WordNet

COS 226 Programming Assignment

WordNet

WordNet is a semantic lexicon for the English language that is used extensively by computational linguists and cognitive scientists; for example, it was a key component in IBM's Watson. WordNet groups words into sets of synonyms called synsets and describes semantic relationships between them. One such relationship is the is-a relationship, which connects a hyponym (more specific synset) to a hypernym (more general synset). For example, a plant organ is a hypernym of carrot and plant organ is a hypernym of plant root.

The WordNet DAG. Your first task is to build the wordnet digraph: each vertex v is an integer that represents a synset, and each directed edge v→w represents that w is a hypernym of v. The graph is directed and acyclic (a DAG), though not necessarily a tree since a synset can have several hypernyms. A small subgraph of the wordnet digraph is illustrated below.

We now describe the two data files that you will use to create the wordnet digraph. The files are in CSV format: each line contains a sequence of fields, separated by commas.

List of noun synsets. The file synsets.txt lists all the (noun) synsets in WordNet. The first field is the synset id (an integer), the second field is the synonym set (or synset), and the third field is its dictionary definition (or gloss). For example, the line
```
45,AND_circuit AND_gate,a circuit in a computer that fires only when all of its inputs fire
```
means that the synset { AND_circuit, AND_gate } has an id number of 45 and it's gloss is a circuit in a computer that fires only when all of its inputs fire. The individual nouns that comprise a synset are separated by spaces (and a synset element is not permitted to contain a space).

List of hypernyms. The file hypernyms.txt contains the hypernym relationships: The first field is a synset id; subsequent fields are the id numbers of the synset's hypernyms. For example, the following line
```
171,22798,57458
```
means that the the synset 171 ("Actifed") has two hypernyms: 22798 ("antihistamine") and 57458 ("nasal_decongestant"), representing that Actifed is both an antihistamine and a nasal decongestant. The synsets are obtained from the corresponding lines in the file synsets.txt.
```
171,Actifed,trade name for a drug containing an antihistamine and a decongestant...
22798,antihistamine,a medicine used to treat allergies...
57458,nasal_decongestant,a decongestant that provides temporary relief of nasal...
```

Implement an immutable data type WordNet with the following API:

// constructor takes the name of the two input files
public WordNet(String synsets, String hypernyms)

// an iterable containing all of the glosses associated with the noun's synsets
public Iterable<String> glosses(String noun)

// is the word a WordNet noun?
public boolean isNoun(String word)

// distance between nounA and nounB (defined below); -1 if infinite
public int distance(String nounA, String nounB)

// a synset (second field of synsets.txt) that is the common ancestor of nounA and nounB
// in a shortest ancestral path (defined below); null if no such path
public String sap(String nounA, String nounB)

Your data type should use space linear in the input size (size of synsets and hypernyms files). The method isNoun() should run in time logarithmic (or better) in the number of nouns. The method glosses() should run in time logarithmic (or better) in the number of synsets, per gloss returned. The methods distance() and sap() should run in time linear in the size of the wordnet digraph.

Shortest ancestral path. An ancestral path between two vertices v and w in a digraph is a directed path from v to a common ancestor x, together with a directed path from w to the same ancestor x. A shortest ancestral path is an ancestral path of minimum total length. For example, in the digraph at left, the shortest ancestral path between 3 and 11 has length 4 (with common ancestor 1). In the digraph at right, one ancestral path between 1 and 5 has length 4 (with common ancestor 5), but the shortest ancestral path has length 2 (with common ancestor 0).

Implement an immutable data type SAP with the following API:

// constructor takes a digraph (not necessarily a DAG)
public SAP(Digraph G)

// length of shortest ancestral path between v and w; -1 if no such path
public int length(int v, int w)

// a common ancestor of v and w that participates in a shortest ancestral path; -1 if no such path
public int ancestor(int v, int w)

// length of shortest ancestral path between any vertex in v and any vertex in w; -1 if no such path
public int length(Iterable<Integer> v, Iterable<Integer> w)

// a common ancestor that participates in shortest ancestral path; -1 if no such path
public int ancestor(Iterable<Integer> v, Iterable<Integer> w)

You data type should use space proportional to E + V. All methods (and the constructor) should take time proportional to E + V in the worst case, where E and V are the number of edges and vertices in the digraph, respectively.

Test client. The following test client takes the name of a digraph input file as as a command-line argument, constructs the digraph, reads in vertex pairs from standard input, and prints out the length of the shortest ancestral path between the two vertices and a common ancestor that participates in that path:

public static void main(String[] args) {
    In in = new In(args[0]);
    Digraph G = new Digraph(in);
    SAP sap = new SAP(G);
    while (!StdIn.isEmpty()) {
        int v = StdIn.readInt();
        int w = StdIn.readInt();
        int length   = sap.length(v, w);
        int ancestor = sap.ancestor(v, w);
        StdOut.printf("length = %d, ancestor = %d\n", length, ancestor);
    }
}

Here is a sample execution:

% more digraph1.txt             % java SAP digraph1.txt
13                              3 11
11                              length = 4, ancestor = 1
 7  3                            
 8  3                           9 12
 3  1                           length = 3, ancestor = 5
 4  1
 5  1                           7 2
 9  5                           length = 4, ancestor = 0
10  5
11 10                           1 6
12 10                           length = -1, ancestor = -1
 1  0
 2  0

Measuring the semantic relatedness of two nouns. Semantic relatedness refers to the degree to which two concepts are related. Measuring semantic relatedness is a challenging problem. For example, most of us agree that George Bush and John Kennedy (two U.S. presidents) are more related than are George Bush and chimpanzee (two primates). However, not most of us agree that George Bush and Eric Arthur Blair are related concepts. But if one is aware that George Bush and Eric Arthur Blair (aka George Orwell) are both communicators, then it becomes clear that the two concepts might be related.

We estimate the semantic relatedness distance(A, B) of two nouns A and B as follows:

If either A or B is not a wordnet noun, then the distance is infinity.
Otherwise, the distance is the minimum length of any ancestral path between any synset v of A and any synset w of B.

This is the notion of distance that you will use to implement the distance() and sap() methods in the WordNet data type.

Outcast detection. Given a list of nouns A₁, A₂, ..., A_n, which noun is the least related to the others? To identify an outcast, compute the sum of the distances between each noun and every other one:

d_i = dist(A_i, A₁) + dist(A_i, A₂) + ... + dist(A_i, A_n)

and return a noun A_t for which d_t is maximum.

Implement an immutable data type Outcast with the following API:

// constructor takes a WordNet object
public Outcast(WordNet wordnet)

// given an array of WordNet nouns, return an outcast
public String outcast(String[] nouns)

The following test client takes from the command line the name of a synset file, the name of a hypernym file, followed by the names of outcast files, and prints out an outcast in each file:

public static void main(String[] args) {
    WordNet wordnet = new WordNet(args[0], args[1]);
    Outcast outcast = new Outcast(wordnet);
    for (int t = 2; t < args.length; t++) {
        String[] nouns = In.readStrings(args[t]);
        StdOut.println(args[t] + ": " + outcast.outcast(nouns));
    }
}

Here is a sample execution:

% more outcast1.txt
horse zebra cat bear table

% more outcast2.txt
apple pear peach banana lime lemon blueberry strawberry mango watermelon potato

% more outcast3.txt
water soda bed orange_juice milk apple_juice tea coffee

% java Outcast synsets.txt hypernyms.txt outcast1.txt outcast2.txt outcast3.txt
outcast1.txt: table
outcast2.txt: potato
outcast3.txt: bed

Deliverables. Submit WordNet.java, SAP.java, and Outcast.java that implement the APIs described above, along with a readme.txt file. Also submit any other supporting files (excluding those in stdlib.jar and algs4.jar).

This assignment was created by Alina Ene and Kevin Wayne.