readme update

README.md

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 An Open Information Extraction system, providing useful extractions:
 * represents contextual information with semantic annotations
-* identifies and removing words that are considered overly specific
+* identifies and removes words that are considered overly specific
 * high precision/recall 
 * shorter, semantically enriched extractions
 

README.md~

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+<img src="https://gkiril.github.io/minie/images/minie_logo.png" align="right" width="150" />
+
+# MinIE - Open Information Extraction system
+
+An Open Information Extraction system, providing useful extractions:
+* represents contextual information with semantic annotations
+* identifies and removing words that are considered overly specific
+* high precision/recall 
+* shorter, semantically enriched extractions
+
+## Open Information Extraction (OIE)
+Open Information Extraction (OIE) systems aim to extract unseen relations and their arguments from unstructured text in unsupervised manner. In its simplest form, given a natural language sentence, they extract information in the form of a triple, consisted of subject (S), relation (R) and object (O). 
+
+Suppose we have the following input sentence:
+```
+AMD, which is based in U.S., is a technology company.
+```
+
+An OIE system aims to make the following extractions: 
+
+```
+("AMD"; "is based in"; "U.S.")
+("AMD"; "is"; "technology company")
+```
+
+## Demo
+
+In general, the code for running MinIE in all of its modes is almost the same. The only exception is MinIE-D, which requires additional input (list of multi-word dictionaries). You can still use MinIE-D without providing multi-word dictionaries, but then MinIE-D assumes that you provided an empty dictionary, thus minimizing all the words which are candidates for dropping. 
+
+The following code demo is for MinIE-S (note that you can use the same for the rest of the modes, you just need to change `MinIE.Mode` accordingly):
+
+```java
+import de.uni_mannheim.minie.MinIE;
+import de.uni_mannheim.minie.annotation.AnnotatedProposition;
+import de.uni_mannheim.utils.coreNLP.CoreNLPUtils;
+
+import edu.stanford.nlp.pipeline.StanfordCoreNLP;
+
+public class Demo {
+    public static void main(String args[]) {
+        // Dependency parsing pipeline initialization
+        StanfordCoreNLP parser = CoreNLPUtils.StanfordDepNNParser();
+
+        // Input sentence
+        String sentence = "The Joker believes that the hero Batman was not actually born in 
+                           foggy Gotham City.";
+
+        // Generate the extractions (With SAFE mode)
+        MinIE minie = new MinIE(sentence, parser, MinIE.Mode.SAFE);
+
+        // Print the extractions
+        System.out.println("\nInput sentence: " + sentence);
+        System.out.println("=============================");
+        System.out.println("Extractions:");
+        for (AnnotatedProposition ap: minie.getPropositions()) {
+            System.out.println("\tTriple: " + ap.getTripleAsString());
+            System.out.print("\tFactuality: " + ap.getFactualityAsString());
+            if (ap.getAttribution().getAttributionPhrase() != null) 
+                System.out.print("\tAttribution: " + ap.getAttribution().toStringCompact());
+            else
+                System.out.print("\tAttribution: NONE");
+            System.out.println("\n\t----------");
+        }
+
+        System.out.println("\n\nDONE!");
+    }
+}
+```
+
+If you want to use MinIE-D, then the only difference would be the way MinIE is called:
+
+```java
+import de.uni_mannheim.utils.Dictionary;
+. . .
+
+// Initialize dictionaries
+String [] filenames = new String [] {"/minie-resources/wiki-freq-args-mw.txt", 
+                                     "/minie-resources/wiki-freq-rels-mw.txt"};
+Dictionary collocationsDict = new Dictionary(filenames);
+
+// Use MinIE
+MinIE minie = new MinIE(sentence, parser, MinIE.Mode.DICTIONARY, collocationsDict);
+
+```
+
+In `resources/minie-resources/` you can find multi-word dictionaries constructed from WordNet (wn-mwe.txt) and from wiktionary (wiktionary-mw-titles.txt). This will give you some sort of functionality for MinIE-D. The multi-word dictionaries constructed with MinIE-S (as explained in the paper) are not here because of their size. If you want to use them, please refer to the download link in the section "Resources".
+
+## Resources
+
+* **Documentation:** for more thorough documentation for the code, please visit [MinIE's project page](https://gkiril.github.io/minie/).
+* **Paper:** _"MinIE: Minimizing Facts in Open Information Extraction"_ - appeared on EMNLP 2017 [[pdf]](http://aclweb.org/anthology/D/D17/D17-1277.pdf)
+* **Dictionary:** Wikipedia: frequent relations and arguments [[zip]](http://dws.informatik.uni-mannheim.de/fileadmin/lehrstuehle/pi1/pi1/minie/wiki-freq-args-rels.zip)
+* **Experiments datasets:** datasets from the paper
+  * [NYT](http://dws.informatik.uni-mannheim.de/fileadmin/lehrstuehle/pi1/pi1/minie/NYT.zip)
+  * [Wiki](http://dws.informatik.uni-mannheim.de/fileadmin/lehrstuehle/pi1/pi1/minie/Wiki.zip)
+  * [NYT-10k](http://dws.informatik.uni-mannheim.de/fileadmin/lehrstuehle/pi1/pi1/minie/nyt10k.zip)
+
+## Citing
+If you use MinIE in your work, please cite our paper:
+
+```
+@inproceedings{gashteovski2017minie,
+  title={MinIE: Minimizing Facts in Open Information Extraction},
+  author={Gashteovski, Kiril and Gemulla, Rainer and Del Corro, Luciano},
+  booktitle={Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
+  pages={2620--2630},
+  year={2017}
+}
+```