Changes between Initial Version and Version 1 of en/NlpInPracticeCourse/2021/InformationExtraction

Timestamp:

Aug 30, 2022, 10:39:15 AM (20 months ago)

Author:

Ales Horak

Comment:

copied from private/NlpInPracticeCourse/InformationExtraction

en/NlpInPracticeCourse/2021/InformationExtraction

@@ +1 @@
+= Extracting structured information from text =
+
+[[https://is.muni.cz/auth/predmet/fi/ia161|IA161]] [[en/NlpInPracticeCourse|NLP in Practice Course]], Course Guarantee: Aleš Horák
+
+Prepared by: Zuzana Nevěřilová
+
+
+== State of the Art ==
+
+Information extraction (IE) is a technology based on
+analyzing natural language in order to extract snippets
+of information. The process takes texts (and sometimes
+speech) as input and produces fixed-format, unambiguous
+data as output. This data may be used directly for
+display to users, or may be stored in a database or
+spreadsheet for later analysis, or may be used for
+indexing purposes in information retrieval (IR) applications
+such as Internet search engines like Google.
+
+=== References ===
+
+ 1. Cunningham, Hamish. [https://gate.ac.uk/sale/ell2/ie/ An Introduction to Information Extraction]. Encyclopedia of Language and Linguistics, 2nd Edition. Elsevier, 2005.
+ 1. Piskorski, J. and Yangarber, R. Information Extraction: Past, Present and Future, pages 23–49. Springer Berlin Heidelberg, Berlin, Heidelberg, 2013.
+ 1. Aydar, Mehmet, Ozge Bozal, and Furkan Ozbay. [https://arxiv.org/abs/2007.04247 Neural relation extraction: a survey.] arXiv e-prints (2020): arXiv-2007. 
+
+== Practical Session ==
+
+We will extract information from news articles using GATE.
+
+ 1. Create {{{<YOUR_FILE>}}}, a text file named {{{ia161-UCO-08.txt}}} where '''UCO''' is your university ID. 
+ 1. Download and install GATE (Java 8 is necessary) from https://gate.ac.uk/download/. Either run the MS installer or the Java installer, install and run as app or in the command line:
+ {{{
+java -jar gate-<VERSION>-installer.jar
+}}}
+ 1. Run GATE
+ {{{
+GATE_Developer_<VERSION>/bin/gate.sh
+}}}
+ 1. Load ANNIE (with defaults), read about its components [[br]]
+ [[Image(annie.png)]]
+ 1. Create document(s):
+   * right click on `Language Resources/New/GATE Document` in the left menu
+   * change {{{markupAware}}} to {{{false}}}
+   * change {{{sourceUrl}}} to {{{stringContent}}} and paste some news text
+   * repeat these steps
+   * you can find three sample texts here: [raw-attachment:text1.txt text1.txt], [raw-attachment:text2.txt text2.txt], [raw-attachment:text3.txt text3.txt]
+ 1. Create corpus:
+   * right click on `Language Resources/New/GATE Corpus` in the left menu
+   * drag and drop the document in order to put them into the corpus
+ 1. Run ANNIE: Click on `Applications/Annie` in the left menu, select `Corpus`
+ 1. Observe the annotated results, click on a document, then `Annotation Sets` and/or `Annotation List`.
+
+So far, GATE did not much more than Stanford NER. Note, however, that all tokens are annotated and POS-tagged. Also note the annotation type Lookup.
+
+We add rules for extracting ''job titles'' and the respective ''person names''. The rules are defined in the grammars [raw-attachment:jobtitle.jape] and [raw-attachment:jobtitleperson.jape]
+
+ 1. Right click `Processing Resources/New/JAPE Transducer` in the left menu
+ 1. Download the grammar(s).
+ 1. Click on {{{grammmarUrl}}} and choose the grammar file {{{jobtitle.jape}}}
+ 1. Click on `Applications/Annie` in the left menu and add the JAPE Transducer to the ANNIE pipeline (Selected Processing Resources)
+ 1. Run ANNIE again: Click on `Applications/Annie` in the left menu
+ 1. Observe the annotated results, click on a document, then `Annotation Sets` and/or `Annotation List`. If applicable, you can see new annotation `JobTitle`.
+ 1. Observe the grammars {{{jobtitle.jape}}} and {{{jobtitleperson.jape}}}
+ 1. Add new transducer with the grammar {{{jobtitleperson.jape}}} and observe the results.
+ 1. Optionally, you can add further documents and observe how universal the {{{jobtitleperson.jape}}} grammar is.
+ 1. According to the above grammars, write your own that extracts new relations (e.g. job title in company or person works in company).
+
+Write your observations to {{{<YOUR_FILE>}}}: Particularly, comment how well the  Gazetteer and NE Transducer perform, describe how well the grammar works. Note that no coreference resolution is used (optionally, you can try one).
+Copy your grammar from the last point to {{{<YOUR_FILE>}}}.