KR 2019 homework part 1: overall task: erinevus redaktsioonide vahel
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Uus lehekülg: ' == Task == The task of the first lab is to build and demonstrate a system which can * tokenize the text (input and question) * run a [https://en.wikipedia.org/wiki/Named-entity...' |
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| (ei näidata sama kasutaja 10 vahepealset redaktsiooni) | |||
| 1. rida: | 1. rida: | ||
== Task == | == Task == | ||
The end result of the lab is a presentation: ca five minutes during which you explain: | |||
* which systems you experimented with | |||
* which gave useful output from your input | |||
* examples of output from your input (show actual textual examples) | |||
The task of the first lab is to build and demonstrate a system which can | The task of the first lab is to build and demonstrate a system which can | ||
* tokenize | * tokenize text (split any text into words: trivial) | ||
* run a [https://en.wikipedia.org/wiki/Named-entity_recognition NER tool] on the text giving wikipedia url-s as output. See [https://gate.d5.mpi-inf.mpg.de/webaida/ webaida]. Notice that | * run a [https://en.wikipedia.org/wiki/Named-entity_recognition NER tool] on the text giving wikipedia url-s as output. See [https://gate.d5.mpi-inf.mpg.de/webaida/ webaida]. Notice that: | ||
** some NER-s do not give wiki URL-s, but just types. This is also useful. | ** some NER-s do not give wiki URL-s, but just types. This is also useful. | ||
** for the second overall scenario (like wikidata) we do need wiki urls, though. | ** for the second overall scenario (like wikidata) we do need wiki urls, though. | ||
* run a parser tool on the text. There are several ways to parse, some more useful for our purposes than others. We will come back to that. | * run and experiment with a (recommendably) pre-trained word vectorization system like word2vec: as an example, [https://medium.com/@martinpella/how-to-use-pre-trained-word-embeddings-in-pytorch-71ca59249f76 see this tutorial]. We will come back to that. | ||
* run and experiment with parser tool on the text. There are several ways to parse, some more useful for our purposes than others. We will come back to that. | |||
You do not have to be wildly successful with all these subtasks to pass. You will, though, have to try out each of these subtasks and report on how it went (got ok results, got bad results, could not get it to work, ...) | |||
The second lab will build upon the results here. In particular, some choices in the second lab depend on what you managed to do with ner, word2vec and parsers. | |||
== Technology and tools == | == Technology and tools == | ||
| 14. rida: | 24. rida: | ||
You are free to use NLP tools and API-s and datasets, but your program should drive them from the beginning to end (ie your program takes input files, calls tools, modifies and prints output). It is better to use the tools than not to use them! | You are free to use NLP tools and API-s and datasets, but your program should drive them from the beginning to end (ie your program takes input files, calls tools, modifies and prints output). It is better to use the tools than not to use them! | ||
== Popular toolkits for NLP == | |||
The recommendation is to take the first (spacy) or the second (nltk) toolkit. | |||
* [https://spacy.io Spacy toolkit for Python] | |||
* [http://www.nltk.org/ NLTK: the main Python toolkit], see also [http://pixelmonkey.org/pub/nlp-training/ this tutorial] | |||
* [https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html Google SyntaxNet] (see [https://github.com/tensorflow/models/tree/master/syntaxnet in github]) | |||
* [http://nlp.stanford.edu/software/corenlp.shtml CoreNLP: the main Stanford NLP tool] in the context of a larger set of [http://nlp.stanford.edu/software/ Stanford NLP toolkits] like [http://nlp.stanford.edu/software/CRF-NER.shtml stanford NER etc] [http://pythonprogramming.net/named-entity-recognition-nltk-python/ and this NER tutorial] | |||
* [http://www.clips.ua.ac.be/pattern Pattern toolkit for Python] | |||
* [http://opennlp.apache.org/ opennlp] | |||
* [https://pypi.python.org/pypi/PyNLPl/ PyNLP for Python] | |||
* [http://williamjturkel.net/2013/06/30/named-entity-recognition-with-command-line-tools-in-linux/ NER tutorial for Linux] in the context of [http://williamjturkel.net/category/linux-unix/ a larger practical tutorial] | |||
=== Web APIs === | |||
* [https://cloud.google.com/natural-language/ Google cloud natural language API] | |||
* [http://new.opencalais.com opencalais] (free registration required) | |||
Viimane redaktsioon: 20. veebruar 2019, kell 13:49
Task
The end result of the lab is a presentation: ca five minutes during which you explain:
- which systems you experimented with
- which gave useful output from your input
- examples of output from your input (show actual textual examples)
The task of the first lab is to build and demonstrate a system which can
- tokenize text (split any text into words: trivial)
- run a NER tool on the text giving wikipedia url-s as output. See webaida. Notice that:
- some NER-s do not give wiki URL-s, but just types. This is also useful.
- for the second overall scenario (like wikidata) we do need wiki urls, though.
- run and experiment with a (recommendably) pre-trained word vectorization system like word2vec: as an example, see this tutorial. We will come back to that.
- run and experiment with parser tool on the text. There are several ways to parse, some more useful for our purposes than others. We will come back to that.
You do not have to be wildly successful with all these subtasks to pass. You will, though, have to try out each of these subtasks and report on how it went (got ok results, got bad results, could not get it to work, ...)
The second lab will build upon the results here. In particular, some choices in the second lab depend on what you managed to do with ner, word2vec and parsers.
Technology and tools
You are free to use any programming language, but Python is recommended.
You are free to use NLP tools and API-s and datasets, but your program should drive them from the beginning to end (ie your program takes input files, calls tools, modifies and prints output). It is better to use the tools than not to use them!
Popular toolkits for NLP
The recommendation is to take the first (spacy) or the second (nltk) toolkit.
- Spacy toolkit for Python
- NLTK: the main Python toolkit, see also this tutorial
- Google SyntaxNet (see in github)
- CoreNLP: the main Stanford NLP tool in the context of a larger set of Stanford NLP toolkits like stanford NER etc and this NER tutorial
- Pattern toolkit for Python
- opennlp
- PyNLP for Python
- NER tutorial for Linux in the context of a larger practical tutorial
Web APIs
- Google cloud natural language API
- opencalais (free registration required)