Review: The Development of Language Processing Strategies: A Cross-linguistic Study Between Japanese and English

The Development of Language Processing Strategies: A Cross-linguistic Study Between Japanese and English by Reiko Mazuka
My rating: 4 of 5 stars

This is basically an updated version of Mazuka's PHD thesis. This book is a significant work on human sentence processing involving data from a head final and a head initial language.
Mazuka presents data on sentence processing experiments with English speaking adults and Japanese speaking children and adults. She shows that sentence processing strategies are the same in children and adults (though their ability differs with age), and that sentence processing strategies differ cross-linguistically. Her experimental data include probe latency tasks (PLTs) for lexical and semantic information in English and Japanese sentences.
A probe latency task involves a subject listening to a sentence and responding to questions about its contents. In lexical PLT, a subject is asked if the sentence contained the specified lexical item. In semantic PLT, the subject is asked if a sentence contained a portion which has a similar meaning to a specified word or phrase. The experimenter then carefully designs sentences which test the subjects' ability to process different types of sentences. Mazuka's experiment measure response time and also the accuracy of the subjects' responses. Her findings for cross-linguistic processing differences are as follows:
English speakers showed processing differences for main and subordinate clauses, while Japanese speakers did not.
English speakers showed different effects for semantic and lexical tasks, while Japanese speakers did not.
In English speakers, response time for semantic probe latency tasks involving sentence-initial subordinate clauses (an LB structure) was increased, while in Japanese speakers it was greatly decreased.
Japanese speaker response times to both lexical and semantic PLTs involving left-branching and coordinate structures were the same; English speakers showed much larger recency effects in LB than coordinate sentences.
Hypotheses about the human language processing mechanism which assume a single processing strategy do not account for these data. Japanese speakers process LB structures efficiently, and English speakers process RB structures efficiently. This is impossible in a parser which assumes only one processing strategy, and a parser which can efficiently process both would be too powerful to account for real human data. For Japanese speakers to process LB structures as efficiently as English speakers do RB structures, processing must be done bottom-up instead of top-down. Mazuka therefore hypothesizes that Universal Grammar (UG) contains a parameter which determines whether a language is right- or left-branching (RB or LB), and that this is linked with the processing strategy by specifying whether processing should be done top-down or bottom-up. She also hypothesizes that in English, main and subordinate clauses are processed to a different semantic level at some initial encoding stage, accounting differences in English main and subordinate clause PLT tasks. This needs to be further tested in the future with PLTs involving two clause sentences beginning with an explicit subordinator in Japanese.
She states that future research is required to determine the exact relationship between her experimental data and the operation of the human sentence parser as she has hypothesized.
Since some languages such as German, actually branch in different directions for different types of clauses, her hypothesis needs to be revised to account for this. I'm hoping that her hypotheses can be tested in detail in some sort of a cogntive modeling system.

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List of Japanese NLP tools

I haven't tried out all of these so I don't have comments for everything, but hopefully this list will come in useful for someone.

Morphological analyzers/tokenizers

Itadaki: a Japanese processing module for OpenOffice. I've done a tiny bit of work and issue documentation on a fork here, and someone forked that to work with a Japanese/German dictionary here.

GoSen: Uses sen as a base, and is part of Itadaki; a pure Java version of ChaSen. See my previous post on where to download it from.

MeCab: This page also contains a comparison of MeCab, ChaSen, JUMAN, and Kakasi.

ChaSen

JUMAN

Cabocha: Uses support vector machines for morphological and dependency structure analysis.

Gomoku

Igo

Kuromoji: Donated to Apache and used in Solr. Looks nice.

Corpora

Hypermedia Corpus

TüBa-J/S: Japanese treebank from universityu of Tübingen. Not as heavily annotated as I'd hoped. You have to send them an agreement to download it, but it's free.

GSK: Not free, but very cheap.

LDC: Expensive unless your institution is a member

Other lexical resources

Kakasi: Gives readings for kanji compounds.

WordNet: Stil under development by NiCT. The sense numbers are cross-indexed with those in the English WordNet, so it could be useful for translation. Also, there are no verb frames like there are in English.

LCS Database: From Okayama University

Framenet: Unfortunately you can only do online browsing.

Chakoshi: Online collocation search engine.

Itadaki GoSen and IPADIC 2.7

Update3: I've forked the Itadaki project on GitHub to keep track of it better.
Update2: I made an executable JAR for GoSen that runs the ReadingProcessorDemo. It requires Java 6; just unzip the contents of this zip file to your computer and click on the jar file.
Update1: The IPADIC dictionary is no longer available from its original location. It has been replaced by the NAIST dictionary. I have edited the following post to reflect the needed changes.

Itadaki is a software suite for processing Japanese in OpenOffice. GoSen, part of the Itadaki project, is a pure Java morphological analysis tool for Japanese, and I have found it extremely useful in my research. Unfortunately, the page for this project went down recently, making the tools harder to find. Itadaki is still available through Google code here, but I can't find a separate installment of GoSen. The old GoSen website can still be accessed through the way-back-machine here. The other problem is that GoSen hasn't been updated since 2007, and in it's current release cannot handle the latest release of IPADIC. I'll describe how to fix it in this post.
Why does it matter that we can't use the latest version of IPADIC? Well, here's an example. I am using GoSen in my thesis work right now, and I put in a sentence which included a negative, past tense verb, such as 行かなかった. It analyzed it as な being used for negation, and かった being the past tense of the verb かう. That is indeed a problem! Using the newer IPADIC fixed it for me, though. To do that, download this modified version of GoSen. The explanation for the fix is here. Basically, a change in the new IPADIC versions to work better with MeCab adds a bunch of commas that break GoSen.


Edit: Once you've downloaded and unzipped GoSen, run ant in the top directory to build an executable JAR file. Note that if you want javadoc, you'll have to change build.xml so that the javadoc command has 'encoding="utf-8"'. Next, you must download the IPADIC dictionary from its legacy repository, here. Unpack the contents into testdata/dictionary. Change testdata/dictionary/build.xml so that the value of "ipadic.version" is "2.7.0" (the version that you downloaded). Now run ant in this directory to build the dictionary. [If you had errors, you may have forgotten to run ant in the top level directory first.]


Then, to run a demo and see what amazing things GoSen can do, copy the dictionary.xml file from the testdata/dictionary directory to the dictionary/dictionary directory, go back to the root directory of GoSen, and then run java -cp bin examples.ReadingProcessorDemo testData/dictionary/dictionary.xml. The GoSen site says to run using the testdata folder, but that means you'll have to download the dictionary twice, which is dumb. When you run the above command, you'll get this GUI:

Notice that it tokenizes the sentence, gives readings, and allows you to choose among alternatives analyses. It also gives information on part of speech and inflection.
To use GoSen in an Eclipse project, add gosen-1.0beta.jar to the project build path. You also need to have the dictionary directory somewhere, along with the dictionary.xml file. This code will get you started:

package edu.byu.xnlsoar.jp.lexacc;

import java.io.IOException;
import java.util.List;

import edu.byu.xnlsoar.utils.Constants;



import net.java.sen.SenFactory;
import net.java.sen.StringTagger;
import net.java.sen.dictionary.Morpheme;
import net.java.sen.dictionary.Token;

public class GoSenInterface {
 public List tokenize(String sentence){
  StringTagger tagger = SenFactory.getStringTagger(Constants.getProperty("GOSEN_DICT_CONFIG"));
  try {
   return tagger.analyze(sentence);
  } catch (IOException e) {
   e.printStackTrace();
   System.exit(-1);
  }
  return null;
 }
 public static void main(String[] args){
  String sentence = "やっぱり日本語情報処理って簡単に出来ちゃうんだもんな。";
  GoSenInterface dict = new GoSenInterface();
  System.out.println("tokenizing " + sentence);
  List tokens = dict.tokenize(sentence);
  System.out.println(tokens);
  Morpheme m;
  System.out.println("surface, lemma, POS, conjugation");
  for(Token t : tokens){
   System.out.print(t + ", ");
   m = t.getMorpheme();
   System.out.print(m.getBasicForm() + ", ");
   System.out.print(m.getPartOfSpeech() + ", ");
   System.out.println(m.getConjugationalType());
  }
 }
}

If you run that you will get:
tokenizing やっぱり日本語情報処理って簡単に出来ちゃうんだもんな。
[やっぱり, 日本語, 情報処理, って, 簡単, に, 出来, ちゃう, ん, だ, もん, な, 。]
surface, lemma, POS, conjugation
やっぱり, やっぱり, 副詞-一般, *
日本語, 日本語, 名詞-一般, *
情報処理, 情報処理, 名詞-一般, *
って, って, 助詞-格助詞-連語, *
簡単, 簡単, 名詞-形容動詞語幹, *
に, に, 助詞-副詞化, *
出来, 出来る, 動詞-自立, 一段
ちゃう, ちゃう, 動詞-非自立, 五段・ワ行促音便
ん, ん, 名詞-非自立-一般, *
だ, だ, 助動詞, 特殊・ダ
もん, もん, 名詞-非自立-一般, *
な, だ, 助動詞, 特殊・ダ
。, 。, 記号-句点, *


You have plenty of other options while processing, like grabbing alternate readings, etc. Notice that it got one wrong here: ちゃう is a contraction of てしまう, not a verb whose lemma is ちゃう. It doesn't seem to work on contractions because every token needs a surface form. So this might not work well on informal registers such as tweets or blogs unless some pre-preprocessing is done.
Feel free to leave any questions or comments.