How to extract relationship from text in NLTK

Hi I'm trying to extract relationships from a string of text based on the second last example here: https://web.archive.org/web/20120907184244/http://nltk.googlecode.com/svn/trunk/doc/howto/relextract.html

From a string such as "Michael James editor of Publishers Weekly" my desired result is to have an output such as:

[PER: 'Michael James'] ', editor of' [ORG: 'Publishers Weekly']

What is the best way to do do this? What format does extract_rels expect and how do I format my input to meet that requirement?

---

Tried to do it myself but it didn't work. Here is the code I've adapted from the book. I'm not getting any results printed. What am I doing wrong?

class doc():
 pass

doc.headline = ['this is expected by nltk.sem.extract_rels but not used in this script']

def findrelations(text):
roles = """
(.*(                   
analyst|
editor|
librarian).*)|
researcher|
spokes(wo)?man|
writer|
,sofsthe?s*  # "X, of (the) Y"
"""
ROLES = re.compile(roles, re.VERBOSE)
tokenizedsentences = nltk.sent_tokenize(text)
for sentence in tokenizedsentences:
    taggedwords  = nltk.pos_tag(nltk.word_tokenize(sentence))
    doc.text = nltk.batch_ne_chunk(taggedwords)
    print doc.text
    for rel in relextract.extract_rels('PER', 'ORG', doc, corpus='ieer', pattern=ROLES):
        print relextract.show_raw_rtuple(rel) # doctest: +ELLIPSIS

text ="Michael James editor of Publishers Weekly"

findrelations(text)

---

here a code based on yours (just few adjusts) that work well ;)

import nltk
import re 
from nltk.chunk import ne_chunk_sents
from nltk.sem import relextract


def findrelations(text):
    roles = """
    (.*(                   
    analyst|
    editor|
    librarian).*)|
    researcher|
    spokes(wo)?man|
    writer|
    ,sofsthe?s*  # "X, of (the) Y"
    """
    ROLES = re.compile(roles, re.VERBOSE)

    sentences = nltk.sent_tokenize(text)
    tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
    tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
    chunked_sentences = nltk.ne_chunk_sents(tagged_sentences)


    for doc in chunked_sentences:
        print doc
        for rel in relextract.extract_rels('PER', 'ORG', doc, corpus='ace', pattern=ROLES):
            #it is a tree, so you need to work on it to output what you want
            print relextract.show_raw_rtuple(rel) 

findrelations('Michael James editor of Publishers Weekly')

(S (PERSON Michael/NNP) (PERSON James/NNP) editor/NN of/IN (ORGANIZATION Publishers/NNS Weekly/NNP))

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