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Searching in-memory corpus with Corpus Query Language (CQL)

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# Concordancer

This library loads and indexes a corpus in RAM and provides concordance search to retrieve data from the corpus with (a subset of) Corpus Query Language (CQL).

## Installation

```bash
pip install -U concordancer
```

## Usage

[Concordancer](https://github.com/liao961120/concordancer) is designed with this workflow in mind:

![](https://img.yongfu.name/concordancer/workflow.png)

The user is expected to preprocess the text data to match the corpus data required by ``concordancer``. Once this is done, subsequent tasks such as indexing the copus, writing query functions to search the corpus, and displaying results in an aligned keyword-in-context format are all done by ``concordancer``. The user could then further process the search results (exported as JSON by ``concordancer``) for other uses.

### Input corpus data structure

``concordancer`` requires the corpus to be structured (minimally) as:

```python
[  # a corpus
    {       # a text
        'text': [
            [<tk>, <tk>, <tk>, ...],   # a sentence in a text
            [<tk>, <tk>, <tk>, ...],   # another sentence in a text
            ...
            [<tk>, <tk>, <tk>, ...]    # the last sentence in a text
        ]
    },
    {...},  # another text                      
    ...
]
```

where `<tk>` is a dictionary representating a token, which may resemble something like:

```python
{
    'word': 'hits',
    'lemma': 'hit',
    'pos': 'V'
}
```

This structure allows the corpus to be saved conveniently as a [newline-delimited JSON](https://jsonlines.org) file (`.jsonl`), where each line of the file corresponds to a single text in the corpus, represented as a JSON object (i.e., a dictionary in Python). You can see an example of the corpus file saved in `.jsonl` [here](https://github.com/liao961120/concordancer/blob/main/example-corpus-data/tokenDict.jsonl). The code below uses a corpus saved in `.jsonl` format for demonstration.

### Loading a corpus from file

The code below uses an [example corpus](https://github.com/liao961120/concordancer/tree/main/test-data), which is saved as a **newline-delimited JSON** file (described in the previous section).

```python
import json
from concordancer.demo import download_demo_corpus
from concordancer.concordancer import Concordancer
from concordancer import server

# Load demo corpus
fp = download_demo_corpus(to="~/Desktop")
with open(fp, encoding="utf-8") as f:
    corpus = [ json.loads(l) for l in f ]

# Index and initiate the corpus as a concordancer object
C = Concordancer(corpus)
C.set_cql_parameters(default_attr="word", max_quant=3)
```

### Interactive Search Interface

You can start an interactive server to query and read results through your browser:

```python
>>> server.run(C)
Initializing server...
Start serving at http://localhost:1420
```

![](https://img.yongfu.name/concordancer/query_interface.png)

### CQL Concordance search

Alternatively, you can work with the `Concordancer` object, which allows you to send CQL queries to the corpus:

```python
cql = '''
verb:[pos="V.*"] noun:[pos="N[abch]"]
'''
concord_list = C.cql_search(cql, left=2, right=2)
```

The results of a query is returned as a generator, which can be converted to a list of dictionaries (and then to JSON or other data structures for further uses):

```python
>>> concord_list = list(concord_list)
>>> concord_list[:2]
[
    {
        'left': [{'word': '買', 'pos': 'VC'}, {'word': '了', 'pos': 'Di'}],
        'keyword': [{'word': '覺得', 'pos': 'VK'}, {'word': '材質', 'pos': 'Na'}],
        'right': [{'word': '很', 'pos': 'Dfa'}, {'word': '對', 'pos': 'VH'}],
        'position': {'doc_idx': 78, 'sent_idx': 13, 'tk_idx': 9},
        'captureGroups': {'verb': [{'word': '覺得', 'pos': 'VK'}],
                          'noun': [{'word': '材質', 'pos': 'Na'}]}
    },
    {
        'left': [{'word': '“', 'pos': 'PARENTHESISCATEGORY'},
                 {'word': '不', 'pos': 'D'}],
        'keyword': [{'word': '戴', 'pos': 'VC'}, {'word': '錶', 'pos': 'Na'}],
        'right': [{'word': '世代', 'pos': 'Na'}, {'word': '”', 'pos': 'VC'}],
        'position': {'doc_idx': 52, 'sent_idx': 7, 'tk_idx': 36},
        'captureGroups': {'verb': [{'word': '戴', 'pos': 'VC'}],
                          'noun': [{'word': '錶', 'pos': 'Na'}]}
    }
]
```

### Keyword in Context

To better read the concordance lines, pass `concord_list` into `concordancer.kwic_print.KWIC()` to print them as a keyword-in-context format in the console:

```python
>>> from concordancer.kwic_print import KWIC
>>> KWIC(concord_list[:5])
left                        keyword          right             LABEL: verb    LABEL: noun
--------------------------  ---------------  ----------------  -------------  -------------
買/VC 了/Di                 覺得/VK 材質/Na  很/Dfa 對/VH      覺得/VK        材質/Na
“/PARENTHESISCATEGORY 不/D  戴/VC 錶/Na      世代/Na ”/VC      戴/VC          錶/Na
聯名鞋/Na 趁著/P            過年/VA 期間/Na  穿出去/VB 四處/D  過年/VA        期間/Na
走/VA  /WHITESPACE          燒/VC 錢/Na      啊/T ～/FW        燒/VC          錢/Na
正/VH 韓/Nc                 賣/VD 家/Nc      裡面/Ncd 很/Dfa   賣/VD          家/Nc
```

## Supported CQL features

CQL search is supported through [cqls](https://github.com/liao961120/cqls), which implements a (quite useful) subset of CQL:

- token: `[]`, `"我"`, `[word="我"]`, `[word!="我" & pos="N.*"]`
- token-level quantifier: `+`, `*`, `?`, `{n,m}`
- grouping: `("a" "b"? "c"){1,2}`
- label: `lab1:[word="我" & pos="N.*"] lab2:("a" "b")`

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