This repository contains code to identify salient concepts in a text corpus. This code is part of the World Modeler's Ontology in a Day (OIAD) pipeline.
At a high level, this software uses the TextRank algorithm to rank noun phrases rather than sentences, as the original algorithm did. More specifically, the algorithm constructs a graph where concepts, i.e., noun phrases, are nodes, and edge weights indicate the similarity between the corresponding concepts. Then, the TextRank algorithm is used to generate PageRank scores for all nodes in the graph. The top nodes with the highest scores are returned by the algorithm.
The API follows the following steps.
First you need to prepare a sequence of input sentences, with each sentence associated with a goodness score that must be provided. This score is used for filtering out less important sentences at runtime:
val texts = Seq(
Seq(
("Food security is a measure of the availability of food and individuals' ability to access it.", 0.4),
))
Then convert texts to the World Modelers CDR document format:
val documents = for ((sentencesWithScores, i) <- texts.zipWithIndex) yield {
var end = 0
val scoredSentences = for ((sentence, sentenceScore) <- sentencesWithScores) yield {
val start = end
end = start + sentence.length
ScoredSentence(sentence, start, end, sentenceScore)
}
DiscoveryDocument(s"doc$i", scoredSentences)
}
We load the ConceptDiscoverer from the config file and apply it to the documents:
val conceptDiscovery = ConceptDiscoverer.fromConfig()
val concepts = conceptDiscovery.discoverConcepts(documents)
val rankedConcepts = conceptDiscovery.rankConcepts(concepts)
You can save the ranked concepts in json format:
val conceptSink = new ConceptSink(rankedConcepts)
Console.withOut(new PrintStream(new FileOutputStream("output_full.json"))){
conceptSink.printJson()
}
The JSON output format looks like this:
[ {
"concept" : {
"phrase" : "average production",
"locations" : [ {
"document_id" : "0df84c35985ba0130636ab8686943756",
"sentence_index" : 225
}, {
"document_id" : "0df84c35985ba0130636ab8686943756",
"sentence_index" : 244
}, ... ]
},
"saliency" : 0.07536057667010426
}, {
"concept" : {
"phrase" : "production",
"locations" : [ {
"document_id" : "0df84c35985ba0130636ab8686943756",
"sentence_index" : 225
}, {
"document_id" : "0df84c35985ba0130636ab8686943756",
"sentence_index" : 244
}, ... ]
},
"saliency" : 0.07435705153994535
}, {
"concept" : {
"phrase" : "women",
"locations" : [ {
"document_id" : "0289d3a06c7872344154991549c6f823",
"sentence_index" : 10
}, {
"document_id" : "0289d3a06c7872344154991549c6f823",
"sentence_index" : 11
}, ... ]
},
"saliency" : 0.07011902079604163
}, {
"concept" : {
"phrase" : "Somalia",
"locations" : [ {
"document_id" : "0bc9c72b3c259d67672e5c3163101828",
"sentence_index" : 5
}, {
"document_id" : "0194254586b5e82c3b24af36907b94d1",
"sentence_index" : 9
}, {
"document_id" : "0eb5eee25d3e3f652fd707a0a674a38b",
"sentence_index" : 11
}, ... ]
},
"saliency" : 0.06664052798844469
}, ... ]
We also add the functionanlity that allows user to load the existed concepts graph and get the ranking.
You can try the sample App:
sbt 'runMain org.clulab.concepts.apps.GraphRankingApp sample_graph.json'
The input format is as following:
{
"directed": true,
"nodes": [
{
"id": "0",
"text": "node1"
},
{
"id": "1",
"text": "node2"
},
{
"id": "2",
"text": "node3"
},
{
"id": "3",
"text": "node4"
}
],
"edges": [
{
"src": "0",
"dst": "1",
"weight": 1
},
{
"src": "0",
"dst": "2",
"weight": 1
},
{
"src": "1",
"dst": "2",
"weight": 1
},
{
"src": "2",
"dst": "3",
"weight": 1
}
]
}
The directed indicates if you want to use a directed graph or not. If you decide to use directed graph, the program will connect the edges from src to dst in a directed way. Else, the program will read the edges in an undirected way. If there are multiple edges between two nodes in the undirected setting, we add the two edge weights up as the new weight.
And the output is similar to the concept discovery output:
[ {
"concept" : {
"text" : "node3",
"id" : "2"
},
"saliency" : 0.3667096488104522
}, {
"concept" : {
"text" : "node1",
"id" : "0"
},
"saliency" : 0.2459349312644232
}, {
"concept" : {
"text" : "node2",
"id" : "1"
},
"saliency" : 0.2459349312644232
}, {
"concept" : {
"text" : "node4",
"id" : "3"
},
"saliency" : 0.14142048866070128
} ]
