNNTestOptions();
// TODO: we can remove this if we reserialize all the models
private void readObject(ObjectInputStream in) throws IOException, ClassNotFoundException {
in.defaultReadObject();
if (testOptions == null) {
testOptions = new RNNTestOptions();
}
}
@Override
public String toString() {
StringBuilder result = new StringBuilder();
result.append("GENERAL OPTIONS\n");
result.append("randomSeed=" + randomSeed + "\n");
result.append("wordVectors=" + wordVectors + "\n");
result.append("unkWord=" + unkWord + "\n");
result.append("randomWordVectors=" + randomWordVectors + "\n");
result.append("numHid=" + numHid + "\n");
result.append("numClasses=" + numClasses + "\n");
result.append("lowercaseWordVectors=" + lowercaseWordVectors + "\n");
result.append("useTensors=" + useTensors + "\n");
result.append("simplifiedModel=" + simplifiedModel + "\n");
result.append("combineClassification=" + combineClassification + "\n");
result.append("classNames=" + StringUtils.join(classNames, ",") + "\n");
result.append("equivalenceClasses=");
if (equivalenceClasses != null) {
for (int i = 0; i < equivalenceClasses.length; ++i) {
if (i > 0) result.append(";");
for (int j = 0; j < equivalenceClasses[i].length; ++j) {
if (j > 0) result.append(",");
result.append(equivalenceClasses[i][j]);
}
}
}
result.append("\n");
result.append("equivalenceClassNames=");
if (equivalenceClassNames != null) {
result.append(StringUtils.join(equivalenceClassNames, ","));
}
result.append("\n");
result.append(trainOptions.toString());
result.append(testOptions.toString());
return result.toString();
}
public int setOption(String[] args, int argIndex) {
if (args[argIndex].equalsIgnoreCase("-randomSeed")) {
randomSeed = Integer.parseInt(args[argIndex + 1]);
return argIndex + 2;
} else if (args[argIndex].equalsIgnoreCase("-wordVectors")) {
wordVectors = args[argIndex + 1];
return argIndex + 2;
} else if (args[argIndex].equalsIgnoreCase("-unkWord")) {
unkWord = args[argIndex + 1];
return argIndex + 2;
} else if (args[argIndex].equalsIgnoreCase("-numHid")) {
numHid = Integer.parseInt(args[argIndex + 1]);
return argIndex + 2;
} else if (args[argIndex].equalsIgnoreCase("-numClasses")) {
numClasses = Integer.parseInt(args[argIndex + 1]);
return argIndex + 2;
} else if (args[argIndex].equalsIgnoreCase("-lowercaseWordVectors")) {
lowercaseWordVectors = true;
return argIndex + 1;
} else if (args[argIndex].equalsIgnoreCase("-nolowercaseWordVectors")) {
lowercaseWordVectors = false;
return argIndex + 1;
} else if (args[argIndex].equalsIgnoreCase("-randomWordVectors")) {
randomWordVectors = true;
return argIndex + 1;
} else if (args[argIndex].equalsIgnoreCase("-norandomWordVectors")) {
randomWordVectors = false;
return argIndex + 1;
} else if (args[argIndex].equalsIgnoreCase("-simplifiedModel")) {
simplifiedModel = true;
return argIndex + 1;
} else if (args[argIndex].equalsIgnoreCase("-nosimplifiedModel")) {
simplifiedModel = false;
combineClassification = false;
return argIndex + 1;
} else if (args[argIndex].equalsIgnoreCase("-combineClassification")) {
combineClassification = true;
simplifiedModel = true;
return argIndex + 1;
} else if (args[argIndex].equalsIgnoreCase("-nocombineClassification")) {
combineClassification = false;
return argIndex + 1;
} else if (args[argIndex].equalsIgnoreCase("-useTensors")) {
useTensors = true;
return argIndex + 1;
} else if (args[argIndex].equalsIgnoreCase("-nouseTensors")) {
useTensors = false;
return argIndex + 1;
} else if (args[argIndex].equalsIgnoreCase("-classNames")) {
classNames = args[argIndex + 1].split(",");
return argIndex + 2;
} else if (args[argIndex].equalsIgnoreCase("-equivalenceClasses")) {
if (args[argIndex + 1].trim().length() == 0) {
equivalenceClasses = null;
return argIndex + 2;
}
String[] pieces = args[argIndex + 1].split(";");
equivalenceClasses = new int[pieces.length][];
for (int i = 0; i < pieces.length; ++i) {
String[] values = pieces[i].split(",");
equivalenceClasses[i] = new int[values.length];
for (int j = 0; j < values.length; ++j) {
equivalenceClasses[i][j] = Integer.valueOf(values[j]);
}
}
return argIndex + 2;
} else if (args[argIndex].equalsIgnoreCase("-equivalenceClassNames")) {
if (args[argIndex + 1].trim().length() > 0) {
equivalenceClassNames = args[argIndex + 1].split(",");
} else {
equivalenceClassNames = null;
}
return argIndex + 2;
} else if (args[argIndex].equalsIgnoreCase("-binaryModel")) { // macro option
numClasses = 2;
classNames = BINARY_DEFAULT_CLASS_NAMES;
// TODO: should we just make this null?
equivalenceClasses = BINARY_APPROXIMATE_EQUIVALENCE_CLASSES;
trainOptions.setOption(args, argIndex); // in case the trainOptions use binaryModel as well
return argIndex + 1;
} else {
int newIndex = trainOptions.setOption(args, argIndex);
if (newIndex == argIndex) {
newIndex = testOptions.setOption(args, argIndex);
}
return newIndex;
}
}
private static final long serialVersionUID = 1;
}