Skip to main content

CWB wrapper to extract concordances and collocates

Project description

Collocation and Concordance Computation

Introduction

This module is a wrapper around the IMS Open Corpus Workbench (CWB). It requires CWB version 3.4.16 or newer for anchored queries. Main purpose of the module is to extract concordance lines and to calculate collocates, as well as to extract the results of queries with more than two anchors.

Installation

The recommended way to install the module is to clone the repository and use setup.py.

python3 setup.py install

Alternatively, you can just install the requirements and make sure the ccc subfolder can be found by Python by including it in your PYTHONPATH.

Usage

CWBEngine

All methods rely on the CWBEngine from ccc.cwb, which you first have to initialize with your system specific settings:

from ccc.cwb import CWBEngine

engine = CWBEngine(
	corpus_name="EXAMPLE_CORPUS"
	registry_path="/path/to/your/cwb/registry"
)

NB: this will raise a KeyError if the named corpus is not in the specified registry.

You can use the cqp_bin to point the engine to a specific version of cqp (this is also helpful if cqp is not in your PATH):

engine = CWBEngine(
	corpus_name="EXAMPLE_CORPUS",
	registry_path="/path/to/your/cwb/registry", 
	cqp_bin="/usr/local/cwb-3.4.16/bin/cqp"
)

If you are using macros and wordlists, you have to store them in a separate folder (with subfolders wordlists and macros). Make sure you specify this folder via lib_path when initializing the engine:

engine = CWBEngine(
	corpus_name="EXAMPLE_CORPUS", 
	registry_path="/path/to/your/cwb/registry",
	lib_path="/path/to/your/lib/"
)

Concordancing

You can use the Concordance class from ccc.concordances for concordancing. The concordancer has to be initialized with the engine and accepts valid CQP queries:

from ccc.concordances import Concordance

# initialize the concordancer with the engine
concordance = Concordance(engine)

# extract concordance lines
concordance.query('[lemma="Angela"] [lemma="Merkel"]')

The result will be a dictionary with the cpos of the match as keys and the entries one concordance line each. Each concordance line is formatted as a pandas.DataFrame with the cpos of each token as index:

cpos word match offset
188530363 , False -5
188530364 dass False -4
188530365 die False -3
188530366 Tage False -2
188530367 von False -1
188530368 Angela True 0
188530369 Merkel True 0
188530370 gezählt False 1
188530371 sind False 2
188530372 . False 3

The queries must not end on a "within" clause. If you want to restrict your concordance lines by a structural attribute, use the s_break parameter (defaults to "text"). The default context window is 20 tokens to the left and 20 tokens to the right of the query match and matchend, respectively.

concordance = Concordance(engine, context=50, s_break='s')
concordance.query('[lemma="Angela"] [lemma="Merkel"]')

Further parameters for the Concordance class are order (one of "random", "first", or "last"), cut_off (for the number of concordance lines to extract), and p_show (a list of additional p-attributes besides the primary word layer to show, e.,g. "lemma" or "pos"; these will be added as additional columns).

Anchored Queries

Concordance detects anchored queries by default. The following query

concordance.query(
	'@0[lemma="Angela"]? @1[lemma="Merkel"] '
	'[word="\\("] @2[lemma="CDU"] [word="\\)"]'
)

will thus return DataFrames with an additional column indicating the anchor positions:

cpos word match offset anchor
298906425 auch False -5 None
298906426 das False -4 None
298906427 Handy False -3 None
298906428 von False -2 None
298906429 Kanzlerin False -1 None
298906430 Angela True 0 0
298906431 Merkel True 0 1
298906432 ( True 0 None
298906433 CDU True 0 2
298906434 ) True 0 None
298906435 sowie False 1 None
298906436 ihres False 2 None
298906437 Vorgängers False 3 None
298906438 Gerhard False 4 None
298906439 Schröder False 5 None

Argument Queries

Argument queries are anchored queries with additional information. (1) Each anchor can be modified by an offset (usually used to capture underspecified tokens near an anchor point). (2) Anchors can be mapped to external identifiers for further logical processing, and (3) be given a clear name:

anchor offset idx clear name
0 0 None None
1 -1 None None
2 0 None None
3 -1 None None

Furthermore, several anchor queries can be combined to form regions, which in turn can be mapped to identifiers and be given a clear name:

start end idx clear name
0 1 "0" "person X"
2 3 "1" "person Y"

Example: Given the definition of anchors and regions above, the follwing complex query extracts corpus positions where there's some correlation between "person X" (the region from anchor 0 to anchor 1) and "person Y" (anchor 2 to 3):

query = (
	"<np> []* /ap[]* [lemma = $nouns_similarity] "
	"[]*</np> \"between\" @0:[::](<np>[pos_simple=\"D|A\"]* "
	"([pos_simple=\"Z|P\" | lemma = $nouns_person_common | "
	"lemma = $nouns_person_origin | lemma = $nouns_person_support | "
	"lemma = $nouns_person_negative | "
	"lemma = $nouns_person_profession] |/region[ner])+ "
	"[]*</np>)+@1:[::] \"and\" @2:[::](<np>[pos_simple=\"D|A\"]* "
	"([pos_simple=\"Z|P\" | lemma = $nouns_person_common | "
	"lemma = $nouns_person_origin | lemma = $nouns_person_support | "
	"lemma = $nouns_person_negative | "
	"lemma = $nouns_person_profession] | /region[ner])+ "
	"[]*</np>) (/region[np] | <vp>[lemma!=\"be\"]</vp> | "
	"/region[pp] |/be_ap[])* @3:[::]"
)

NB: the set of identifiers defined in the table of anchors and in the table of regions, respectively, should not overlap.

It is customary to store these queries in json query files such as the example. You can directly process these files using the process_argmin_file method from ccc.anchors:

from ccc.argmin import process_argmin_file

# process the query file
query_path = "tests/gold/query-example.json"
result = process_argmin_file(engine, query_path)

The result is a dict with the same keys as specified in the query file as well as an entry "result" with the following keys:

  • "nr_matches": the number of query matches in the corpus.
  • "matches": the actual concordance lines as returned from Concordance().query() (see above) converted to a dict. An additional entry "holes" contains a mapping from the idx specified in the anchor and region tables to the tokens or token sequences, respectively, for each concordance line (default: lemma layer).
  • "holes": a global list of all tokens of the entities specified in the "idx" columns (default: lemma layer).

Acknowledgements

The module relies on several other python modules (see the requirements). Special thanks to Yannick Versley and Jorg Asmussen for the implementation of cwb-python.

This work has been funded by the Deutsche Forschungsgemeinschaft (DFG) within the project "Reconstructing Arguments from Noisy Text", grant number 377333057, as part of the Priority Program "Robust Argumentation Machines (RATIO)" (SPP-1999).

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cwb-ccc-0.9.2.tar.gz (15.4 kB view hashes)

Uploaded Source

Built Distributions

cwb_ccc-0.9.2-py3.6.egg (31.6 kB view hashes)

Uploaded Source

cwb_ccc-0.9.2-py3-none-any.whl (28.8 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page