Concrete-python is the Python interface to Concrete, a natural language processing data format and set of service protocols that work across different operating systems and programming languages via Apache Thrift. Concrete-python contains generated Python classes, utility classes and functions, and scripts. It does not contain the Thrift schema for Concrete, which can be found in the Concrete GitHub repository.
For information about installing and using concrete-python, please see the online documentation.
Table of Contents
Copyright 2012-2017 Johns Hopkins University HLTCOE. All rights reserved. This software is released under the 2-clause BSD license. Please see LICENSE for more information.
concrete-python is tested on Python 2.7 or 3.5 (it does not run on
Python 2.6; it may run on more Python 3.x versions) and requires the
Thrift Python library, among other Python libraries. These are
installed automatically by
pip. The Thrift
compiler is not required.
Note: The accelerated protocol offers a (de)serialization speedup
of 10x or more; if you would like to use it, ensure a C++ compiler is
available on your system before installing concrete-python.
(If a compiler is not available, concrete-python will fall back to the
unaccelerated protocol automatically.) If you are on Linux, a suitable
C++ compiler will be listed as
gcc-c++ in your package
You can install Concrete using the
pip package manager:
pip install concrete
or by cloning the repository and running
git clone https://github.com/hltcoe/concrete-python.git cd concrete-python python setup.py install
Here and in the following sections we make use of an example Concrete
Communication file included in the concrete-python source distribution.
The Communication type represents an article, book, post, Tweet, or
any other kind of document that we might want to store and analyze.
Copy it from
tests/testdata/serif_dog-bites-man.concrete if you
have the concrete-python source distribution or download it
separately here: serif_dog-bites-man.concrete.
First we use the
concrete-inspect.py tool (explained in more detail
in the following section) to inspect some of the contents of the
concrete-inspect.py --text serif_dog-bites-man.concrete
This command prints the text of the Communication to the console. In our case the text is a short article formatted in SGML:
<DOC id="dog-bites-man" type="other"> <HEADLINE> Dog Bites Man </HEADLINE> <TEXT> <P> John Smith, manager of ACMÉ INC, was bit by a dog on March 10th, 2013. </P> <P> He died! </P> <P> John's daughter Mary expressed sorrow. </P> </TEXT> </DOC>
Now run the following command to inspect some of the annotations stored in that Communication:
concrete-inspect.py --ner --pos --dependency serif_dog-bites-man.concrete
This command shows a tokenization, part-of-speech tagging, named entity tagging, and dependency parse in a CoNLL-like columnar format:
INDEX TOKEN POS NER HEAD DEPREL ----- ----- --- --- ---- ------ 1 John NNP PER 2 compound 2 Smith NNP PER 10 nsubjpass 3 , , 4 manager NN 2 appos 5 of IN 7 case 6 ACMÉ NNP ORG 7 compound 7 INC NNP ORG 4 nmod 8 , , 9 was VBD 10 auxpass 10 bit NN 0 ROOT 11 by IN 13 case 12 a DT 13 det 13 dog NN 10 nmod 14 on IN 15 case 15 March DATE-NNP 13 nmod 16 10th JJ 15 amod 17 , , 18 2013 CD 13 amod 19 . . 1 He PRP 2 nsubj 2 died VBD 0 ROOT 3 ! . 1 John NNP PER 3 nmod:poss 2 's POS 1 case 3 daughter NN 5 dep 4 Mary NNP PER 5 nsubj 5 expressed VBD 0 ROOT 6 sorrow NN 5 dobj 7 . .
There are even more annotations stored in this Communication, but for now we move on to demonstrate handling of the Communication in Python. The example file contains a single Communication, but many (if not most) files contain several. The same code can be used to read Communications in a regular file, tar archive, or zip archive:
from concrete.util import CommunicationReader for (comm, filename) in CommunicationReader('serif_dog-bites-man.concrete'): print(comm.id) print() print(comm.text)
This loop prints the unique ID and text (the same text we saw before) of our one Communication:
tests/testdata/serif_dog-bites-man.xml <DOC id="dog-bites-man" type="other"> <HEADLINE> Dog Bites Man </HEADLINE> <TEXT> <P> John Smith, manager of ACMÉ INC, was bit by a dog on March 10th, 2013. </P> <P> He died! </P> <P> John's daughter Mary expressed sorrow. </P> </TEXT> </DOC>
In addition to the general-purpose
CommunicationReader there is a
convenience function for reading a single Communication from a regular
from concrete.util import read_communication_from_file comm = read_communication_from_file('serif_dog-bites-man.concrete')
Communications are broken into Sections, which are in turn broken into Sentences, which are in turn broken into Tokens (and that's only scratching the surface). To traverse this decomposition:
from concrete.util import lun, get_tokens for section in lun(comm.sectionList): print('* section') for sentence in lun(section.sentenceList): print(' + sentence') for token in get_tokens(sentence.tokenization): print(' - ' + token.text)
The output is:
* section * section + sentence - John - Smith - , - manager - of - ACMÉ - INC - , - was - bit - by - a - dog - on - March - 10th - , - 2013 - . * section + sentence - He - died - ! * section + sentence - John - 's - daughter - Mary - expressed - sorrow - .
Here we used
get_tokens, which abstracts the process of extracting
a sequence of Tokens from a Tokenization, and
returns its argument or (if its argument is
None) an empty list
and stands for "list un-none". Many fields in Concrete are optional,
None quickly becomes tedious.
In this Communication the tokens have been annotated with
part-of-speech tags, as we saw previously using
concrete-inspect.py. We can print them with the following code:
from concrete.util import get_tagged_tokens for section in lun(comm.sectionList): print('* section') for sentence in lun(section.sentenceList): print(' + sentence') for token_tag in get_tagged_tokens(sentence.tokenization, 'POS'): print(' - ' + token_tag.tag)
The output is:
* section * section + sentence - NNP - NNP - , - NN - IN - NNP - NNP - , - VBD - NN - IN - DT - NN - IN - DATE-NNP - JJ - , - CD - . * section + sentence - PRP - VBD - . * section + sentence - NNP - POS - NN - NNP - VBD - NN - .
We can add a new part-of-speech tagging to the Communication as well. Let's add a simplified version of the current tagging:
from concrete.util import AnalyticUUIDGeneratorFactory, now_timestamp from concrete import TokenTagging, TaggedToken, AnnotationMetadata augf = AnalyticUUIDGeneratorFactory(comm) aug = augf.create() for section in lun(comm.sectionList): for sentence in lun(section.sentenceList): sentence.tokenization.tokenTaggingList.append(TokenTagging( uuid=aug.next(), metadata=AnnotationMetadata( tool='Simple POS', timestamp=now_timestamp(), kBest=1 ), taggingType='POS', taggedTokenList=[ TaggedToken( tokenIndex=original.tokenIndex, tag=original.tag.split('-')[-1][:2], ) for original in get_tagged_tokens(sentence.tokenization, 'POS') ] ))
Here we used
generate_UUID, which generates a random UUID object,
now_timestamp, which returns a Concrete timestamp representing
the current time. But now how do we know which tagging is ours? Each
annotation's metadata contains a tool name, and we can use it to
distinguish between competing annotations:
from concrete.util import get_tagged_tokens for section in lun(comm.sectionList): print('* section') for sentence in lun(section.sentenceList): print(' + sentence') token_tag_pairs = zip( get_tagged_tokens(sentence.tokenization, 'POS', tool='Serif: part-of-speech'), get_tagged_tokens(sentence.tokenization, 'POS', tool='Simple POS') ) for (old_tag, new_tag) in token_tag_pairs: print(' - ' + old_tag.tag + ' -> ' + new_tag.tag)
The output shows our new part-of-speech tagging has a smaller, simpler set of possible values:
* section * section + sentence - NNP -> NN - NNP -> NN - , -> , - NN -> NN - IN -> IN - NNP -> NN - NNP -> NN - , -> , - VBD -> VB - NN -> NN - IN -> IN - DT -> DT - NN -> NN - IN -> IN - DATE-NNP -> NN - JJ -> JJ - , -> , - CD -> CD - . -> . * section + sentence - PRP -> PR - VBD -> VB - . -> . * section + sentence - NNP -> NN - POS -> PO - NN -> NN - NNP -> NN - VBD -> VB - NN -> NN - . -> .
Finally, let's write our newly annotated Communication back to disk:
from concrete.util import CommunicationWriter with CommunicationWriter('serif_dog-bites-man.concrete') as writer: writer.write(comm)
concrete-inspect.py to quickly explore the contents of a
Communication from the command line.
concrete-inspect.py and other
scripts are installed to the path along with the concrete-python
Run the following command to print the unique ID of our modified example Communication:
concrete-inspect.py --id serif_dog-bites-man.concrete
--metadata to print the stored annotations along with their
concrete-inspect.py --metadata serif_dog-bites-man.concrete
Communication: concrete_serif v3.10.1pre Tokenization: Serif: tokens Dependency Parse: Stanford Parse: Serif: parse TokenTagging: Serif: names TokenTagging: Serif: part-of-speech TokenTagging: Simple POS EntityMentionSet #0: Serif: names EntityMentionSet #1: Serif: values EntityMentionSet #2: Serif: mentions EntitySet #0: Serif: doc-entities EntitySet #1: Serif: doc-values SituationMentionSet #0: Serif: relations SituationMentionSet #1: Serif: events SituationSet #0: Serif: relations SituationSet #1: Serif: events CommunicationTagging: lda CommunicationTagging: urgency
--sections to print the text of the Communication, broken out
concrete-inspect.py --sections serif_dog-bites-man.concrete
Section 0 (0ab68635-c83d-4b02-b8c3-288626968e05), from 81 to 82: Section 1 (54902d75-1841-4d8d-b4c5-390d4ef1a47a), from 85 to 162: John Smith, manager of ACMÉ INC, was bit by a dog on March 10th, 2013. </P> Section 2 (7ec8b7d9-6be0-4c62-af57-3c6c48bad711), from 165 to 180: He died! </P> Section 3 (68da91a1-5beb-4129-943d-170c40c7d0f7), from 183 to 228: John's daughter Mary expressed sorrow. </P>
--entities to print the named entities detected in the
concrete-inspect.py --entities serif_dog-bites-man.concrete
Entity Set 0 (Serif: doc-entities): Entity 0-0: EntityMention 0-0-0: tokens: John Smith text: John Smith entityType: PER phraseType: PhraseType.NAME EntityMention 0-0-1: tokens: John Smith , manager of ACMÉ INC , text: John Smith, manager of ACMÉ INC, entityType: PER phraseType: PhraseType.APPOSITIVE child EntityMention #0: tokens: John Smith text: John Smith entityType: PER phraseType: PhraseType.NAME child EntityMention #1: tokens: manager of ACMÉ INC text: manager of ACMÉ INC entityType: PER phraseType: PhraseType.COMMON_NOUN EntityMention 0-0-2: tokens: manager of ACMÉ INC text: manager of ACMÉ INC entityType: PER phraseType: PhraseType.COMMON_NOUN EntityMention 0-0-3: tokens: He text: He entityType: PER phraseType: PhraseType.PRONOUN EntityMention 0-0-4: tokens: John text: John entityType: PER.Individual phraseType: PhraseType.NAME Entity 0-1: EntityMention 0-1-0: tokens: ACMÉ INC text: ACMÉ INC entityType: ORG phraseType: PhraseType.NAME Entity 0-2: EntityMention 0-2-0: tokens: John 's daughter Mary text: John's daughter Mary entityType: PER.Individual phraseType: PhraseType.NAME child EntityMention #0: tokens: Mary text: Mary entityType: PER phraseType: PhraseType.OTHER EntityMention 0-2-1: tokens: daughter text: daughter entityType: PER phraseType: PhraseType.COMMON_NOUN Entity Set 1 (Serif: doc-values): Entity 1-0: EntityMention 1-0-0: tokens: March 10th , 2013 text: March 10th, 2013 entityType: TIMEX2.TIME phraseType: PhraseType.OTHER
--mentions to show the named entity mentions in the
Communication, annotated on the text:
concrete-inspect.py --mentions serif_dog-bites-man.concrete
<ENTITY ID=0><ENTITY ID=0>John Smith</ENTITY> , <ENTITY ID=0>manager of <ENTITY ID=1>ACMÉ INC</ENTITY></ENTITY> ,</ENTITY> was bit by a dog on <ENTITY ID=3>March 10th , 2013</ENTITY> . <ENTITY ID=0>He</ENTITY> died ! <ENTITY ID=2><ENTITY ID=0>John</ENTITY> 's <ENTITY ID=2>daughter</ENTITY> Mary</ENTITY> expressed sorrow .
--situations to show the situations detected in the
concrete-inspect.py --situations serif_dog-bites-man.concrete
Situation Set 0 (Serif: relations): Situation Set 1 (Serif: events): Situation 1-0: situationType: Life.Die
--treebank to show constituency parse trees of the sentences in
concrete-inspect.py --treebank serif_dog-bites-man.concrete
(S (NP (NPP (NNP john) (NNP smith)) (, ,) (NP (NPA (NN manager)) (PP (IN of) (NPP (NNP acme) (NNP inc)))) (, ,)) (VP (VBD was) (NP (NPA (NN bit)) (PP (IN by) (NP (NPA (DT a) (NN dog)) (PP (IN on) (NP (DATE (DATE-NNP march) (JJ 10th)) (, ,) (NPA (CD 2013)))))))) (. .)) (S (NPA (PRP he)) (VP (VBD died)) (. !)) (S (NPA (NPPOS (NPP (NNP john)) (POS 's)) (NN daughter) (NPP (NNP mary))) (VP (VBD expressed) (NPA (NN sorrow))) (. .))
or independently) to show respective token-level information in a
CoNLL-like format, and use
--text to print the text of the
Communication, as described in a previous section.
concrete-inspect.py --help to show a detailed help message
explaining the options discussed above and others. All
concrete-python scripts have such help messages.
create-comm.py to generate a simple Communication from a text
file. For example, create a file called
containing the following text:
The dog ran . The cat jumped . The dolphin teleported .
Then run the following command to convert it to a Concrete Communication, creating Sections, Sentences, and Tokens based on whitespace:
create-comm.py --annotation-level token history-of-the-world.txt history-of-the-world.concrete
concrete-inspect.py as shown previously to verify the
structure of the Communication:
concrete-inspect.py --sections history-of-the-world.concrete
Section 0 (a188dcdd-1ade-be5d-41c4-fd4d81f71685), from 0 to 30: The dog ran . The cat jumped . Section 1 (a188dcdd-1ade-be5d-41c4-fd4d81f7168a), from 32 to 57: The dolphin teleported .
concrete-python provides a number of other scripts, including but not limited to:
- reads in a Concrete Communication and prints a JSON version of the Communication to stdout. The JSON is "pretty printed" with indentation and whitespace, which makes the JSON easier to read and to use for diffs.
create-comm.pybut for multiple files: reads in a tar.gz archive of text files, parses them into sections and sentence based on whitespace, and writes them back out as Concrete Communications in another tar.gz archive.
- connects to a FetchCommunicationService, retrieves one or more Communications (as specified on the command line), and writes them to disk.
- implements FetchCommunicationService, serving Communications to clients from a file or directory of Communications on disk.
- connects to a SearchService, reading queries from the console and printing out results as Communication ids in a loop.
- reads in a Concrete Communication file and prints out information
about any invalid fields. This script is a command-line wrapper
around the functionality in the
--help flag for details about the scripts' command line
Validating Concrete Communications
The Python version of the Thrift Libraries does not perform any
validation of Thrift objects. You should use the
validate_communication() function after reading and before writing
a Concrete Communication:
from concrete.util import read_communication_from_file from concrete.validate import validate_communication comm = read_communication_from_file('tests/testdata/serif_dog-bites-man.concrete') # Returns True|False, logs details using Python stdlib 'logging' module validate_communication(comm)
Thrift fields have three levels of requiredness:
- explicitly labeled as required
- explicitly labeled as optional
- no requiredness label given ("default required")
Other Concrete tools will raise an exception if a required field is
missing on deserialization or serialization, and will raise an
exception if a "default required" field is missing on serialization.
By default, concrete-python does not perform any validation of Thrift
objects on serialization or deserialization. The Python Thrift classes
do provide shallow
validate() methods, but they only check for
explicitly required fields (not "default required" fields) and do
not validate nested objects.
validate_communication() function recursively checks a
Communication object for required fields, plus additional checks for