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Corpus Search Queries

A common task in corpus-based linguistic work is to search for specific elements of interest. PyCantonese provides the general-purpose :func:`~pycantonese.CHATReader.search` as a corpus object method. For a given corpus, it can search for specific Jyutping elements, Chinese characters, part-of-speech tags, and any combinations of these.

:func:`~pycantonese.CHATReader.search` is also capable of grabbing the neighboring words and utterances around the match word. This is useful for a wide variety of purposes, e.g., syntax, semantics, word collocation, discourse analysis, conversation analysis, etc.

The following examples show how :func:`~pycantonese.CHATReader.search` works using its parameters. We'll use the built-in HKCanCor corpus.

>>> import pycantonese
>>> corpus = pycantonese.hkcancor()

Searching by a Jyutping Element

Search queries by various parsed Jyutping elements are possible by specifying a Jyutping parameter in the :func:`~pycantonese.CHATReader.search` call.

The Jyutping parameters are:

  • onset
  • nucleus
  • coda
  • tone
  • initial (聲母, equivalent to onset)
  • final (韻母, equivalent to nucleus + coda)
  • jyutping (a complete Jyutping romanization, i.e., onset + nucleus + coda + tone)

For example, for the nucleus "aa":

>>> aa = corpus.search(nucleus='aa')
>>> len(aa)  # number of matching results found
22328
>>> aa[: 5]  # show first 5 results
[Token(word='啊', pos='Y', jyutping='aa3', mor=None, gloss=None, gra=None),
 Token(word='啊', pos='Y', jyutping='aa3', mor=None, gloss=None, gra=None),
 Token(word='淡季', pos='AN', jyutping='daam6gwai3', mor=None, gloss=None, gra=None),
 Token(word='𡃉', pos='Y', jyutping='gaa3', mor=None, gloss=None, gra=None),
 Token(word='嗱', pos='Y', jyutping='laa4', mor=None, gloss=None, gra=None)]

The tone parameter:

>>> tone2 = corpus.search(tone='2')
>>> len(tone2)
21167
>>> tone2[: 5]
[Token(word='講', pos='V', jyutping='gong2', mor=None, gloss=None, gra=None),
 Token(word='嗰個', pos='R', jyutping='go2go3', mor=None, gloss=None, gra=None),
 Token(word='嗰個', pos='R', jyutping='go2go3', mor=None, gloss=None, gra=None),
 Token(word='好', pos='D', jyutping='hou2', mor=None, gloss=None, gra=None),
 Token(word='抵', pos='A', jyutping='dai2', mor=None, gloss=None, gra=None)]

The parameters onset, nucleus, coda, tone, and initial may take a regular expression for more powerful search queries. For instance, we may ask for all words that contain any of the codas {p, t, k}. [ptk] as regex means to match any of these letters, and we set it to be the value of the coda parameter:

>>> codas_ptk = corpus.search(coda='[ptk]')
>>> len(codas_ptk)
12715
>>> codas_ptk[: 5]
[Token(word='迪士尼', pos='NT', jyutping='dik6si6nei4', mor=None, gloss=None, gra=None),
 Token(word='直程', pos='D', jyutping='zik6cing4', mor=None, gloss=None, gra=None),
 Token(word='七', pos='M', jyutping='cat1', mor=None, gloss=None, gra=None),
 Token(word='八月', pos='T', jyutping='baat3jyut6', mor=None, gloss=None, gra=None),
 Token(word='日', pos='Q', jyutping='jat6', mor=None, gloss=None, gra=None)]

For regular expressions in Python, see here.

The search criteria can be mixed in a single :func:`~pycantonese.CHATReader.search` call, with the following constraints:

  • If jyutping is used, no other Jyutping search parameters can be used.
  • If final is used, neither nucleus nor coda can be used.

Searching by a Chinese Character

Search queries for a given Chinese character are performed by the character parameter:

>>> machine = corpus.search(character='機')
>>> len(machine)
195
>>> machine[: 5]
[Token(word='機票', pos='N', jyutping='gei1piu3', mor=None, gloss=None, gra=None),
 Token(word='機票', pos='N', jyutping='gei1piu3', mor=None, gloss=None, gra=None),
 Token(word='機票', pos='N', jyutping='gei1piu3', mor=None, gloss=None, gra=None),
 Token(word='飛機', pos='N', jyutping='fei1gei1', mor=None, gloss=None, gra=None),
 Token(word='機', pos='NG', jyutping='gei1', mor=None, gloss=None, gra=None)]

Searching by a Part-of-speech Tag

With the parameter pos in :func:`~pycantonese.CHATReader.search`, verbs which bear the part-of-speech tag "V" in HKCanCor can be accessed as follows:

>>> verbs = corpus.search(pos='V')
>>> len(verbs)
29954
>>> verbs[: 5]
[Token(word='去', pos='V', jyutping='heoi3', mor=None, gloss=None, gra=None),
 Token(word='去', pos='V', jyutping='heoi3', mor=None, gloss=None, gra=None),
 Token(word='旅行', pos='VN', jyutping='leoi5hang4', mor=None, gloss=None, gra=None),
 Token(word='有冇', pos='V1', jyutping='jau5mou5', mor=None, gloss=None, gra=None),
 Token(word='要', pos='VU', jyutping='jiu3', mor=None, gloss=None, gra=None)]

The pos parameter may take a regular expression. For instance, we can use '^V' to match any part-of-speech tags that begin with "V" for different kinds of verbs annotated in HKCanCor:

>>> all_verbs = corpus.search(pos='^V')
>>> len(all_verbs)  # number of all verbs -- more than just "V" alone above
29726
>>> all_verbs[:20]  # printing the first 20 results
[Token(word='去', pos='V', jyutping='heoi3', mor=None, gloss=None, gra=None),
 Token(word='去', pos='V', jyutping='heoi3', mor=None, gloss=None, gra=None),
 Token(word='旅行', pos='VN', jyutping='leoi5hang4', mor=None, gloss=None, gra=None),
 Token(word='有冇', pos='V1', jyutping='jau5mou5', mor=None, gloss=None, gra=None),
 Token(word='要', pos='VU', jyutping='jiu3', mor=None, gloss=None, gra=None),
 Token(word='有得', pos='VU', jyutping='jau5dak1', mor=None, gloss=None, gra=None),
 Token(word='冇得', pos='VU', jyutping='mou5dak1', mor=None, gloss=None, gra=None),
 Token(word='去', pos='V', jyutping='heoi3', mor=None, gloss=None, gra=None),
 Token(word='係', pos='V', jyutping='hai6', mor=None, gloss=None, gra=None),
 Token(word='係', pos='V', jyutping='hai6', mor=None, gloss=None, gra=None),
 Token(word='聽', pos='V', jyutping='teng1', mor=None, gloss=None, gra=None),
 Token(word='講', pos='V', jyutping='gong2', mor=None, gloss=None, gra=None),
 Token(word='話', pos='V', jyutping='waa6', mor=None, gloss=None, gra=None),
 Token(word='去', pos='V', jyutping='heoi3', mor=None, gloss=None, gra=None),
 Token(word='玩', pos='V', jyutping='waan2', mor=None, gloss=None, gra=None),
 Token(word='可以', pos='VU', jyutping='ho2ji5', mor=None, gloss=None, gra=None),
 Token(word='住', pos='V', jyutping='zyu6', mor=None, gloss=None, gra=None),
 Token(word='話', pos='V', jyutping='waa6', mor=None, gloss=None, gra=None),
 Token(word='跟', pos='V', jyutping='gan1', mor=None, gloss=None, gra=None),
 Token(word='去', pos='V', jyutping='heoi3', mor=None, gloss=None, gra=None)]

For regular expressions in Python, see here.

For the part-of-speech tagset used by HKCanCor, see here.

Searching by a Word or Utterance Range

It is possible to include in search results the neighboring words and utterances around a match word. This functionality is useful for syntax, semantics, and discourse-level research.

The parameters word_range and utterance_range each take a tuple of (int, int).

word_range defaults to (0, 0), for zeros words (the first 0) to the left of a match word, and zeros words (the second 0) to the right -- all within the same utterance. Likewise, utterance_range defaults to (0, 0) for zero utterances preceding the utterance containing the match word and zero utterances following it.

word_range:

>>> gwo3 = corpus.search(character='過', word_range=(1, 2))
>>> len(gwo3)
705
>>> gwo3[:5]
[[Token(word='去', pos='V', jyutping='heoi3', mor=None, gloss=None, gra=None),
  Token(word='過', pos='U', jyutping='gwo3', mor=None, gloss=None, gra=None),
  Token(word='喇', pos='Y', jyutping='laa1', mor=None, gloss=None, gra=None),
  Token(word='.', pos='.', jyutping=None, mor=None, gloss=None, gra=None)],
 [Token(word='不過', pos='C', jyutping='bat1gwo3', mor=None, gloss=None, gra=None),
  Token(word='幾', pos='M', jyutping='gei2', mor=None, gloss=None, gra=None),
  Token(word='日', pos='Q', jyutping='jat6', mor=None, gloss=None, gra=None)],
 [Token(word='去', pos='VK', jyutping='heoi3', mor=None, gloss=None, gra=None),
  Token(word='過', pos='V', jyutping='gwo3', mor=None, gloss=None, gra=None),
  Token(word='嗰邊', pos='R', jyutping='go2bin1', mor=None, gloss=None, gra=None),
  Token(word='瞓覺', pos='V', jyutping='fan3gaau3', mor=None, gloss=None, gra=None)],
 [Token(word='不過', pos='C', jyutping='bat1gwo3', mor=None, gloss=None, gra=None),
  Token(word='都', pos='D', jyutping='dou1', mor=None, gloss=None, gra=None),
  Token(word=',', pos=',', jyutping=None, mor=None, gloss=None, gra=None)],
 [Token(word=',', pos=',', jyutping=None, mor=None, gloss=None, gra=None),
  Token(word='不過', pos='C', jyutping='bat1gwo3', mor=None, gloss=None, gra=None),
  Token(word='真係', pos='D', jyutping='zan1hai6', mor=None, gloss=None, gra=None),
  Token(word='好', pos='D', jyutping='hou2', mor=None, gloss=None, gra=None)]]

utterance_range:

>>> laa1 = corpus.search(jyutping='laa1', utterance_range=(1, 1))
>>> len(laa1)
1681
>>> laa1[0]  # print the 1st result
[[Token(word='係', pos='V', jyutping='hai6', mor=None, gloss=None, gra=None),
  Token(word='唔係', pos='V', jyutping='m4hai6', mor=None, gloss=None, gra=None),
  Token(word='啊', pos='Y', jyutping='aa3', mor=None, gloss=None, gra=None),
  Token(word='?', pos='?', jyutping=None, mor=None, gloss=None, gra=None)],
 [Token(word='你', pos='R', jyutping='nei5', mor=None, gloss=None, gra=None),
  Token(word='都', pos='D', jyutping='dou1', mor=None, gloss=None, gra=None),
  Token(word='去', pos='V', jyutping='heoi3', mor=None, gloss=None, gra=None),
  Token(word='過', pos='U', jyutping='gwo3', mor=None, gloss=None, gra=None),
  Token(word='喇', pos='Y', jyutping='laa1', mor=None, gloss=None, gra=None),
  Token(word='.', pos='.', jyutping=None, mor=None, gloss=None, gra=None)],
 [Token(word='咪', pos='C', jyutping='mai6', mor=None, gloss=None, gra=None),
  Token(word='係', pos='V', jyutping='hai6', mor=None, gloss=None, gra=None),
  Token(word='囖', pos='Y', jyutping='lo1', mor=None, gloss=None, gra=None),
  Token(word='.', pos='.', jyutping=None, mor=None, gloss=None, gra=None)]]

If utterance_range is not (0, 0), word_range is ignored since full utterances are already in the output.

Searching by Multiple Criteria

:func:`~pycantonese.CHATReader.search` is flexible and allows multiple parameters described above to be specified at the same time. For instance, if we are interested in pinjam ("changed tone") in Cantonese, we may be interested in all words with coda {p, t, k} plus tone 2 (high-rising):

>>> ptk_tone2 = corpus.search(coda='[ptk]', tone='2')
>>> len(ptk_tone2)
71
>>> ptk_tone2[: 10]
[Token(word='雀', pos='N', jyutping='zoek2', mor=None, gloss=None, gra=None),
 Token(word='雀', pos='N', jyutping='zoek2', mor=None, gloss=None, gra=None),
 Token(word='綠', pos='A', jyutping='luk2', mor=None, gloss=None, gra=None),
 Token(word='賊', pos='N', jyutping='caak2', mor=None, gloss=None, gra=None),
 Token(word='dut2', pos='O', jyutping='dut2', mor=None, gloss=None, gra=None),
 Token(word='碟', pos='N', jyutping='dip2', mor=None, gloss=None, gra=None),
 Token(word='碟', pos='N', jyutping='dip2', mor=None, gloss=None, gra=None),
 Token(word='碟', pos='N', jyutping='dip2', mor=None, gloss=None, gra=None),
 Token(word='碟形', pos='N', jyutping='dip2jing4', mor=None, gloss=None, gra=None),
 Token(word='碟', pos='N', jyutping='dip2', mor=None, gloss=None, gra=None)]

Output Format of Search Results

While :func:`~pycantonese.CHATReader.search` always returns a list, the format of the elements in the list can be adjusted by the parameters by_tokens and by_utterances.

If by_tokens is True (default), words are all represented in the token format with Jyutping and part-of-speech tags, as in all the examples above. Otherwise, words are text strings with Chinese characters only.

If by_utterances is False (default), the elements in the output list are words (or spans of words when word_range is used). Otherwise, all utterances containing a match word are in the output list. If utterance_range is used, by_utterances is automatically True.

Complex Searches

By design, :func:`~pycantonese.CHATReader.search` targets a single match word. If your use case needs to involve more, you'll have to write your custom code to iterate through the data and keep track of whatever is of your interest. As you have complete control over the search logic, the search can be as customized as desired, to the extent that what you're after can be formulated in terms of what the corpus data and annotations provide.

For examples of complex searches, please check out the tutorials from :ref:`archives`.