使用opennlp自定义命名实体

序

本文主要研究一下如何使用opennlp自定义命名实体，标注训练及模型运用。

maven


        <dependency>
            <groupId>org.apache.opennlp</groupId>
            <artifactId>opennlp-tools</artifactId>
            <version>1.8.4</version>
        </dependency>
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实践

训练模型


        // train the name finder
        String typedEntities = "<START:organization> NATO <END>\n" +
                "<START:location> United States <END>\n" +
                "<START:organization> NATO Parliamentary Assembly <END>\n" +
                "<START:location> Edinburgh <END>\n" +
                "<START:location> Britain <END>\n" +
                "<START:person> Anders Fogh Rasmussen <END>\n" +
                "<START:location> U . S . <END>\n" +
                "<START:person> Barack Obama <END>\n" +
                "<START:location> Afghanistan <END>\n" +
                "<START:person> Rasmussen <END>\n" +
                "<START:location> Afghanistan <END>\n" +
                "<START:date> 2010 <END>";
        ObjectStream<NameSample> sampleStream = new NameSampleDataStream(
                new PlainTextByLineStream(new MockInputStreamFactory(typedEntities), "UTF-8"));
 
        TrainingParameters params = new TrainingParameters();
        params.put(TrainingParameters.ALGORITHM_PARAM, "MAXENT");
        params.put(TrainingParameters.ITERATIONS_PARAM, 70);
        params.put(TrainingParameters.CUTOFF_PARAM, 1);
 
        TokenNameFinderModel nameFinderModel = NameFinderME.train("eng", null, sampleStream,
                params, TokenNameFinderFactory.create(null, null, Collections.emptyMap(), new BioCodec()));
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opennlp使用<START> 及 <END>来进行自定义标注实体，命名实体的话则在START之后用冒号标明，比如<START:person>

参数说明

ALGORITHM_PARAM

On the engineering level, using maxent is an excellent way of creating programs which perform very difficult classification tasks very well.

ITERATIONS_PARAM

number of training iterations, ignored if -params is used.

CUTOFF_PARAM

minimal number of times a feature must be seen

使用模型

上面训练完模型之后，就可以使用该模型进行解析


      NameFinderME nameFinder = new NameFinderME(nameFinderModel);
 
        // now test if it can detect the sample sentences
 
        String[] sentence = "NATO United States Barack Obama".split("\\s+");
 
        Span[] names = nameFinder.find(sentence);
 
        Stream.of(names)
                .forEach(span -> {
                    String named = IntStream.range(span.getStart(),span.getEnd())
                            .mapToObj(i -> sentence[i])
                            .collect(Collectors.joining(" "));
                    System.out.println("find type: "+ span.getType()+",name: " + named);
                });
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输出如下：


find type: organization,name: NATO
find type: location,name: United States
find type: person,name: Barack Obama
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小结

opennlp的自定义命名实体的标注，给以了一定定制空间，方便开发者定制各自领域特殊的命名实体，以提高特定命名实体分词的准确性。

使用opennlp自定义命名实体

序

maven

实践

训练模型

使用模型

小结

doc