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utilities for working with lexicography data encoded using Standard Format Markers (SFM data files).

Project description

sfm_utils is a collection of python utilities to quickly and easily summarise content and identify inconsistencies in lexicography data encoded using Standard Format Markers (SFM data files). Primarily these utilities are intended to provide assistance when cleaning SFM data before converting to another format or importing into a tool such as SIL Fieldworks Language Explorer (FLEx).

SFM files contain lexicographical data structured using tags (backslash codes). For example:

\lx déláme
\ps n
\gn petite calebasse
\ps v
\gn sorte de verre
\ge drinking bowl
\gr ɓi loonde

sfm_utils scripts do not attribute meaning to the tags and are therefore independent of the set of tags used in an SFM data file. The intent of sfm_utils is to ensure that tags are used consistently throughout the data file.

Author: Gavin Falconer (gfalconer@expressivelogic.co.uk)

Installation

sfm_utils is distributed as a python package, so can be installed via pip (or your package manager of choice). Requires python v3 or above:

> pip install sfm_utils

Future Suggestion

Use a hosted version of sfm_utils within an online jupyter notebook See, for example: https://jvns.ca/blog/2017/11/12/binder–an-awesome-tool-for-hosting-jupyter-notebooks/

Introduction

Use sfm-sniffer to quickly get an insight into the content of any SFM file. sfm-sniffer lists the tags used in the file, giving the number of occurrences of each tag. It also deduces a type for each tag, and shows the number of ‘exceptions’, where the tag value did not match the expected type.

> sfm-sniffer --summary my_lexicon.sfm
\gn : gloss (national)     : occurrences=2480 : type=text            : exceptions=26
\lx : lexeme               : occurrences=2474 : type=word            : exceptions=7
\sn : sense number         : occurrences=2456 : type=enumeration     : exceptions=28
\ps : part of speech       : occurrences=2450 : type=enumeration     : exceptions=79
\ge : gloss (english)      : occurrences= 511 : type=optional word   : exceptions=12
\gr : gloss (regional)     : occurrences= 500 : type=optional phrase : exceptions=11
\glo: ???                  : occurrences= 354 : type=text            : exceptions=0

Running sfm-sniffer in full mode gives line references to pinpoint exceptions:

> sfm-sniffer my_lexicon.sfm
glo: gloss (other)        : occurrences= 354: type=text   : exceptions=0
===================================
\lx : lexeme              : occurrences=2474: type=word
7 exceptions for \lx of type 'word':
line    1: \lx <no value>
line 2335: \lx eptsá - v. int. fatsa
line 2470: \lx ékséɓé, ésséɓá
line 2474: \lx ékslá, alá
line 2712: \lx fá wé...
line 4025: \lx icá  - v.int. ɗatsa
line 11051: \lx ŋá (v.int. ŋɛŋa)
====================================
\ps : part of speech      : occurrences=2451: type=enumeration
Example values:
adj,adj adv,adj num,adj poss,adj poss.,adj?,adv,adv inter,adv tm,...
79 exceptions for \ps of type 'enumeration':
line  855: \ps v. int
line 1875: \ps v. int.
line 1879: \ps <no value>
line 1947: \ps <no value>
...

The results indicate the consistency of usage (or otherwise) for each tag. See the example walkthrough for more details.

Tag Type Deduction

Tag type deduction works by examining the set of values used for each tag. If the majority of values conform to a known type then the tag is deduced to be of that type. (The threshold applied to determine an acceptable majority can be varied by selecing a ‘strictness’ option.)

The types are checked in order, with more specific types being checked first. Therefore a tag will be deduced to be of the most specific type that can be applied to the set of values used for that tag.

Tag types may be one of the following (ordered from most specific to least specific):

Order Type Description
1 NULL type Tag never has a value.
2 number Numeric value, e.g. 1, 2, 3. The tag must have a value.
3 optional number Numeric value, or may be empty.
4 enumeration A single word or phrase drawn from a limited set of possible values. A typical example could be \ps (part of speech) accepting one of: noun, verb, adjective, adverb,… The tag must have a value.
5 optional enumeration As above, or may be empty.
6 word A single-word value. A word may include non-alphanumeric characters, but must include at least one alphanumeric character. It may not include any whitespace, period, comma or semicolon within the value. A trailing period, comma or semicolon is acceptable. The following are all valid words: ésséɓá, up!, abbrev.. The tag must have a value.
7 optional word As above, or may be empty.
8 phrase A single-phrase value. Like word but may contain whitespace. May not contain a period, comma or semicolon except as a trailing character. up and away! is a valid phrase. up; away! is not (it is assumed to be a list value). The tag must have a value.
9 optional phrase As above, or may be empty.
10 enumeration list A list of words or phrases (separated by commas or semicolons) where each word or phrase is drawn from a limited set of possible values. The tag must have a value.
11 text Any combination of characters, words or phrases. The tag must have a value.
12 optional text Any combination of characters, words or phrases, or may be empty. The optional text type is generic, and indicates that no consistent pattern of usage could be deduced for the tag.

Coming Soon…

Use sfm-struct-sniffer to analyse the tree structure of the SFM file and generate a proposed schema:

> sfm-struct-sniffer my_lexicon.sfm > my_lexicon.schema

Then use sfm-struct-sniffer to verify the integrity of the SFM data against the schema:

> sfm-struct-sniffer --verify --schema=my_lexicon.schema my_lexicon.sfm
...

The generated schema is a simple text file so can easily be modified:

\lx
    \ps
        \ge
        \go?
        \sn?
            \ge
            \go?

When it becomes necessary to edit or correct the SFM file by hand, the data can be formatted by sfm-struct-sniffer to apply indentation that shows the tree structure:

> sfm-struct-sniffer --format -schema=my_lexicon.schema my_lexicon.sfm
\lx déláme
    \ps n
        \gn petite calebasse
    \ps v
        \gn sorte de verre
        \ge drinking bowl
        \gr ɓi loonde
 \lx deremke
    \ps num
        \gn cent
        \ge one hundred
        \gr temerre

This also makes it easier to reason about the outcomes of importing the data into SIL Fieldworks Language Explorer (FLEx)

Future Suggestion

sfm-struct-sniffer could embed comments in the file to highlight exceptions or ambiguous tree elements, e.g:

\lx déláme
   \ps n
# >>> unexpected \sn
      \sn 1
# <<<

Features

  • Works with any SFM file. Inferred types are the result of statistical analysis on the SFM file contents. No semantics are assumed, no a priori knowledge is ncessary.

Usage

Usage information for sfm-sniffer can be shown by using the –help option. See also the example walkthrough.

Usage:

sfm-sniffer [--tags=<dictionary>] [--summary] [--normal|--stricter|--strictest] <file>
sfm-sniffer --dumptags
sfm-sniffer (-h | --help)
sfm-sniffer --version
Options:
-t, --tags=file
 Read a dictionary file that maps tags to labels. If unspecified, the default MDF tag labels will be used. [1]
-s, --summary Output a summary report only.
-1, --normal Apply normal type deduction rules.
-2, --stricter Apply stricter type deduction rules.
-3, --strictest
 Apply strictest type deduction rules.
-d, --dumptags Print the default SFM tag dictionary in the format used by –tags
-h, --help Show this screen.
--version Show version.

Applying stricter type deduction rules will generate a report that prefers more specific types (such as number or word) over more general types (such as optional text). However, stricter type deduction rules are more likely to generate a large number of exceptions.

Similarily, for sfm-struct-sniffer:

Usage:

sfm-struct-sniffer [--tags=<dictionary>] <file>
sfm-struct-sniffer --dumptags
sfm-struct-sniffer (-h | --help)
sfm-struct-sniffer --version
Options:
-t, --tags=file
 Read a dictionary file that maps tags to labels. If unspecified, the default MDF tag labels will be used. [1]
-d, --dumptags Print the default SFM tag dictionary in the format used by –tags
-h, --help Show this screen.
--version Show version.

Repository contents

TODO

See Also

  • SOLID is an existing graphical utility provided by SIL to check, clean up, and convert SDF files.

References

  1. Making Dictionaries: A guide to lexicography and the Multi-Dictionary Formatter (Coward & Grimes, 2000): a description of the MDF (Multi-Dictionary Formatter) and the defined set of SFM backslash codes that are commonly recognised.
  2. Technical Notes on SFM Database Import (Ken Zook, 2010): provides further information on issues that are likely to be encountered when working with SFM files.

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