Find all references to a supplied noun in StanfordNLP

Question

I'm trying to parse some text to find all references to a particular item. So, for example, if my item was The Bridge on the River Kwai and I passed it this text, I'd like it to find all the instances I've put in bold.

The Bridge on the River Kwai is a 1957 British-American epic war film directed by David Lean and starring William Holden, Jack Hawkins, Alec Guinness, and Sessue Hayakawa. The film is a work of fiction, but borrows the construction of the Burma Railway in 1942–1943 for its historical setting. The movie was filmed in Ceylon (now Sri Lanka). The bridge in the film was near Kitulgala.

So far my attempt has been to go through all the mentions attached to each CorefChain and loop through those hunting for my target string. If I find the target string, I add the whole CorefChain, as I think this means the other items in that CorefChain also refer to the same thing.

List<CorefChain> gotRefs = new ArrayList<CorefChain>();
String pQuery = "The Bridge on the River Kwai";

for (CorefChain cc : document.get(CorefCoreAnnotations.CorefChainAnnotation.class).values()) {
    List<CorefChain.CorefMention> corefMentions = cc.getMentionsInTextualOrder();
    boolean addedChain = false;
    for (CorefChain.CorefMention cm : corefMentions) {
        if ((!addedChain) && 
                (pQuery.equals(cm.mentionSpan))) {
            gotRefs.add(cc);
            addedChain = true;
        }
    }
}

I then loop through this second list of CorefChains, re-retrieve the mentions for each chain and step through them. In that loop I show which sentences have a likely mention of my item in a sentence.

for (CorefChain gr : gotRefs) {
    List<CorefChain.CorefMention> corefMentionsUsing = gr.getMentionsInTextualOrder();
    for (CorefChain.CorefMention cm : corefMentionsUsing) {
        //System.out.println("Got reference to " + cm.mentionSpan + " in sentence #" + cm.sentNum);
    }
}

It finds some of my references, but not that many, and it produces a lot of false positives. As might be entirely apparently from reading this, I don't really know the first thing about NLP - am I going about this entirely the wrong way? Is there a StanfordNLP parser that will already do some of what I'm after? Should I be training a model in some way?

Answer 1

I think a problem with your example is that you are looking for references to a movie title, and there isn't support in Stanford CoreNLP for recognizing movie titles, book titles, etc...

If you look at this example:

"Joe bought a laptop. He is happy with it."

You will notice that it connects:

"Joe" -> "He"

and

"a laptop" -> "it"

Coreference is an active research area and even the best system can only really be expected to produce an F1 of around 60.0 on general text, meaning it will often make errors.