Examples with TaggedWord edu.stanford.nlp.ling.TaggedWord used on opensource projects

Example 26 with TaggedWord

use of edu.stanford.nlp.ling.TaggedWord in project CoreNLP by stanfordnlp.

the class BaseLexicon method train.

@Override
public final void train(List<TaggedWord> sentence, double weight) {
    uwModelTrainer.incrementTreesRead(weight);
    int loc = 0;
    for (TaggedWord tw : sentence) {
        train(tw, loc, weight);
        ++loc;
    }
}

Example 27 with TaggedWord

use of edu.stanford.nlp.ling.TaggedWord in project CoreNLP by stanfordnlp.

the class BaseLexicon method listToEvents.

protected List<IntTaggedWord> listToEvents(List<TaggedWord> taggedWords) {
    List<IntTaggedWord> itwList = new ArrayList<>();
    for (TaggedWord tw : taggedWords) {
        IntTaggedWord iTW = new IntTaggedWord(tw.word(), tw.tag(), wordIndex, tagIndex);
        itwList.add(iTW);
    }
    return itwList;
}

Example 28 with TaggedWord

use of edu.stanford.nlp.ling.TaggedWord in project CoreNLP by stanfordnlp.

the class ShiftReduceDemo method main.

public static void main(String[] args) {
    String modelPath = "edu/stanford/nlp/models/srparser/englishSR.ser.gz";
    String taggerPath = "edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger";
    for (int argIndex = 0; argIndex < args.length; ) {
        switch(args[argIndex]) {
            case "-tagger":
                taggerPath = args[argIndex + 1];
                argIndex += 2;
                break;
            case "-model":
                modelPath = args[argIndex + 1];
                argIndex += 2;
                break;
            default:
                throw new RuntimeException("Unknown argument " + args[argIndex]);
        }
    }
    String text = "My dog likes to shake his stuffed chickadee toy.";
    MaxentTagger tagger = new MaxentTagger(taggerPath);
    ShiftReduceParser model = ShiftReduceParser.loadModel(modelPath);
    DocumentPreprocessor tokenizer = new DocumentPreprocessor(new StringReader(text));
    for (List<HasWord> sentence : tokenizer) {
        List<TaggedWord> tagged = tagger.tagSentence(sentence);
        Tree tree = model.apply(tagged);
        log.info(tree);
    }
}

Example 29 with TaggedWord

use of edu.stanford.nlp.ling.TaggedWord in project CoreNLP by stanfordnlp.

the class LexicalizedParser method main.

/**
   * A main program for using the parser with various options.
   * This program can be used for building and serializing
   * a parser from treebank data, for parsing sentences from a file
   * or URL using a serialized or text grammar parser,
   * and (mainly for parser quality testing)
   * for training and testing a parser on a treebank all in one go.
   *
   * <p>
   * Sample Usages:
   * <ul>
   *   <li> <b>Train a parser (saved to <i>serializedGrammarFilename</i>)
   *      from a directory of trees (<i>trainFilesPath</i>, with an optional <i>fileRange</i>, e.g., 0-1000):</b>
   *    {@code java -mx1500m edu.stanford.nlp.parser.lexparser.LexicalizedParser [-v] -train trainFilesPath [fileRange] -saveToSerializedFile serializedGrammarFilename}
   *   </li>
   *
   *   <li> <b>Train a parser (not saved) from a directory of trees, and test it (reporting scores) on a directory of trees</b>
   *    {@code java -mx1500m edu.stanford.nlp.parser.lexparser.LexicalizedParser [-v] -train trainFilesPath [fileRange] -testTreebank testFilePath [fileRange] }
   *   </li>
   *
   *   <li> <b>Parse one or more files, given a serialized grammar and a list of files</b>
   *    {@code java -mx512m edu.stanford.nlp.parser.lexparser.LexicalizedParser [-v] serializedGrammarPath filename [filename]*}
   *   </li>
   *
   *   <li> <b>Test and report scores for a serialized grammar on trees in an output directory</b>
   *    {@code java -mx512m edu.stanford.nlp.parser.lexparser.LexicalizedParser [-v] -loadFromSerializedFile serializedGrammarPath -testTreebank testFilePath [fileRange]}
   *   </li>
   * </ul>
   *
   *<p>
   * If the {@code serializedGrammarPath} ends in {@code .gz},
   * then the grammar is written and read as a compressed file (GZip).
   * If the {@code serializedGrammarPath} is a URL, starting with
   * {@code http://}, then the parser is read from the URL.
   * A fileRange specifies a numeric value that must be included within a
   * filename for it to be used in training or testing (this works well with
   * most current treebanks).  It can be specified like a range of pages to be
   * printed, for instance as {@code 200-2199} or
   * {@code 1-300,500-725,9000} or just as {@code 1} (if all your
   * trees are in a single file, either omit this parameter or just give a dummy
   * argument such as {@code 0}).
   * If the filename to parse is "-" then the parser parses from stdin.
   * If no files are supplied to parse, then a hardwired sentence
   * is parsed.
   *
   * <p>
   * The parser can write a grammar as either a serialized Java object file
   * or in a text format (or as both), specified with the following options:
   * <blockquote>{@code
   * java edu.stanford.nlp.parser.lexparser.LexicalizedParser
   * [-v] -train
   * trainFilesPath [fileRange] [-saveToSerializedFile grammarPath]
   * [-saveToTextFile grammarPath]
   * }</blockquote>
   *
   * <p>
   * In the same position as the verbose flag ({@code -v}), many other
   * options can be specified.  The most useful to an end user are:
   * <ul>
   * <LI>{@code -tLPP class} Specify a different
   * TreebankLangParserParams, for when using a different language or
   * treebank (the default is English Penn Treebank). <i>This option MUST occur
   * before any other language-specific options that are used (or else they
   * are ignored!).</i>
   * (It's usually a good idea to specify this option even when loading a
   * serialized grammar; it is necessary if the language pack specifies a
   * needed character encoding or you wish to specify language-specific
   * options on the command line.)</LI>
   * <LI>{@code -encoding charset} Specify the character encoding of the
   * input and output files.  This will override the value in the
   * {@code TreebankLangParserParams}, provided this option appears
   * <i>after</i> any {@code -tLPP} option.</LI>
   * <LI>{@code -tokenized} Says that the input is already separated
   * into whitespace-delimited tokens.  If this option is specified, any
   * tokenizer specified for the language is ignored, and a universal (Unicode)
   * tokenizer, which divides only on whitespace, is used.
   * Unless you also specify
   * {@code -escaper}, the tokens <i>must</i> all be correctly
   * tokenized tokens of the appropriate treebank for the parser to work
   * well (for instance, if using the Penn English Treebank, you must have
   * coded "(" as "-LRB-", "3/4" as "3\/4", etc.)</LI>
   * <li>{@code -escaper class} Specify a class of type
   * {@link Function}&lt;List&lt;HasWord&gt;,List&lt;HasWord&gt;&gt; to do
   * customized escaping of tokenized text.  This class will be run over the
   * tokenized text and can fix the representation of tokens. For instance,
   * it could change "(" to "-LRB-" for the Penn English Treebank.  A
   * provided escaper that does such things for the Penn English Treebank is
   * {@code edu.stanford.nlp.process.PTBEscapingProcessor}
   * <li>{@code -tokenizerFactory class} Specifies a
   * TokenizerFactory class to be used for tokenization</li>
   * <li>{@code -tokenizerOptions options} Specifies options to a
   * TokenizerFactory class to be used for tokenization.   A comma-separated
   * list. For PTBTokenizer, options of interest include
   * {@code americanize=false} and {@code asciiQuotes} (for German).
   * Note that any choice of tokenizer options that conflicts with the
   * tokenization used in the parser training data will likely degrade parser
   * performance. </li>
   * <li>{@code -sentences token } Specifies a token that marks sentence
   * boundaries.  A value of {@code newline} causes sentence breaking on
   * newlines.  A value of {@code onePerElement} causes each element
   * (using the XML {@code -parseInside} option) to be treated as a
   * sentence. All other tokens will be interpreted literally, and must be
   * exactly the same as tokens returned by the tokenizer.  For example,
   * you might specify "|||" and put that symbol sequence as a token between
   * sentences.
   * If no explicit sentence breaking option is chosen, sentence breaking
   * is done based on a set of language-particular sentence-ending patterns.
   * </li>
   * <LI>{@code -parseInside element} Specifies that parsing should only
   * be done for tokens inside the indicated XML-style
   * elements (done as simple pattern matching, rather than XML parsing).
   * For example, if this is specified as {@code sentence}, then
   * the text inside the {@code sentence} element
   * would be parsed.
   * Using "-parseInside s" gives you support for the input format of
   * Charniak's parser. Sentences cannot span elements. Whether the
   * contents of the element are treated as one sentence or potentially
   * multiple sentences is controlled by the {@code -sentences} flag.
   * The default is potentially multiple sentences.
   * This option gives support for extracting and parsing
   * text from very simple SGML and XML documents, and is provided as a
   * user convenience for that purpose. If you want to really parse XML
   * documents before NLP parsing them, you should use an XML parser, and then
   * call to a LexicalizedParser on appropriate CDATA.
   * <LI>{@code -tagSeparator char} Specifies to look for tags on words
   * following the word and separated from it by a special character
   * {@code char}.  For instance, many tagged corpora have the
   * representation "house/NN" and you would use {@code -tagSeparator /}.
   * Notes: This option requires that the input be pretokenized.
   * The separator has to be only a single character, and there is no
   * escaping mechanism. However, splitting is done on the <i>last</i>
   * instance of the character in the token, so that cases like
   * "3\/4/CD" are handled correctly.  The parser will in all normal
   * circumstances use the tag you provide, but will override it in the
   * case of very common words in cases where the tag that you provide
   * is not one that it regards as a possible tagging for the word.
   * The parser supports a format where only some of the words in a sentence
   * have a tag (if you are calling the parser programmatically, you indicate
   * them by having them implement the {@code HasTag} interface).
   * You can do this at the command-line by only having tags after some words,
   * but you are limited by the fact that there is no way to escape the
   * tagSeparator character.</LI>
   * <LI>{@code -maxLength leng} Specify the longest sentence that
   * will be parsed (and hence indirectly the amount of memory
   * needed for the parser). If this is not specified, the parser will
   * try to dynamically grow its parse chart when long sentence are
   * encountered, but may run out of memory trying to do so.</LI>
   * <LI>{@code -outputFormat styles} Choose the style(s) of output
   * sentences: {@code penn} for prettyprinting as in the Penn
   * treebank files, or {@code oneline} for printing sentences one
   * per line, {@code words}, {@code wordsAndTags},
   * {@code dependencies}, {@code typedDependencies},
   * or {@code typedDependenciesCollapsed}.
   * Multiple options may be specified as a comma-separated
   * list.  See TreePrint class for further documentation.</LI>
   * <LI>{@code -outputFormatOptions} Provide options that control the
   * behavior of various {@code -outputFormat} choices, such as
   * {@code lexicalize}, {@code stem}, {@code markHeadNodes},
   * or {@code xml}.  {@link edu.stanford.nlp.trees.TreePrint}
   * Options are specified as a comma-separated list.</LI>
   * <LI>{@code -writeOutputFiles} Write output files corresponding
   * to the input files, with the same name but a {@code ".stp"}
   * file extension.  The format of these files depends on the
   * {@code outputFormat} option.  (If not specified, output is sent
   * to stdout.)</LI>
   * <LI>{@code -outputFilesExtension} The extension that is appended to
   * the filename that is being parsed to produce an output file name (with the
   * -writeOutputFiles option). The default is {@code stp}.  Don't
   * include the period.
   * <LI>{@code -outputFilesDirectory} The directory in which output
   * files are written (when the -writeOutputFiles option is specified).
   * If not specified, output files are written in the same directory as the
   * input files.
   * <LI>{@code -nthreads} Parsing files and testing on treebanks
   * can use multiple threads.  This option tells the parser how many
   * threads to use.  A negative number indicates to use as many
   * threads as the machine has cores.
   * </ul>
   * See also the package documentation for more details and examples of use.
   *
   * @param args Command line arguments, as above
   */
public static void main(String[] args) {
    boolean train = false;
    boolean saveToSerializedFile = false;
    boolean saveToTextFile = false;
    String serializedInputFileOrUrl = null;
    String textInputFileOrUrl = null;
    String serializedOutputFileOrUrl = null;
    String textOutputFileOrUrl = null;
    String treebankPath = null;
    Treebank testTreebank = null;
    Treebank tuneTreebank = null;
    String testPath = null;
    FileFilter testFilter = null;
    String tunePath = null;
    FileFilter tuneFilter = null;
    FileFilter trainFilter = null;
    String secondaryTreebankPath = null;
    double secondaryTreebankWeight = 1.0;
    FileFilter secondaryTrainFilter = null;
    // variables needed to process the files to be parsed
    TokenizerFactory<? extends HasWord> tokenizerFactory = null;
    String tokenizerOptions = null;
    String tokenizerFactoryClass = null;
    String tokenizerMethod = null;
    // whether or not the input file has already been tokenized
    boolean tokenized = false;
    Function<List<HasWord>, List<HasWord>> escaper = null;
    String tagDelimiter = null;
    String sentenceDelimiter = null;
    String elementDelimiter = null;
    int argIndex = 0;
    if (args.length < 1) {
        log.info("Basic usage (see Javadoc for more): java edu.stanford.nlp.parser.lexparser.LexicalizedParser parserFileOrUrl filename*");
        return;
    }
    Options op = new Options();
    List<String> optionArgs = new ArrayList<>();
    String encoding = null;
    // while loop through option arguments
    while (argIndex < args.length && args[argIndex].charAt(0) == '-') {
        if (args[argIndex].equalsIgnoreCase("-train") || args[argIndex].equalsIgnoreCase("-trainTreebank")) {
            train = true;
            Pair<String, FileFilter> treebankDescription = ArgUtils.getTreebankDescription(args, argIndex, "-train");
            argIndex = argIndex + ArgUtils.numSubArgs(args, argIndex) + 1;
            treebankPath = treebankDescription.first();
            trainFilter = treebankDescription.second();
        } else if (args[argIndex].equalsIgnoreCase("-train2")) {
            // train = true;     // cdm july 2005: should require -train for this
            Triple<String, FileFilter, Double> treebankDescription = ArgUtils.getWeightedTreebankDescription(args, argIndex, "-train2");
            argIndex = argIndex + ArgUtils.numSubArgs(args, argIndex) + 1;
            secondaryTreebankPath = treebankDescription.first();
            secondaryTrainFilter = treebankDescription.second();
            secondaryTreebankWeight = treebankDescription.third();
        } else if (args[argIndex].equalsIgnoreCase("-tLPP") && (argIndex + 1 < args.length)) {
            try {
                op.tlpParams = (TreebankLangParserParams) Class.forName(args[argIndex + 1]).newInstance();
            } catch (ClassNotFoundException e) {
                log.info("Class not found: " + args[argIndex + 1]);
                throw new RuntimeException(e);
            } catch (InstantiationException e) {
                log.info("Couldn't instantiate: " + args[argIndex + 1] + ": " + e.toString());
                throw new RuntimeException(e);
            } catch (IllegalAccessException e) {
                log.info("Illegal access" + e);
                throw new RuntimeException(e);
            }
            argIndex += 2;
        } else if (args[argIndex].equalsIgnoreCase("-encoding")) {
            // sets encoding for TreebankLangParserParams
            // redone later to override any serialized parser one read in
            encoding = args[argIndex + 1];
            op.tlpParams.setInputEncoding(encoding);
            op.tlpParams.setOutputEncoding(encoding);
            argIndex += 2;
        } else if (args[argIndex].equalsIgnoreCase("-tokenized")) {
            tokenized = true;
            argIndex += 1;
        } else if (args[argIndex].equalsIgnoreCase("-escaper")) {
            try {
                escaper = ReflectionLoading.loadByReflection(args[argIndex + 1]);
            } catch (Exception e) {
                log.info("Couldn't instantiate escaper " + args[argIndex + 1] + ": " + e);
            }
            argIndex += 2;
        } else if (args[argIndex].equalsIgnoreCase("-tokenizerOptions")) {
            tokenizerOptions = args[argIndex + 1];
            argIndex += 2;
        } else if (args[argIndex].equalsIgnoreCase("-tokenizerFactory")) {
            tokenizerFactoryClass = args[argIndex + 1];
            argIndex += 2;
        } else if (args[argIndex].equalsIgnoreCase("-tokenizerMethod")) {
            tokenizerMethod = args[argIndex + 1];
            argIndex += 2;
        } else if (args[argIndex].equalsIgnoreCase("-sentences")) {
            sentenceDelimiter = args[argIndex + 1];
            if (sentenceDelimiter.equalsIgnoreCase("newline")) {
                sentenceDelimiter = "\n";
            }
            argIndex += 2;
        } else if (args[argIndex].equalsIgnoreCase("-parseInside")) {
            elementDelimiter = args[argIndex + 1];
            argIndex += 2;
        } else if (args[argIndex].equalsIgnoreCase("-tagSeparator")) {
            tagDelimiter = args[argIndex + 1];
            argIndex += 2;
        } else if (args[argIndex].equalsIgnoreCase("-loadFromSerializedFile") || args[argIndex].equalsIgnoreCase("-model")) {
            // load the parser from a binary serialized file
            // the next argument must be the path to the parser file
            serializedInputFileOrUrl = args[argIndex + 1];
            argIndex += 2;
        } else if (args[argIndex].equalsIgnoreCase("-loadFromTextFile")) {
            // load the parser from declarative text file
            // the next argument must be the path to the parser file
            textInputFileOrUrl = args[argIndex + 1];
            argIndex += 2;
        } else if (args[argIndex].equalsIgnoreCase("-saveToSerializedFile")) {
            saveToSerializedFile = true;
            if (ArgUtils.numSubArgs(args, argIndex) < 1) {
                log.info("Missing path: -saveToSerialized filename");
            } else {
                serializedOutputFileOrUrl = args[argIndex + 1];
            }
            argIndex += 2;
        } else if (args[argIndex].equalsIgnoreCase("-saveToTextFile")) {
            // save the parser to declarative text file
            saveToTextFile = true;
            textOutputFileOrUrl = args[argIndex + 1];
            argIndex += 2;
        } else if (args[argIndex].equalsIgnoreCase("-saveTrainTrees")) {
            // save the training trees to a binary file
            op.trainOptions.trainTreeFile = args[argIndex + 1];
            argIndex += 2;
        } else if (args[argIndex].equalsIgnoreCase("-treebank") || args[argIndex].equalsIgnoreCase("-testTreebank") || args[argIndex].equalsIgnoreCase("-test")) {
            Pair<String, FileFilter> treebankDescription = ArgUtils.getTreebankDescription(args, argIndex, "-test");
            argIndex = argIndex + ArgUtils.numSubArgs(args, argIndex) + 1;
            testPath = treebankDescription.first();
            testFilter = treebankDescription.second();
        } else if (args[argIndex].equalsIgnoreCase("-tune")) {
            Pair<String, FileFilter> treebankDescription = ArgUtils.getTreebankDescription(args, argIndex, "-tune");
            argIndex = argIndex + ArgUtils.numSubArgs(args, argIndex) + 1;
            tunePath = treebankDescription.first();
            tuneFilter = treebankDescription.second();
        } else {
            int oldIndex = argIndex;
            argIndex = op.setOptionOrWarn(args, argIndex);
            optionArgs.addAll(Arrays.asList(args).subList(oldIndex, argIndex));
        }
    }
    if (tuneFilter != null || tunePath != null) {
        if (tunePath == null) {
            if (treebankPath == null) {
                throw new RuntimeException("No tune treebank path specified...");
            } else {
                log.info("No tune treebank path specified.  Using train path: \"" + treebankPath + '\"');
                tunePath = treebankPath;
            }
        }
        tuneTreebank = op.tlpParams.testMemoryTreebank();
        tuneTreebank.loadPath(tunePath, tuneFilter);
    }
    if (!train && op.testOptions.verbose) {
        StringUtils.logInvocationString(log, args);
    }
    // always initialized in next if-then-else block
    LexicalizedParser lp;
    if (train) {
        StringUtils.logInvocationString(log, args);
        // so we train a parser using the treebank
        GrammarCompactor compactor = null;
        if (op.trainOptions.compactGrammar() == 3) {
            compactor = new ExactGrammarCompactor(op, false, false);
        }
        Treebank trainTreebank = makeTreebank(treebankPath, op, trainFilter);
        Treebank secondaryTrainTreebank = null;
        if (secondaryTreebankPath != null) {
            secondaryTrainTreebank = makeSecondaryTreebank(secondaryTreebankPath, op, secondaryTrainFilter);
        }
        List<List<TaggedWord>> extraTaggedWords = null;
        if (op.trainOptions.taggedFiles != null) {
            extraTaggedWords = new ArrayList<>();
            List<TaggedFileRecord> fileRecords = TaggedFileRecord.createRecords(new Properties(), op.trainOptions.taggedFiles);
            for (TaggedFileRecord record : fileRecords) {
                for (List<TaggedWord> sentence : record.reader()) {
                    extraTaggedWords.add(sentence);
                }
            }
        }
        lp = getParserFromTreebank(trainTreebank, secondaryTrainTreebank, secondaryTreebankWeight, compactor, op, tuneTreebank, extraTaggedWords);
    } else if (textInputFileOrUrl != null) {
        // so we load the parser from a text grammar file
        lp = getParserFromTextFile(textInputFileOrUrl, op);
    } else {
        // so we load a serialized parser
        if (serializedInputFileOrUrl == null && argIndex < args.length) {
            // the next argument must be the path to the serialized parser
            serializedInputFileOrUrl = args[argIndex];
            argIndex++;
        }
        if (serializedInputFileOrUrl == null) {
            log.info("No grammar specified, exiting...");
            return;
        }
        String[] extraArgs = new String[optionArgs.size()];
        extraArgs = optionArgs.toArray(extraArgs);
        try {
            lp = loadModel(serializedInputFileOrUrl, op, extraArgs);
            op = lp.op;
        } catch (IllegalArgumentException e) {
            log.info("Error loading parser, exiting...");
            throw e;
        }
    }
    // set up tokenizerFactory with options if provided
    if (tokenizerFactoryClass != null || tokenizerOptions != null) {
        try {
            if (tokenizerFactoryClass != null) {
                Class<TokenizerFactory<? extends HasWord>> clazz = ErasureUtils.uncheckedCast(Class.forName(tokenizerFactoryClass));
                Method factoryMethod;
                if (tokenizerOptions != null) {
                    factoryMethod = clazz.getMethod(tokenizerMethod != null ? tokenizerMethod : "newWordTokenizerFactory", String.class);
                    tokenizerFactory = ErasureUtils.uncheckedCast(factoryMethod.invoke(null, tokenizerOptions));
                } else {
                    factoryMethod = clazz.getMethod(tokenizerMethod != null ? tokenizerMethod : "newTokenizerFactory");
                    tokenizerFactory = ErasureUtils.uncheckedCast(factoryMethod.invoke(null));
                }
            } else {
                // have options but no tokenizer factory.  use the parser
                // langpack's factory and set its options
                tokenizerFactory = lp.op.langpack().getTokenizerFactory();
                tokenizerFactory.setOptions(tokenizerOptions);
            }
        } catch (IllegalAccessException | InvocationTargetException | ClassNotFoundException | NoSuchMethodException e) {
            log.info("Couldn't instantiate TokenizerFactory " + tokenizerFactoryClass + " with options " + tokenizerOptions);
            throw new RuntimeException(e);
        }
    }
    // OVERWRITTEN BY ONE SPECIFIED IN SERIALIZED PARSER
    if (encoding != null) {
        op.tlpParams.setInputEncoding(encoding);
        op.tlpParams.setOutputEncoding(encoding);
    }
    if (testFilter != null || testPath != null) {
        if (testPath == null) {
            if (treebankPath == null) {
                throw new RuntimeException("No test treebank path specified...");
            } else {
                log.info("No test treebank path specified.  Using train path: \"" + treebankPath + '\"');
                testPath = treebankPath;
            }
        }
        testTreebank = op.tlpParams.testMemoryTreebank();
        testTreebank.loadPath(testPath, testFilter);
    }
    op.trainOptions.sisterSplitters = Generics.newHashSet(Arrays.asList(op.tlpParams.sisterSplitters()));
    // Now what do we do with the parser we've made
    if (saveToTextFile) {
        // save the parser to textGrammar format
        if (textOutputFileOrUrl != null) {
            lp.saveParserToTextFile(textOutputFileOrUrl);
        } else {
            log.info("Usage: must specify a text grammar output path");
        }
    }
    if (saveToSerializedFile) {
        if (serializedOutputFileOrUrl != null) {
            lp.saveParserToSerialized(serializedOutputFileOrUrl);
        } else if (textOutputFileOrUrl == null && testTreebank == null) {
            // no saving/parsing request has been specified
            log.info("usage: " + "java edu.stanford.nlp.parser.lexparser.LexicalizedParser " + "-train trainFilesPath [fileRange] -saveToSerializedFile serializedParserFilename");
        }
    }
    if (op.testOptions.verbose || train) {
        // Tell the user a little or a lot about what we have made
        // get lexicon size separately as it may have its own prints in it....
        String lexNumRules = lp.lex != null ? Integer.toString(lp.lex.numRules()) : "";
        log.info("Grammar\tStates\tTags\tWords\tUnaryR\tBinaryR\tTaggings");
        log.info("Grammar\t" + lp.stateIndex.size() + '\t' + lp.tagIndex.size() + '\t' + lp.wordIndex.size() + '\t' + (lp.ug != null ? lp.ug.numRules() : "") + '\t' + (lp.bg != null ? lp.bg.numRules() : "") + '\t' + lexNumRules);
        log.info("ParserPack is " + op.tlpParams.getClass().getName());
        log.info("Lexicon is " + lp.lex.getClass().getName());
        if (op.testOptions.verbose) {
            log.info("Tags are: " + lp.tagIndex);
        // log.info("States are: " + lp.pd.stateIndex); // This is too verbose. It was already printed out by the below printOptions command if the flag -printStates is given (at training time)!
        }
        printOptions(false, op);
    }
    if (testTreebank != null) {
        // test parser on treebank
        EvaluateTreebank evaluator = new EvaluateTreebank(lp);
        evaluator.testOnTreebank(testTreebank);
    } else if (argIndex >= args.length) {
        // no more arguments, so we just parse our own test sentence
        PrintWriter pwOut = op.tlpParams.pw();
        PrintWriter pwErr = op.tlpParams.pw(System.err);
        ParserQuery pq = lp.parserQuery();
        if (pq.parse(op.tlpParams.defaultTestSentence())) {
            lp.getTreePrint().printTree(pq.getBestParse(), pwOut);
        } else {
            pwErr.println("Error. Can't parse test sentence: " + op.tlpParams.defaultTestSentence());
        }
    } else {
        // We parse filenames given by the remaining arguments
        ParseFiles.parseFiles(args, argIndex, tokenized, tokenizerFactory, elementDelimiter, sentenceDelimiter, escaper, tagDelimiter, op, lp.getTreePrint(), lp);
    }
}

Example 30 with TaggedWord

use of edu.stanford.nlp.ling.TaggedWord in project CoreNLP by stanfordnlp.

the class LexicalizedParserQuery method parseInternal.

/**
   * Parse a sentence represented as a List of tokens.
   * The text must already have been tokenized and
   * normalized into tokens that are appropriate to the treebank
   * which was used to train the parser.  The tokens can be of
   * multiple types, and the list items need not be homogeneous as to type
   * (in particular, only some words might be given tags):
   * <ul>
   * <li>If a token implements HasWord, then the word to be parsed is
   * given by its word() value.</li>
   * <li>If a token implements HasTag and the tag() value is not
   * null or the empty String, then the parser is strongly advised to assign
   * a part of speech tag that <i>begins</i> with this String.</li>
   * </ul>
   *
   * @param sentence The sentence to parse
   * @return true Iff the sentence was accepted by the grammar
   * @throws UnsupportedOperationException If the Sentence is too long or
   *                                       of zero length or the parse
   *                                       otherwise fails for resource reasons
   */
private boolean parseInternal(List<? extends HasWord> sentence) {
    parseSucceeded = false;
    parseNoMemory = false;
    parseUnparsable = false;
    parseSkipped = false;
    parseFallback = false;
    whatFailed = null;
    addedPunct = false;
    originalSentence = sentence;
    int length = sentence.size();
    if (length == 0) {
        parseSkipped = true;
        throw new UnsupportedOperationException("Can't parse a zero-length sentence!");
    }
    List<HasWord> sentenceB;
    if (op.wordFunction != null) {
        sentenceB = Generics.newArrayList();
        for (HasWord word : originalSentence) {
            if (word instanceof Label) {
                Label label = (Label) word;
                Label newLabel = label.labelFactory().newLabel(label);
                if (newLabel instanceof HasWord) {
                    sentenceB.add((HasWord) newLabel);
                } else {
                    throw new AssertionError("This should have been a HasWord");
                }
            } else if (word instanceof HasTag) {
                TaggedWord tw = new TaggedWord(word.word(), ((HasTag) word).tag());
                sentenceB.add(tw);
            } else {
                sentenceB.add(new Word(word.word()));
            }
        }
        for (HasWord word : sentenceB) {
            word.setWord(op.wordFunction.apply(word.word()));
        }
    } else {
        sentenceB = new ArrayList<>(sentence);
    }
    if (op.testOptions.addMissingFinalPunctuation) {
        addedPunct = addSentenceFinalPunctIfNeeded(sentenceB, length);
    }
    if (length > op.testOptions.maxLength) {
        parseSkipped = true;
        throw new UnsupportedOperationException("Sentence too long: length " + length);
    }
    TreePrint treePrint = getTreePrint();
    PrintWriter pwOut = op.tlpParams.pw();
    //Insert the boundary symbol
    if (sentence.get(0) instanceof CoreLabel) {
        CoreLabel boundary = new CoreLabel();
        boundary.setWord(Lexicon.BOUNDARY);
        boundary.setValue(Lexicon.BOUNDARY);
        boundary.setTag(Lexicon.BOUNDARY_TAG);
        //1-based indexing used in the parser
        boundary.setIndex(sentence.size() + 1);
        sentenceB.add(boundary);
    } else {
        sentenceB.add(new TaggedWord(Lexicon.BOUNDARY, Lexicon.BOUNDARY_TAG));
    }
    if (Thread.interrupted()) {
        throw new RuntimeInterruptedException();
    }
    if (op.doPCFG) {
        if (!pparser.parse(sentenceB)) {
            return parseSucceeded;
        }
        if (op.testOptions.verbose) {
            pwOut.println("PParser output");
            // getBestPCFGParse(false).pennPrint(pwOut); // with scores on nodes
            // without scores on nodes
            treePrint.printTree(getBestPCFGParse(false), pwOut);
        }
    }
    if (Thread.interrupted()) {
        throw new RuntimeInterruptedException();
    }
    if (op.doDep && !op.testOptions.useFastFactored) {
        if (!dparser.parse(sentenceB)) {
            return parseSucceeded;
        }
        // so don't calculate the best parse twice!
        if (op.testOptions.verbose) {
            pwOut.println("DParser output");
            treePrint.printTree(dparser.getBestParse(), pwOut);
        }
    }
    if (Thread.interrupted()) {
        throw new RuntimeInterruptedException();
    }
    if (op.doPCFG && op.doDep) {
        if (!bparser.parse(sentenceB)) {
            return parseSucceeded;
        } else {
            parseSucceeded = true;
        }
    }
    return true;
}