Allow null frequency values and normalize SweWac source name

README.md

@@ -36,6 +36,7 @@ Keywords for Language Learning for Young and adults alike
   - [Data Instances](#data-instances)
   - [Data Fields](#data-fields)
   - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
 - [Additional Information](#additional-information)
   - [Licensing Information](#licensing-information)
   - [Citation Information](#citation-information)
@@ -50,14 +51,9 @@ Keywords for Language Learning for Young and adults alike
 
 The Swedish Kelly list is a freely available frequency-based vocabulary list
 that comprises general-purpose language of modern Swedish. The list was
-generated from a large web-acquired corpus (SweWAC) of 114 million words
+generated from a large web-acquired corpus (SweWaC) of 114 million words
 dating from the 2010s. It is adapted to the needs of language learners and
-contains 8,425 most frequent lemmas that cover 80% of SweWAC.
-
-Please refer to the article [Corpus-based approaches for the creation of a frequency
-based vocabulary list in the EU project KELLY – issues on reliability, validity and
-coverage](https://gup.ub.gu.se/publication/148533?lang=en) for information about how
-the dataset was created.
+contains 8,425 most frequent lemmas that cover 80% of SweWaC.
 
 ### Languages
 
@@ -71,7 +67,7 @@ Here is a sample of the data:
 
 ```python
 {
-    'id': '190',
+    'id': 190,
     'raw_frequency': 117835.0,
     'relative_frequency': 1033.61,
     'cefr_level': 'A1',
@@ -96,11 +92,11 @@ phrase "god dag" ("good day").
 
 - `id`: The row number for the data entry, starting at 1. Generally corresponds
   to the rank of the word.
-- `raw_frequency`: The raw frequency of the word or -1.0 if not available.
+- `raw_frequency`: The raw frequency of the word.
 - `relative_frequency`: The relative frequency of the word measured in
-  number of occurences per million words or -1.0 if not available.
+  number of occurences per million words.
 - `cefr_level`: The CEFR level (A1, A2, B1, B2, C1, C2) of the word.
-- `source`: Whether the word came from SweWAC, translation lists (T2), or
+- `source`: Whether the word came from SweWaC, translation lists (T2), or
   was manually added (manual).
 - `marker`: The grammatical marker of the word, if any, such as an article or
   infinitive marker.
@@ -115,11 +111,23 @@ has a rank of 20, while the first non-manual entry "och" ("and") has a rank of
 87. However, a conjunction and common stopword is far more likely to occur than
 the name of a city.
 
-Additionally, manually entries hav a recoreded relative frequency of `1000000`.
-
 ### Data Splits
 
-There is a single split, `train`. 
+There is a single split, `train`.
+
+## Dataset Creation
+
+Please refer to the article [Corpus-based approaches for the creation of a frequency
+based vocabulary list in the EU project KELLY – issues on reliability, validity and
+coverage](https://gup.ub.gu.se/publication/148533?lang=en) for information about how
+the original dataset was created and considerations for using the data.
+
+**The following changes have been made to the original dataset**:
+
+- Changed header names.
+- Normalized the large web-acquired corpus name to "SweWac" in the `source` field.
+- Set the relative frequency of manual entries to null rather than 1000000.
+
 
 ## Additional Information
 

dataset_infos.json

@@ -0,0 +1 @@
+{"default": {"description": "The Swedish Kelly list is a freely available frequency-based vocabulary list that comprises general-purpose language of modern Swedish. The list was generated from a large web-acquired corpus (SweWaC) of 114 million words dating from the 2010s. It is adapted to the needs of language learners and contains 8,425 most frequent lemmas that cover 80% of SweWaC.", "citation": "@article{Kilgarriff2013,\ndoi = {10.1007/s10579-013-9251-2},\nurl = {https://doi.org/10.1007/s10579-013-9251-2},\nyear = {2013},\nmonth = sep,\npublisher = {Springer Science and Business Media {LLC}},\nvolume = {48},\nnumber = {1},\npages = {121--163},\nauthor = {Adam Kilgarriff and Frieda Charalabopoulou and Maria Gavrilidou and Janne Bondi Johannessen and Saussan Khalil and Sofie Johansson Kokkinakis and Robert Lew and Serge Sharoff and Ravikiran Vadlapudi and Elena Volodina},\ntitle = {Corpus-based vocabulary lists for language learners for nine languages},\njournal = {Language Resources and Evaluation}\n}\n", "homepage": "https://spraakbanken.gu.se/en/resources/kelly", "license": "CC BY 4.0", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "raw_frequency": {"dtype": "float64", "id": null, "_type": "Value"}, "relative_frequency": {"dtype": "float64", "id": null, "_type": "Value"}, "cefr_level": {"dtype": "string", "id": null, "_type": "Value"}, "source": {"dtype": "string", "id": null, "_type": "Value"}, "marker": {"dtype": "string", "id": null, "_type": "Value"}, "lemma": {"dtype": "string", "id": null, "_type": "Value"}, "pos": {"dtype": "string", "id": null, "_type": "Value"}, "examples": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "kelly", "config_name": "default", "version": {"version_str": "1.0.1", "description": null, "major": 1, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 568998, "num_examples": 8425, "dataset_name": "kelly"}}, "download_checksums": {"sv.csv": {"num_bytes": 377409, "checksum": "92a8b97affa5b36031c0854729ccbbeda3065d994d3f0e058c4b343bf2c6611c"}}, "download_size": 377409, "post_processing_size": null, "dataset_size": 568998, "size_in_bytes": 946407}}

kelly.py

@@ -25,9 +25,9 @@ journal = {Language Resources and Evaluation}
 _DESCRIPTION = """\
 The Swedish Kelly list is a freely available frequency-based vocabulary list \
 that comprises general-purpose language of modern Swedish. The list was \
-generated from a large web-acquired corpus (SweWAC) of 114 million words \
+generated from a large web-acquired corpus (SweWaC) of 114 million words \
 dating from the 2010s. It is adapted to the needs of language learners \
-and contains 8,425 most frequent lemmas that cover 80% of SweWAC.
+and contains 8,425 most frequent lemmas that cover 80% of SweWaC.\
 """
 
 _HOMEPAGE = "https://spraakbanken.gu.se/en/resources/kelly"
@@ -42,19 +42,19 @@ _URLS = {
 class Kelly(datasets.GeneratorBasedBuilder):
   """Kelly: Keywords for Language Learning for Young and adults alike"""
 
-  VERSION = datasets.Version("1.0.0")
+  VERSION = datasets.Version("1.0.1")
 
   def _info(self):
     features = datasets.Features(
       {
-        "id": datasets.Value("string"),
+        "id": datasets.Value("int32"),
         "raw_frequency": datasets.Value("float64"),
         "relative_frequency": datasets.Value("float64"),
         "cefr_level": datasets.Value("string"),
         "source": datasets.Value("string"),
         "marker": datasets.Value("string"),
         "lemma": datasets.Value("string"),
-        "class": datasets.Value("string"),
+        "pos": datasets.Value("string"),
         "examples": datasets.Value("string"),
       }
     )
@@ -89,12 +89,12 @@ class Kelly(datasets.GeneratorBasedBuilder):
         a, b, c, d, e, f, g, h, i = row
         yield key, {
           "id": a,
-          "raw_frequency": b or "-1",
-          "relative_frequency": c or "-1",
+          "raw_frequency": b or "NaN",
+          "relative_frequency": c or "NaN",
           "cefr_level": d,
           "source": e,
           "marker": f,
           "lemma": g,
-          "class": h,
+          "pos": h,
           "examples": i,
         }

sv.csv

@@ -1,90 +1,90 @@
-ID,Raw freq,WPM,CEFR levels,Source,Grammar,Swedish items for translation,Word classes,Examples
-1,,1000000.00,A1,manual,,andra,numeral,
-2,,1000000.00,A1,manual,,arton,numeral,
-3,,1000000.00,A1,manual,,bakre,adjective,
-4,,1000000.00,A1,manual,en,brunch,noun-en,
-5,,1000000.00,A1,manual,en,decimeter,noun-en,
-6,,1000000.00,A1,manual,,elva,numeral,
-7,,1000000.00,A1,manual,,ett,numeral,
-8,416,1000000.00,A1,manual,,Europa,proper name,
-9,,1000000.00,A1,manual,,fem,numeral,
-10,,1000000.00,A1,manual,,femte,numeral,
-11,,1000000.00,A1,manual,,femtio,numeral,
-12,,1000000.00,A1,manual,,femton,numeral,
-13,,1000000.00,A1,manual,,fjorton,numeral,
-14,,1000000.00,A1,manual,,fjärde,numeral,
-15,,1000000.00,A1,manual,,främre,adjective,
-16,,1000000.00,A1,manual,,fyra,numeral,
-17,,1000000.00,A1,manual,,fyrtio,numeral,
-18,,1000000.00,A1,manual,,första,numeral,
-19,,1000000.00,A1,manual,,Grekland,proper name,
-20,,1000000.00,A1,manual,,Göteborg,proper name,
-21,,1000000.00,A1,manual,ett,hekto,noun-ett,
-22,,1000000.00,A1,manual,en,hela,noun-en,
-23,,1000000.00,A1,manual,,hundra,numeral,
-24,,1000000.00,A1,manual,,hundratusen,numeral,
-25,,1000000.00,A1,manual,,hörsel,noun-en,
-26,,1000000.00,A1,manual,en,inrikesminister,noun-en,
-27,,1000000.00,A1,manual,,inrikespolitik,noun-en,
-28,,1000000.00,A1,manual,,Italien,proper name,
-29,,1000000.00,A1,manual,,Kina,proper name,
-30,,1000000.00,A1,manual,ett,kvällsmål,noun-ett,
-31,,1000000.00,A1,manual,,känsel,noun-en,
-32,,1000000.00,A1,manual,ett,mellanmål,noun-ett,
-33,,1000000.00,A1,manual,,miljard,numeral,
-34,,1000000.00,A1,manual,,miljon,numeral,
-35,,1000000.00,A1,manual,ett,milligram,noun-ett,
-36,,1000000.00,A1,manual,en,millimeter,noun-en,
-37,,1000000.00,A1,manual,en,morbror,noun-en,
-38,,1000000.00,A1,manual,ett,månadsskifte,noun-ett,
-39,,1000000.00,A1,manual,en,nia,noun-en,
-40,,1000000.00,A1,manual,,nio,numeral,
-41,,1000000.00,A1,manual,,nionde,numeral,
-42,,1000000.00,A1,manual,,nittio,numeral,
-43,,1000000.00,A1,manual,,nitton,numeral,
-44,,1000000.00,A1,manual,,nordost,noun,
-45,,1000000.00,A1,manual,,Norge,proper name,
-46,,1000000.00,A1,manual,,pingst,noun-en,
-47,,1000000.00,A1,manual,,Polen,proper name,
-48,,1000000.00,A1,manual,,Ryssland,proper name,
-49,,1000000.00,A1,manual,,sex,numeral,
-50,,1000000.00,A1,manual,en,sexa,noun-en,
-51,,1000000.00,A1,manual,,sextio,numeral,
-52,,1000000.00,A1,manual,,sexton,numeral,
-53,,1000000.00,A1,manual,,sju,numeral,
-54,,1000000.00,A1,manual,en,sjua,noun-en,
-55,,1000000.00,A1,manual,,sjunde,numeral,
-56,,1000000.00,A1,manual,,sjuttio,numeral,
-57,,1000000.00,A1,manual,,sjutton,numeral,
-58,,1000000.00,A1,manual,,sjätte,numeral,
-59,541,1000000.00,A1,SweWaC,,Stockholm,proper name,
-60,,1000000.00,A1,manual,en,storasyster,noun-en,
-61,,1000000.00,A1,manual,,Storbritannien,proper name,
-62,,1000000.00,A1,manual,,syd,noun,
-63,,1000000.00,A1,manual,,sydväst,noun,
-64,,1000000.00,A1,manual,,sydöst,noun,
-65,,1000000.00,A1,manual,en,syssling,noun-en,
-66,,1000000.00,A1,manual,en,tia,noun-en,
-67,,1000000.00,A1,manual,,tio,numeral,
-68,,1000000.00,A1,manual,,tionde,numeral,
-69,,1000000.00,A1,manual,,tiotusen,numeral,
-70,,1000000.00,A1,manual,,tjugo,numeral,
-71,,1000000.00,A1,manual,,tolv,numeral,
-72,,1000000.00,A1,manual,,tre,numeral,
-73,,1000000.00,A1,manual,,tredje,numeral,
-74,,1000000.00,A1,manual,,trettio,numeral,
-75,,1000000.00,A1,manual,,tretton,numeral,
-76,,1000000.00,A1,manual,,tusen,numeral,
-77,,1000000.00,A1,manual,,två,numeral,
-78,,1000000.00,A1,manual,en,utbildningsminister,noun-en,
-79,,1000000.00,A1,manual,en,veckodag,noun-en,
-80,,1000000.00,A1,manual,en,åtta,noun-en,
-81,,1000000.00,A1,manual,,åtta,numeral,
-82,,1000000.00,A1,manual,,åttio,numeral,
-83,,1000000.00,A1,manual,,åttonde,numeral,
-84,,1000000.00,A1,manual,en,änkeman,noun-en,
-85,,1000000.00,A1,manual,en,änkling,noun-en,
-86,,1000000.00,A1,manual,ett,ögonlock,noun-ett,
+ID,Raw Frequency,Relative Frequency,CEFR Level,Source,Marker,Lemma,POS,Examples
+1,,,A1,manual,,andra,numeral,
+2,,,A1,manual,,arton,numeral,
+3,,,A1,manual,,bakre,adjective,
+4,,,A1,manual,en,brunch,noun-en,
+5,,,A1,manual,en,decimeter,noun-en,
+6,,,A1,manual,,elva,numeral,
+7,,,A1,manual,,ett,numeral,
+8,416,,A1,manual,,Europa,proper name,
+9,,,A1,manual,,fem,numeral,
+10,,,A1,manual,,femte,numeral,
+11,,,A1,manual,,femtio,numeral,
+12,,,A1,manual,,femton,numeral,
+13,,,A1,manual,,fjorton,numeral,
+14,,,A1,manual,,fjärde,numeral,
+15,,,A1,manual,,främre,adjective,
+16,,,A1,manual,,fyra,numeral,
+17,,,A1,manual,,fyrtio,numeral,
+18,,,A1,manual,,första,numeral,
+19,,,A1,manual,,Grekland,proper name,
+20,,,A1,manual,,Göteborg,proper name,
+21,,,A1,manual,ett,hekto,noun-ett,
+22,,,A1,manual,en,hela,noun-en,
+23,,,A1,manual,,hundra,numeral,
+24,,,A1,manual,,hundratusen,numeral,
+25,,,A1,manual,,hörsel,noun-en,
+26,,,A1,manual,en,inrikesminister,noun-en,
+27,,,A1,manual,,inrikespolitik,noun-en,
+28,,,A1,manual,,Italien,proper name,
+29,,,A1,manual,,Kina,proper name,
+30,,,A1,manual,ett,kvällsmål,noun-ett,
+31,,,A1,manual,,känsel,noun-en,
+32,,,A1,manual,ett,mellanmål,noun-ett,
+33,,,A1,manual,,miljard,numeral,
+34,,,A1,manual,,miljon,numeral,
+35,,,A1,manual,ett,milligram,noun-ett,
+36,,,A1,manual,en,millimeter,noun-en,
+37,,,A1,manual,en,morbror,noun-en,
+38,,,A1,manual,ett,månadsskifte,noun-ett,
+39,,,A1,manual,en,nia,noun-en,
+40,,,A1,manual,,nio,numeral,
+41,,,A1,manual,,nionde,numeral,
+42,,,A1,manual,,nittio,numeral,
+43,,,A1,manual,,nitton,numeral,
+44,,,A1,manual,,nordost,noun,
+45,,,A1,manual,,Norge,proper name,
+46,,,A1,manual,,pingst,noun-en,
+47,,,A1,manual,,Polen,proper name,
+48,,,A1,manual,,Ryssland,proper name,
+49,,,A1,manual,,sex,numeral,
+50,,,A1,manual,en,sexa,noun-en,
+51,,,A1,manual,,sextio,numeral,
+52,,,A1,manual,,sexton,numeral,
+53,,,A1,manual,,sju,numeral,
+54,,,A1,manual,en,sjua,noun-en,
+55,,,A1,manual,,sjunde,numeral,
+56,,,A1,manual,,sjuttio,numeral,
+57,,,A1,manual,,sjutton,numeral,
+58,,,A1,manual,,sjätte,numeral,
+59,541,,A1,SweWaC,,Stockholm,proper name,
+60,,,A1,manual,en,storasyster,noun-en,
+61,,,A1,manual,,Storbritannien,proper name,
+62,,,A1,manual,,syd,noun,
+63,,,A1,manual,,sydväst,noun,
+64,,,A1,manual,,sydöst,noun,
+65,,,A1,manual,en,syssling,noun-en,
+66,,,A1,manual,en,tia,noun-en,
+67,,,A1,manual,,tio,numeral,
+68,,,A1,manual,,tionde,numeral,
+69,,,A1,manual,,tiotusen,numeral,
+70,,,A1,manual,,tjugo,numeral,
+71,,,A1,manual,,tolv,numeral,
+72,,,A1,manual,,tre,numeral,
+73,,,A1,manual,,tredje,numeral,
+74,,,A1,manual,,trettio,numeral,
+75,,,A1,manual,,tretton,numeral,
+76,,,A1,manual,,tusen,numeral,
+77,,,A1,manual,,två,numeral,
+78,,,A1,manual,en,utbildningsminister,noun-en,
+79,,,A1,manual,en,veckodag,noun-en,
+80,,,A1,manual,en,åtta,noun-en,
+81,,,A1,manual,,åtta,numeral,
+82,,,A1,manual,,åttio,numeral,
+83,,,A1,manual,,åttonde,numeral,
+84,,,A1,manual,en,änkeman,noun-en,
+85,,,A1,manual,en,änkling,noun-en,
+86,,,A1,manual,ett,ögonlock,noun-ett,
 87,2966316,26019.68,A1,SweWaC,,och (vardagl. å; förk. o.),conj,
 88,2624032,23017.26,A1,SweWaC,att,vara (vardagl. va),verb,e.g. var så god!
 89,2197163,19272.89,A1,SweWaC,,i,prep,
@@ -2225,7 +2225,7 @@ ID,Raw freq,WPM,CEFR levels,Source,Grammar,Swedish items for translation,Word cl
 2224,3775,33.11,A2,SweWaC,en,finansiering,noun-en,
 2225,3775,33.11,A2,SweWaC,ett,löfte,noun-ett,
 2226,3775,33.11,A2,SweWaC,att,mörda,verb,
-2227,,33.10,A2,SweWAC,,anställd,adjective,
+2227,,33.10,A2,SweWaC,,anställd,adjective,
 2228,3772,33.09,A2,SweWaC,en,amerikan,noun-en,
 2229,3772,33.09,A2,SweWaC,en,rit,noun-en,
 2230,3771,33.08,A2,SweWaC,,status,noun-en,
@@ -6019,7 +6019,7 @@ ID,Raw freq,WPM,CEFR levels,Source,Grammar,Swedish items for translation,Word cl
 6018,722,6.33,C1,SweWaC,,invandringspolitik,noun-en,
 6019,722,6.33,C1,SweWaC,att,utbrista,verb,
 6020,,6.33,C1,T2,,aids,noun,
-6021,,6.33,C1,SweWAC,att,bekanta,verb,e.g. bekanta sig
+6021,,6.33,C1,SweWaC,att,bekanta,verb,e.g. bekanta sig
 6022,721,6.32,C1,SweWaC,en,utövare,noun-en,
 6023,,6.32,C1,T2,att,angå,verb,
 6024,720,6.32,C1,SweWaC,en,förlossning,noun-en,