CogComp's Natural Language Processing Libraries and Demos: Modules include lemmatizer, ner, pos, prep-srl, quantifier, question type, relation-extraction, similarity, temporal normalizer, tokenizer, transliteration, verb-sense, and more.
This module specifies a simple API for NLP components that compare objects – especially Strings – and return a score indicating how similar they are. It is used in our WordNet-, Named Entity-, embedding-, and paraphrase-based similarity code to simplify integration of different similarity resources.
When you first use the specific metrics, the system will automatically download corresponding resource files from CogComp server to user.home directory in your local machine.
Notice: Some resource file is very large and it may take a while to download. “paragram” is already included in the src/main/resources/.
See the default config in SimConfigurator in edu.illinois.cs.cogcomp.config package.
When you want to compare the similarity between two words, you can use word comparison metric below:
//initialization
ResourceManager rm_ = new SimConfigurator().getConfig(new ResourceManager(file));
WordSim ws = new WordSim(rm_, metric);
//ws.compare(word1,word2,metric);
double score=ws.compare("word", "sentence", metric);
And the metric can be chosen from “word2vec”, “paragram”, “esa”, “glove”, “wordnet”, “phrase2vec” or “customized”.
In config file, customized gives your option to use your own embedding file. Just put the location of the file at this field and the dimension of the embedding at the filed customized_embedding_dim.
When you want to compare Name Entities, you can use name entity comparison metric below:
NESim nesim=new NESim();
double score=nesim.compare("Donald Trump", "Trump");
You can also provide the types of one mention or both of the mentions. When specifying the type of only one mention, use null for the type of the other mention. For example,
NESim nesim = new NESim();
double score=nesim.compare("Donald Trump", "Trump", "PER", "PER");
double score2=nesim.compare("Obama", "chair", "PER", null);
When you want to compute similarity score between two sentences, you can use lexical level matching comparison:
String config = "config/configurations.properties";
Metric llm =new LLMStringSim(config);
String s1="please turn off the light";
String s1="please turn on the monitor";
double score=nesim.compare(s1,s2);
To use this metric properly, you need to specify some configurations in the Config file.
wordMetric is the word comparator metric used in LLM. It can be chosen from “word2vec”, “paragram”, “esa”, “glove”, “wordnet” “phrase2vec” or “customized” (your own embedding file).
usePhraseSim option will automatically split the sentence into phrase-based units when comparing sentences. To use this option, set it as true and use phrase2vec as wordMetric. The system splits the text into phrases, then matches those phrases using a phrase similarity metric that can match different formulations of many phrases, E.g. “please turn the light on” => “please turn-on the light”. Notice: When we split sentences into phrases, this phrases identification process depends on the generalized phrases dictionary we extracted from Wordnet (see src/main/resources/phrases.txt).
useNER option will run NER on sentence and compare name-entity using NE comparison metrics in LLM. To use this option, set it as true. The system will run NER on the sentences first and comparing name entity and words separately. Notice: the NER initialization takes a lot of memory. See NER detail here.
To get the basic LLM similarity score, just set usePhraseSim and useNER as false in config file (which is also the default setting).
Do, Quang, et al. “Robust, light-weight approaches to compute lexical similarity.” Computer Science Research and Technical Reports, University of Illinois (2009): 94.
@article{do2009robust,
title={Robust, light-weight approaches to compute lexical similarity},
author={Do, Quang and Roth, Dan and Sammons, Mark and Tu, Yuancheng and Vydiswaran, V}
}