The most powerful thing about corpkit is its ability to search parsed corpora for very complex constituency, dependency or token level features.
Before we begin, make sure you’ve
set the corpus as the thing to search:
> set nyt-parsed as corpus # you could also try just typing `set` ...
By default, when using the interpreter, searching also produces concordance lines. If you don’t need them, you can use
toggle conc to switch them off, or on again. This can dramatically speed up processing time.
> search corpus ### interactive search helper > search corpus for words matching ".*" > search corpus for words matching "^[A-M]" showing lemma and word with case_sensitive > search corpus for cql matching '[pos="DT"] [pos="NN"]' showing pos and word with coref > search corpus for function matching roles.process showing dependent-lemma > search corpus for governor-lemma matching processes.verbal showing governor-lemma, lemma > search corpus for words matching any and not words matching wordlists.closedclass > search corpus for trees matching '/NN.?/ >># NP' > search corpus for pos matching NNP showing ngram-word and pos with gramsize as 3 > etc.
Under the surface, what you are doing is selecting a Corpus object to search, and then generating arguments for the
interrogate() method. These arguments, in order, are:
- search criteria
- exclude criteria
- show values
- Keyword arguments
Here is a syntax example that might help you see how the command gets parsed. Note that there are two ways of setting exclude criteria.
> search corpus \ # select object ... for words matching 'ing$' and \ # search criterion ... not lemma matching 'being' and \ # exclude criterion ... pos matching 'NN' \ # seach criterion ... excluding words matching wordlists.closedclass \ # exclude criterion ... showing lemma and pos and function \ # show values ... with preserve_case and \ # boolean keyword arg ... not no_punct and \ # bool keyword arg ... excludemode as 'all' # keyword arg
Working with metadata¶
By default, corpkit treats folders within your corpus as subcorpora. If you want to treat files, rather than folders, as subcorpora, you can do:
> search corpus for words matching 'ing$' with subcorpora as files
If you have metadata in your corpus, you can use the metadata value as the subcorpora:
> search corpus for words matching 'ing$' with subcorpora as speaker
If you don’t want to keep specifying the subcorpus structure every time you search a corpus, you have a couple of choices. First, you can set the default subcorpus value with for the session with
set subcorpora. This applies the filter globally, to whatever corpus you search:
# use speaker metadata as subcorpora > set subcorpora as speaker # ignore folders, use files as subcorpora > set subcorpora as files
You can also define metadata filters, which skip sentences matching a metadata feature, or which keep only sentences matching a metadata feature:
# if you have a `year` metadata field, skip this decade > set skip year as '^201' # if you want only this decade: > set keep year as '^201'
If you want to set subcorpora and filters for a corpus, rather than globally, you can do this by passing in the values when you select the corpus:
> set mydata-parsed as corpus with year as subcorpora and \ ... just year as '^201' and skip speaker as 'chomsky' # forget filters for this corpus: > set mydata-parsed
Sampling a corpus¶
Sometimes, your corpus is too big to search quickly. If this is the case, you can use the
sample command to create a randomise sample of the corpus data:
> sample 3 subcorpora of corpus > sample 100 files of corpus
If you pass in a float, it will try to get a proportional amount of data:
sample 0.33 subcorpora of corpus will return a third of the subcorpora in the corpus.
A sampled corpus becomes an object called
sampled. You can then refer to it when searching:
> search sampled for words matching '^[abcde]'
Global metadata filters and subcorpus declarations will be observed when searching this corpus as well.