This is the main public code repository of the Computational Language Understanding (CLU) Lab led by Mihai Surdeanu at University of Arizona. This repository contains:
- A rule-based event extraction (EE) framework called Odin (Open Domain INformer) in the
org.clulab.odinpackage. See Odin's Wiki page for more details. - Two full-fledged Rhetorical Structure Theory (RST) discourse parsers. The discourse parsers are transparently included in our natural language (NL) processors (see below). The version in
CoreNLPProcessorrelies on constituent syntax, whereas the one inFastNLPProcessoruses dependency syntax. They perform approximately the same, but the latter is much faster. - A machine learning (ML) package (
org.clulab.learning), which includes implementations for common ML algorithms (e.g., Perceptron, Logistic Regression, Support Vector Machines, Random Forests) for both classification and ranking. - A suite of NL processors in the
org.clulab.processorspackage. We currently provide the following APIs:CoreNLPProcessor- a wrapper for Stanford's CoreNLP, using its constituent parser or dependency parser;FastNLPProcessor- a wrapper for Stanford's CoreNLP, but using its neural-network dependency parser;BioNLPProcessor- a version ofCoreNLPProcessortuned for the biomedical domain: better tokenization for biomedical texts, improved POS tagging for the bio domain, and a custom NER for this domain that recognizes entities relevant in this domain such as proteins, chemical, and biological processes;FastBioNLPProcessor- a version ofFastNLPProcessortuned for the biomedical domain, similarly toBioNLPProcessor;CluProcessor- an in-house processor (licensed under the Apache license) that contains: tokenization (using Antlr), lemmatization (using MorphaStemmer), POS tagging, named entity recognition (NER), and shallow syntax or chunking. The last three components are implemented using a multi-task learning framework implemented using the Scala wrapper of DyNet. Performance is comparable toFastNLPProcessor, under a more permissive license. Additionally, the memory footprint ofCluProcessoris smaller than that ofFastNLPProcessor, so it may be more appropriate for older machines. Coming soon: dependency parsing, and support for multiple languages.
This software requires Java 1.8, Scala 2.11, and CoreNLP 3.x or higher.
Our code is licensed as follows:
main, odin- Apache License Version 2.0. Please note that these subprojects do not interact with thecorenlpsubproject below.corenlp- GLP Version 3 or higher, due to the dependency on Stanford's CoreNLP. If you use onlyCluProcessor, this dependency does not have to be included in your project.
(c) Mihai Surdeanu, 2013 -
Authors: Mihai Surdeanu, Marco Valenzuela, Gustave Hahn-Powell, Peter Jansen, Daniel Fried, Dane Bell, Tom Hicks, and Keith Alcock.
If you use one of our discourse parsers, please cite this paper:
Mihai Surdeanu, Thomas Hicks, and Marco A. Valenzuela-Escarcega. Two Practical Rhetorical Structure Theory Parsers. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies: Software Demonstrations (NAACL HLT), 2015. [pdf] [bib]
If you use Odin, our event extraction framework, please cite this paper:
Marco A. Valenzuela-Escarcega, Gustave Hahn-Powell, Thomas Hicks, and Mihai Surdeanu. A Domain-independent Rule-based Framework for Event Extraction. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing: Software Demonstrations (ACL-IJCNLP), 2015. [pdf] [bib]
If you use CoreNLPProcessor, please cite Stanford's paper:
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. The Stanford CoreNLP Natural Language Processing Toolkit. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), 2014. [pdf] [bib]
If you use CluProcessor, please cite this paper:
Mihai Surdeanu and Christopher D. Manning. Ensemble Models for Dependency Parsing: Cheap and Good? In Proceedings of the North American Chapter of the Association for Computational Linguistics Conference (NAACL-2010), 2010. [pdf] [bib]
If you use anything else in this package, please link to this github page.
The bulk of this software is available on Maven Central. To use, simply add the dependencies below to your pom.xml (please replace x.x.x with an actual version number; the latest stable version is 8.0.3). One component is not available at Maven Central because of size limitations there. However, Maven seems to fetch this transitive dependency, processors-models, automatically.
<dependency>
<groupId>org.clulab</groupId>
<artifactId>processors-corenlp_2.12</artifactId>
<version>x.x.x</version>
</dependency>
<dependency>
<groupId>org.clulab</groupId>
<artifactId>processors-main_2.12</artifactId>
<version>x.x.x</version>
</dependency>
<dependency>
<groupId>org.clulab</groupId>
<artifactId>processors-odin_2.12</artifactId>
<version>x.x.x</version>
</dependency>SBT is not able to fetch the dependency without further configuration. An additional resolver must be provided, but thereafter the procedure is the same. The equivalent SBT dependencies are:
resolvers += "Artifactory" at "http://artifactory.cs.arizona.edu:8081/artifactory/sbt-release"
libraryDependencies ++= {
val procVer = "x.x.x"
Seq(
"org.clulab" %% "processors-main" % procVer,
"org.clulab" %% "processors-corenlp" % procVer,
"org.clulab" %% "processors-odin" % procVer
)
}Most of the processors dependencies are captured in the build file. However, a few processors unit tests depend also on svm-rank, which should be installed separately. Simply installing the svm-rank binaries to /usr/local/bin (or another generic location on your OS) solves the problem:
https://www.cs.cornell.edu/people/tj/svm_light/svm_rank.html
brew tap myedibleenso/nlp
brew install svmlight svmrankAlternatively, you can run just the unit tests that do not require external binaries with the following command:
sbt 'test-only -- -l NeedsExternalBinary'
- Simple API - the APIs provided are, at least in my opinion, simpler than those provided for the original code. For example, when using CoreNLP you won't have to deal with hash maps that take class objects as keys. Instead, we use mostly arrays of integers or strings, which are self explanatory.
- Memory efficient - arrays are more memory efficient than hash maps. Furthermore, we used our own implementation to intern strings (i.e., avoiding the storage of duplicated strings multiple times). Due to these changes, I measured up to 99% decrease in memory for the annotations corresponding to a typical natural language text, when compared to the original CoreNLP code. (Note: this reduction takes effect only after CoreNLP finishes its work.)
- Faster access - again, because we use arrays instead of hash maps, access to the NL annotations (once constructed) is considerably faster than in the original Stanford code.
- Tool-independent API - we support multiple NL and machine learning tools. If you use this code, you will have to change your code only minimally (i.e., only the constructor for the
Processorobject). - Discourse parsing - we include a complete RST parser; simply instantiate
CoreNLPProcessorwithwithDiscourse = true. - Biomedical tools - we now include tools to process biomedical texts, which can be used under the same simple interface. We also offer event extraction tools for the biomedical domain.
- Rule-based event extraction - we include Odin, an event extraction framework, which can be customized to various domains.
- Apache license - our
CluProcessorreplicates most functionality of Stanford's CoreNLP (tokenization, POS tagging, dependency parsing, more soon!) under the more permissive Apache license.
This is a standard sbt project, so use the usual commands, e.g., sbt compile, sbt assembly, to compile.
Add the generated jar files under target/ to your $CLASSPATH, along with the other necessary dependency jars. Take a look at build.sbt to see which dependencies are necessary at runtime.
Most of the examples here use Scala. However, this software can be used as is from Java as well! Scroll down towards the end of this document to see a Java usage example.
(see corenlp/src/main/scala/org/clulab/processors/examples/ProcessorExample.scala for a complete running example)
import org.clulab.processors.corenlp.CoreNLPProcessor
import org.clulab.processors.shallownlp.ShallowNLPProcessor
import org.clulab.processors.{Document, Processor}
import org.clulab.struct.DirectedGraphEdgeIterator
// create the processor
// any processor works here! Try FastNLPProcessor, BioNLPProcessor, or our own CluProcessor
// use no arguments in the c'tor if you don't need the discourse parser
val proc:Processor = new CoreNLPProcessor(ShallowNLPProcessor.WITH_DISCOURSE)
// the actual work is done here
val doc = proc.annotate("John Smith went to China. He visited Beijing, on January 10th, 2013.")
// you are basically done. the rest of this code simply prints out the annotations
// let's print the sentence-level annotations
var sentenceCount = 0
for (sentence <- doc.sentences) {
println("Sentence #" + sentenceCount + ":")
println("Tokens: " + sentence.words.mkString(" "))
println("Start character offsets: " + sentence.startOffsets.mkString(" "))
println("End character offsets: " + sentence.endOffsets.mkString(" "))
// these annotations are optional, so they are stored using Option objects, hence the foreach statement
sentence.lemmas.foreach(lemmas => println(s"Lemmas: ${lemmas.mkString(" ")}"))
sentence.tags.foreach(tags => println(s"POS tags: ${tags.mkString(" ")}"))
sentence.chunks.foreach(chunks => println(s"Chunks: ${chunks.mkString(" ")}"))
sentence.entities.foreach(entities => println(s"Named entities: ${entities.mkString(" ")}"))
sentence.norms.foreach(norms => println(s"Normalized entities: ${norms.mkString(" ")}"))
sentence.dependencies.foreach(dependencies => {
println("Syntactic dependencies:")
val iterator = new DirectedGraphEdgeIterator[String](dependencies)
while(iterator.hasNext) {
val dep = iterator.next
// note that we use offsets starting at 0 (unlike CoreNLP, which uses offsets starting at 1)
println(" head:" + dep._1 + " modifier:" + dep._2 + " label:" + dep._3)
}
})
sentence.syntacticTree.foreach(tree => {
println("Constituent tree: " + tree)
// see the org.clulab.utils.Tree class for more information
// on syntactic trees, including access to head phrases/words
})
sentenceCount += 1
println("\n")
}
// let's print the coreference chains
doc.coreferenceChains.foreach(chains => {
for (chain <- chains.getChains) {
println("Found one coreference chain containing the following mentions:")
for (mention <- chain) {
// note that all these offsets start at 0 too
println("\tsentenceIndex:" + mention.sentenceIndex +
" headIndex:" + mention.headIndex +
" startTokenOffset:" + mention.startOffset +
" endTokenOffset:" + mention.endOffset +
" text: " + doc.sentences(mention.sentenceIndex).words.slice(mention.startOffset, mention.endOffset).mkString("[", " ", "]"))
}
}
})
// let's print the discourse tree
doc.discourseTree.foreach(dt => {
println("Document-wide discourse tree:")
println(dt.toString())
})The above code generates the following output:
Sentence #0:
Tokens: John Smith went to China .
Start character offsets: 0 5 11 16 19 24
End character offsets: 4 10 15 18 24 25
Lemmas: John Smith go to China .
POS tags: NNP NNP VBD TO NNP .
Chunks: B-NP I-NP B-VP B-PP B-NP O
Named entities: PERSON PERSON O O LOCATION O
Normalized entities: O O O O O O
Syntactic dependencies:
head:1 modifier:0 label:nn
head:2 modifier:1 label:nsubj
head:2 modifier:4 label:prep_to
Constituent tree: (ROOT (S (NP (NNP John) (NNP Smith)) (VP (VBD went) (PP (TO to) (NP (NNP China)))) (. .)))
Sentence #1:
Tokens: He visited Beijing , on January 10th , 2013 .
Start character offsets: 26 29 37 44 46 49 57 61 63 67
End character offsets: 28 36 44 45 48 56 61 62 67 68
Lemmas: he visit Beijing , on January 10th , 2013 .
POS tags: PRP VBD NNP , IN NNP JJ , CD .
Chunks: B-NP B-VP B-NP O B-PP B-NP I-NP I-NP I-NP O
Named entities: O O LOCATION O O DATE DATE DATE DATE O
Normalized entities: O O O O O 2013-01-10 2013-01-10 2013-01-10 2013-01-10 O
Syntactic dependencies:
head:1 modifier:0 label:nsubj
head:1 modifier:2 label:dobj
head:1 modifier:8 label:tmod
head:2 modifier:5 label:prep_on
head:5 modifier:6 label:amod
Constituent tree: (ROOT (S (NP (PRP He)) (VP (VBD visited) (NP (NP (NNP Beijing)) (, ,) (PP (IN on) (NP (NNP January) (JJ 10th))) (, ,)) (NP-TMP (CD 2013))) (. .)))
Found one coreference chain containing the following mentions:
sentenceIndex:1 headIndex:2 startTokenOffset:2 endTokenOffset:3 text: [Beijing]
Found one coreference chain containing the following mentions:
sentenceIndex:1 headIndex:0 startTokenOffset:0 endTokenOffset:1 text: [He]
sentenceIndex:0 headIndex:1 startTokenOffset:0 endTokenOffset:2 text: [John Smith]
Found one coreference chain containing the following mentions:
sentenceIndex:1 headIndex:5 startTokenOffset:5 endTokenOffset:9 text: [January 10th , 2013]
Found one coreference chain containing the following mentions:
sentenceIndex:0 headIndex:4 startTokenOffset:4 endTokenOffset:5 text: [China]
Document-wide discourse tree:
elaboration (LeftToRight)
TEXT:John Smith went to China .
TEXT:He visited Beijing , on January 10th , 2013 .
For more details about the annotation data structures, please see the org/clulab/processor/Document.scala file.
Changing processors is trivial: just replace the first line in the above example with:
val proc:Processor = new FastNLPProcessor()
// everything else stays the sameFastNLPProcessor uses the Stanford tokenizer, POS tagger, and NER, but replaces its parser with maltparser, trained to generated Stanford "basic" (rather than "collapsed") dependencies. This means that this annotator does not produce constituent trees and coreference chains. However, because of the faster parser, you should see a speedup increase of at least one order of magnitude. The output of the above code with FastNLPProcessor is:
Sentence #0:
Tokens: John Smith went to China .
Start character offsets: 0 5 11 16 19 24
End character offsets: 4 10 15 18 24 25
Lemmas: John Smith go to China .
POS tags: NNP NNP VBD TO NNP .
Chunks: B-NP I-NP B-VP B-PP B-NP O
Named entities: PERSON PERSON O O LOCATION O
Normalized entities: O O O O O O
Syntactic dependencies:
head:1 modifier:0 label:nn
head:2 modifier:1 label:nsubj
head:2 modifier:3 label:prep
head:2 modifier:5 label:punct
head:3 modifier:4 label:pobj
Sentence #1:
Tokens: He visited Beijing , on January 10th , 2013 .
Start character offsets: 26 29 37 44 46 49 57 61 63 67
End character offsets: 28 36 44 45 48 56 61 62 67 68
Lemmas: he visit Beijing , on January 10th , 2013 .
POS tags: PRP VBD NNP , IN NNP JJ , CD .
Chunks: B-NP B-VP B-NP O B-PP B-NP I-NP I-NP I-NP O
Named entities: O O LOCATION O O DATE DATE DATE DATE O
Normalized entities: O O O O O 2013-01-10 2013-01-10 2013-01-10 2013-01-10 O
Syntactic dependencies:
head:1 modifier:0 label:nsubj
head:1 modifier:2 label:dobj
head:1 modifier:3 label:punct
head:1 modifier:4 label:prep
head:1 modifier:9 label:punct
head:4 modifier:5 label:pobj
head:5 modifier:6 label:amod
head:5 modifier:7 label:punct
head:5 modifier:8 label:num
val doc = proc.annotateFromSentences(List("John Smith went to China.", "He visited Beijing."))
// everything else stays the sameval doc = annotateFromTokens(List(
List("John", "Smith", "went", "to", "China", "."),
List("There", ",", "he", "visited", "Beijing", ".")))
// everything else stays the sameYou can of course use only some of the annotators provided by CoreNLP by calling them individually. To illustrate,
the Processor.annotate() method is implemented as follows:
def annotate(doc:Document): Document = {
tagPartsOfSpeech(doc)
lemmatize(doc)
recognizeNamedEntities(doc)
parse(doc)
chunking(doc)
labelSemanticRoles(doc)
resolveCoreference(doc)
discourse(doc)
doc.clear()
doc
}(Note that CoreNLP currently does not support semantic role labeling.) You can use just a few of these annotators. For example, if you need just POS tags, lemmas and named entities, you could use the following code:
val doc = proc.mkDocument("John Smith went to China. He visited Beijing, on January 10th, 2013.")
proc.tagPartsOfSpeech(doc)
proc.lemmatize(doc)
proc.recognizeNamedEntities(doc)
doc.clear()Note that the last method called (doc.clear()) clears the internal structures created by the actual CoreNLP annotators.
This saves a lot of memory, so, although it is not strictly necessary, I recommend you call it.
This package also offers serialization code for the generated annotations. The two scenarios currently supported are:
// saving to a PrintWriter, pw
val someAnnotation = proc.annotate(someText)
val serializer = new DocumentSerializer
serializer.save(someAnnotation, pw)
// loading from an BufferedReader, br
val someAnnotation = serializer.load(br)// saving to a String object, savedString
val someAnnotation = proc.annotate(someText)
val serializer = new DocumentSerializer
val savedString = serializer.save(someAnnotation)
// loading from a String object, fromString
val someAnnotation = serializer.load(fromString)Note that space required for these serialized annotations is considerably smaller (8 to 10 times) than the corresponding serialized Java objects. This is because we store only the information required to recreate these annotations (e.g., words, lemmas, etc.) without storing any of the Java/Scala objects and classes.
As of v5.9.6, Document and Mention instances can be serialized to/from json (see the complete working example).
Classes that implement the Processor trait intern String objects to avoid allocating memory repeatedly for the same string.
This is implemented by maintaining an internal dictionary of strings previously seen. This dictionary is unlikely to
use a lot of memory due to the Zipfian distribution of language. But, if memory usage is a big concern, it can be cleaned by
calling:
Processor.in.clear()I recommend you do this only after you annotated all the documents you plan to keep in memory.
Although I see no good reason for doing this, you can disable the interning of strings completely by setting the internStrings = false in the
CoreNLProcessor constructor, as such:
val processor = new CoreNLPProcessor(internStrings = false)Scala is (largely) compatible with Java, so this library can be directly used from Java. Below is Java code that translates most of the functionality from the first Scala example in this document to Java:
package org.clulab.processors;
import org.clulab.discourse.rstparser.DiscourseTree;
import org.clulab.processors.fastnlp.FastNLPProcessor;
import org.clulab.processors.corenlp.CoreNLPProcessor;
import org.clulab.struct.CorefMention;
import org.clulab.struct.DirectedGraphEdgeIterator;
public class ProcessorsJavaExample {
public static void main(String [] args) throws Exception {
// create the processor
Processor proc = new CoreNLPProcessor(true, false, 0, 100);
// for much faster processing, use FastNLPProcessor
// Processor proc = new FastNLPProcessor(true, false);
// the actual work is done here
Document doc = proc.annotate("John Smith went to China. He visited Beijing, on January 10th, 2013.", false);
// you are basically done. the rest of this code simply prints out the annotations
// let's print the sentence-level annotations
int sentenceCount = 0;
for (Sentence sentence: doc.sentences()) {
System.out.println("Sentence #" + sentenceCount + ":");
System.out.println("Tokens: " + mkString(sentence.words(), " "));
System.out.println("Start character offsets: " + mkString(sentence.startOffsets(), " "));
System.out.println("End character offsets: " + mkString(sentence.endOffsets(), " "));
// these annotations are optional, so they are stored using Option objects, hence the isDefined checks
if(sentence.lemmas().isDefined()){
System.out.println("Lemmas: " + mkString(sentence.lemmas().get(), " "));
}
if(sentence.tags().isDefined()){
System.out.println("POS tags: " + mkString(sentence.tags().get(), " "));
}
if(sentence.chunks().isDefined()){
System.out.println("Chunks: " + mkString(sentence.chunks().get(), " "));
}
if(sentence.entities().isDefined()){
System.out.println("Named entities: " + mkString(sentence.entities().get(), " "));
}
if(sentence.norms().isDefined()){
System.out.println("Normalized entities: " + mkString(sentence.norms().get(), " "));
}
if(sentence.dependencies().isDefined()) {
System.out.println("Syntactic dependencies:");
DirectedGraphEdgeIterator<String> iterator = new
DirectedGraphEdgeIterator<String>(sentence.dependencies().get());
while(iterator.hasNext()) {
scala.Tuple3<Object, Object, String> dep = iterator.next();
// note that we use offsets starting at 0 (unlike CoreNLP, which uses offsets starting at 1)
System.out.println(" head:" + dep._1() + " modifier:" + dep._2() + " label:" + dep._3());
}
}
if(sentence.syntacticTree().isDefined()) {
System.out.println("Constituent tree: " + sentence.syntacticTree().get());
// see the org.clulab.struct.Tree class for more information
// on syntactic trees, including access to head phrases/words
}
sentenceCount += 1;
System.out.println("\n");
}
// let's print the coreference chains
if(doc.coreferenceChains().isDefined()) {
// these are scala.collection Iterator and Iterable (not Java!)
scala.collection.Iterator<scala.collection.Iterable<CorefMention>> chains = doc.coreferenceChains().get().getChains().iterator();
while(chains.hasNext()) {
scala.collection.Iterator<CorefMention> chain = chains.next().iterator();
System.out.println("Found one coreference chain containing the following mentions:");
while(chain.hasNext()) {
CorefMention mention = chain.next();
// note that all these offsets start at 0 too
System.out.println("\tsentenceIndex:" + mention.sentenceIndex() +
" headIndex:" + mention.headIndex() +
" startTokenOffset:" + mention.startOffset() +
" endTokenOffset:" + mention.endOffset());
}
}
}
// let's print the discourse tree
if(doc.discourseTree().isDefined()) {
DiscourseTree tree = doc.discourseTree().get();
System.out.println("Discourse tree:\n" + tree);
}
}
public static String mkString(String [] sa, String sep) {
StringBuilder os = new StringBuilder();
for(int i = 0; i < sa.length; i ++) {
if(i > 0) os.append(sep);
os.append(sa[i]);
}
return os.toString();
}
public static String mkString(int [] sa, String sep) {
StringBuilder os = new StringBuilder();
for(int i = 0; i < sa.length; i ++) {
if(i > 0) os.append(sep);
os.append(Integer.toString(sa[i]));
}
return os.toString();
}
}The output of this code is:
Sentence #0:
Tokens: John Smith went to China .
Start character offsets: 0 5 11 16 19 24
End character offsets: 4 10 15 18 24 25
Lemmas: John Smith go to China .
POS tags: NNP NNP VBD TO NNP .
Chunks: B-NP I-NP B-VP B-PP B-NP O
Named entities: PERSON PERSON O O LOCATION O
Normalized entities: O O O O O O
Syntactic dependencies:
head:1 modifier:0 label:nn
head:2 modifier:1 label:nsubj
head:2 modifier:4 label:prep_to
Constituent tree:
(ROOT
(S
(NP
(NNP John)
(NNP Smith)
)
(VP
(VBD went)
(PP
(TO to)
(NP
(NNP China)
)
)
)
(. .)
)
)
Sentence #1:
Tokens: He visited Beijing , on January 10th , 2013 .
Start character offsets: 26 29 37 44 46 49 57 61 63 67
End character offsets: 28 36 44 45 48 56 61 62 67 68
Lemmas: he visit Beijing , on January 10th , 2013 .
POS tags: PRP VBD NNP , IN NNP JJ , CD .
Chunks: B-NP B-VP B-NP O B-PP B-NP I-NP I-NP I-NP O
Named entities: O O LOCATION O O DATE DATE DATE DATE O
Normalized entities: O O O O O 2013-01-10 2013-01-10 2013-01-10 2013-01-10 O
Syntactic dependencies:
head:1 modifier:0 label:nsubj
head:1 modifier:2 label:dobj
head:1 modifier:8 label:tmod
head:2 modifier:5 label:prep_on
head:5 modifier:6 label:amod
Constituent tree:
(ROOT
(S
(NP
(PRP He)
)
(VP
(VBD visited)
(NP
(NP
(NNP Beijing)
)
(, ,)
(PP
(IN on)
(NP
(NNP January)
(JJ 10th)
)
)
(, ,)
)
(NP-TMP
(CD 2013)
)
)
(. .)
)
)
Found one coreference chain containing the following mentions:
sentenceIndex:1 headIndex:2 startTokenOffset:2 endTokenOffset:7
Found one coreference chain containing the following mentions:
sentenceIndex:0 headIndex:1 startTokenOffset:0 endTokenOffset:2
sentenceIndex:1 headIndex:0 startTokenOffset:0 endTokenOffset:1
Found one coreference chain containing the following mentions:
sentenceIndex:1 headIndex:5 startTokenOffset:5 endTokenOffset:7
Found one coreference chain containing the following mentions:
sentenceIndex:0 headIndex:4 startTokenOffset:4 endTokenOffset:5
Found one coreference chain containing the following mentions:
sentenceIndex:1 headIndex:8 startTokenOffset:8 endTokenOffset:9
Discourse tree:
elaboration (LeftToRight)
TEXT:John Smith went to China .
TEXT:He visited Beijing , on January 10th , 2013 .
The discourse parser in processors is inspired by the parser of Feng and Hirst and the HILDA parser of Hernault et al., but with a different feature set.
It is transparently integrated in both CoreNLPProcessor and FastNLPProcessor: just instantiate it as CoreNLPProcessor(withDiscourse = true) or FastNLPProcessor(withDiscourse = true). If discourse is enabled, Document.discourseTree stores the discourse tree for the entire document as an instance of the DiscourseTree class.
Following the conventions from other modern discourse parsing work, the discourse tree:
- Is represented as a binary tree, containing hypotactic relations (containing one nucleus and one satellite node) or paratactic relations (both nodes have equal importance).
- Stores the relation labels in the parent node (in
DiscourseTree.relationLabel) rather than the satellite nodes (like the RST corpus). We use the same 18 labels as Feng and Hirst. - Stores the relation direction in
DiscourseTree.relationDirection. The direction can beLeftToRight(meaning the nucleus is the left child),RightToLeft(the right node is the nucleus), orNone(for paratactic relations).
Developers only: For more details on the discourse parsers, please see this Wiki page.
processors now contains a machine learning (ML) package (org.clulab.learning), which includes implementations for common ML algorithms (e.g., Perceptron, Logistic Regression, Support Vector Machines, Random Forests) for both classification and ranking.
The structure of this package is heavily inspired by Stanford's CoreNLP. Similar to CoreNLP, we use a Datum trait to store a single data point, which is implemented by BVFDatum to store binary-valued-feature datums, or by RVFDatum to store real-valued-feature datums. A collection of data points is stored as a Dataset, which is similarly implemented by BVFDataset or RVFDataset. All classifiers implement the Classifier trait, which has three main methods: train, which trains a model a given dataset, classOf, which returns the most likely prediction for a given datum, and scoresOf, which returns the scores for all known labels for a given datum. We currently support the following classifiers: large-margin Perceptron (PerceptronClassifier), linear SVMs and logistic regression from liblinear (LibLinearClassifier), dual SVMs from libsvm (LibSVMClassifier), and random forests, implemented in-house (RFClassifier).
A similar structure exists for ranking problems, with RankingDataset used to store a corpus of ranking examples, and RankingClassifier as the API to be implemented by all ranking classifiers. We currently support the following classifiers: ranking Perceptron (PerceptronRankingClassifier), and ranking SVMs from svm-rank (SVMRankingClassifier).
For usage examples, including how to create datums and datasets from scratch or import them from the svm-light format, please take a look at the examples under src/test/scala/org/clulab/learning.
Please see Odin's Wiki page for details.
- Odin online demo
- See our Reach project for more applications of Odin and demos