NL Reference Corpus

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As a natural language corpus, the NC varies for each language. It is derived from a base corpus to be compiled and processed according to the following criteria:
 
As a natural language corpus, the NC varies for each language. It is derived from a base corpus to be compiled and processed according to the following criteria:
 
#The '''Base Corpus''' must have at least 5,000,000 tokens (strings isolated by blank space and other word boundary markers). It must be representative of the contemporary standard use of the written language, and should include documents from as many different genres and domains as possible.  
 
#The '''Base Corpus''' must have at least 5,000,000 tokens (strings isolated by blank space and other word boundary markers). It must be representative of the contemporary standard use of the written language, and should include documents from as many different genres and domains as possible.  
−
#The Base Corpus must be '''segmented''' (in sentences) and '''tagged''' for POS.
+
#The Base Corpus must be '''segmented''' (in sentences).  
−
#The segmented corpus is used to calculate the '''average sentence length''' (ASL), which is the median of the length (in words) of all sentences.
+
#The Segmented Corpus must be '''tokenized''' (according to the natural language dictionary exported from the UNLarium).
−
#The tagged corpus is used to extract the [[LSS|linear sentence structures]] (LSS), which are sequences of POS, and to calculate their frequency of occurrence.
+
#The Tokenized Corpus must be '''annotated''' for lexical category, in order to generate the [[LSS|linear sentence structures]] (LSS).
−
#The average sentence length (ASL) and the linear sentence structures (LSS) are used to generate the '''NC templates''', as follows:
+
#The Annotated Corpus (C) must be subdivided into 6 different subsets, according to the number of tokens:
−
#*NC-A1 = 100 most frequent LSS's for noun phrases where length < (ASL/2) (100 most frequent shortest NP's)
+
#*A1C = length <= 15th percentile (very small sentences)
−
#*NC-A2 = 300 most frequent LSS's for verbal phrases where length < (ASL/2) (300 most frequent shortest VP's)
+
#*A2C = 15th percentile < length <= 30th percentile (small sentences)
−
#*NC-B1 = 500 most frequent LSS's where length < ASL (500 most frequent short structures)
+
#*B1C = 30th percentile < length <= 45th percentile (small medium-size sentences)
−
#*NC-B2 = 500 most frequent LSS's where length < ASL (500 most frequent short structures)
+
#*B2C = 45th percentile < length <= 60th percentile (long medium-size sentences)
−
#*NC-C1 = 500 most frequent LSS's where length >= ASL
+
#*C1C = 60th percentile < length <= 80th percentile (long sentences)
−
#*NC-C2 = 500 most frequent SSS's where length >= ASL
+
#*C2C = length > 80th percentile (very long sentences)
−
#The NC templates are used to compile the NC corpora: the training corpora and the testing corpora. The training corpora consists of 1 exemplar of each LSS, and will be used to prepare the grammar. The testing corpora consists of 4 exemplars of each LSS randomly selected in the Base Corpus. The whole NC corpora (i.e., 5 exemplars for each LSS) is used to calculate the [[F-measure]], which is the parameter for assessing the precision and the recall of the grammars.
+
#Each subcorpus is used to compile a part of the NC corpus: the training corpora (A) and the testing corpora (B).
 +
#*The training corpora consists of 1 exemplar of the 1,000 most frequent LSS, and will be used to prepare the grammar:
 +
#**A1A = 1 sentence for each 1,000 most frequent LSS from A1_C (1,000 sentences in total)
 +
#**A2A = 1 sentence for each 1,000 most frequent LSS from A2_C (1,000 sentences in total)
 +
#**B1A = 1 sentence for each 1,000 most frequent LSS from B1_C (1,000 sentences in total)
 +
#**B2A = 1 sentence for each 1,000 most frequent LSS from B2_C (1,000 sentences in total)
 +
#**C1A = 1 sentence for each 1,000 most frequent LSS from C1_C (1,000 sentences in total)
 +
#**C2A = 1 sentence for each 1,000 most frequent LSS from C2_C (1,000 sentences in total)
 +
#*The testing corpora consists of 4 exemplars of each LSS included in the training corpora. The exemplars are randomly selected in the Annotated Corpus.
 +
#**A1B = 4 sentences for each 1,000 most frequent LSS from A1_C (4,000 sentences in total)
 +
#**A2B = 4 sentences for each 1,000 most frequent LSS from A2_C (4,000 sentences in total)
 +
#**B1B = 4 sentences for each 1,000 most frequent LSS from B1_C (4,000 sentences in total)
 +
#**B2B = 4 sentences for each 1,000 most frequent LSS from B2_C (4,000 sentences in total)
 +
#**C1B = 4 sentences for each 1,000 most frequent LSS from C1_C (4,000 sentences in total)
 +
#**C2B = 4 sentences for each 1,000 most frequent LSS from C2_C (4,000 sentences in total)
 +
#The whole NC corpus (i.e., 5 exemplars for each LSS) is used to calculate the [[F-measure]], which is the parameter for assessing the precision and the recall of the grammars.
  
 
== Files ==
 
== Files ==
−
*Arabic
+
{|border=1 cellpadding=5
−
**Source: Wikipedia
+
!rowspan=2|Language
−
**Total number of distinct sentences: 801,258
+
!colspan=6|Training Corpora (A)
−
**ASL = 16
+
!colspan=6|Test Corpora (B)
−
**Raw files
+
!colspan=6|Annotated Corpora (C)
−
***[http://www.unlweb.net/resources/corpus/NC/NC_ara_A.rar Corpus NC_ara_A] (sentences < ASL/2): 121,747 (9 MB)
+
!colspan=6|Percentiles<br |>(number of tokens)
−
***[http://www.unlweb.net/resources/corpus/NC/NC_ara_B.rar Corpus NC_ara_B] (ASL/2 <= sentences ASL): 256,352 (45 MB)
+
!colspan=6|Sentences (Total)
−
***[http://www.unlweb.net/resources/corpus/NC/NC_ara_C.rar Corpus NC_ara_C] (sentences >= ASL): 423,159
+
|-
−
**[[LSS|LSS patterns]]
+
!A1A
−
***[http://www.unlweb.net/resources/corpus/NC/LSS_ara_A.rar Corpus LSS_ara_A] (46,232 distinct patterns extracted from sentences < ASL/2) (1.8 MB)
+
!A2A
 +
!B1A
 +
!B2A
 +
!C1A
 +
!C2A
 +
!A1B
 +
!A2B
 +
!B1B
 +
!B2B
 +
!C1B
 +
!C2B
 +
!A1C
 +
!A2C
 +
!B1C
 +
!B2C
 +
!C1C
 +
!C2C
 +
!A1
 +
!A2
 +
!B1
 +
!B2
 +
!C1
 +
!C2
 +
!A1C
 +
!A2C
 +
!B1C
 +
!B2C
 +
!C1C
 +
!C2C
 +
|-
 +
|Arabic
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_A1A.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_A2A.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_B1A.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_B2A.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_C1A.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_C2A.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_A1B.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_A2B.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_B1B.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_B2B.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_C1B.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_C2B.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_A1C.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_A2C.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_B1C.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_B2C.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_C1C.rar]
 +
|[http://www.unlweb.net/resources/corpus/NC/NC_ara_C2C.rar]
 +
|1-8
 +
|9-12
 +
|13-16
 +
|17-21
 +
|22-31
 +
|32-
 +
|118,067
 +
|155,495
 +
|153,312
 +
|149,948
 +
|170,893
 +
|163,214
 +
|}

Latest revision as of 15:44, 18 April 2014

The NL Reference Corpus (NC) is the corpus used to prepare and to assess grammars for sentence-based UNLization. It is divided in 6 different levels according to the Framework of Reference for UNL (FoR-UNL):

  • NC-A1: NL Reference Corpus A1
  • NC-A2: NL Reference Corpus A2
  • NC-B1: NL Reference Corpus B1
  • NC-B2: NL Reference Corpus B2
  • NC-C1: NL Reference Corpus C1
  • NC-C2: NL Reference Corpus C2

Methodology

As a natural language corpus, the NC varies for each language. It is derived from a base corpus to be compiled and processed according to the following criteria:

  1. The Base Corpus must have at least 5,000,000 tokens (strings isolated by blank space and other word boundary markers). It must be representative of the contemporary standard use of the written language, and should include documents from as many different genres and domains as possible.
  2. The Base Corpus must be segmented (in sentences).
  3. The Segmented Corpus must be tokenized (according to the natural language dictionary exported from the UNLarium).
  4. The Tokenized Corpus must be annotated for lexical category, in order to generate the linear sentence structures (LSS).
  5. The Annotated Corpus (C) must be subdivided into 6 different subsets, according to the number of tokens:
    • A1C = length <= 15th percentile (very small sentences)
    • A2C = 15th percentile < length <= 30th percentile (small sentences)
    • B1C = 30th percentile < length <= 45th percentile (small medium-size sentences)
    • B2C = 45th percentile < length <= 60th percentile (long medium-size sentences)
    • C1C = 60th percentile < length <= 80th percentile (long sentences)
    • C2C = length > 80th percentile (very long sentences)
  6. Each subcorpus is used to compile a part of the NC corpus: the training corpora (A) and the testing corpora (B).
    • The training corpora consists of 1 exemplar of the 1,000 most frequent LSS, and will be used to prepare the grammar:
      • A1A = 1 sentence for each 1,000 most frequent LSS from A1_C (1,000 sentences in total)
      • A2A = 1 sentence for each 1,000 most frequent LSS from A2_C (1,000 sentences in total)
      • B1A = 1 sentence for each 1,000 most frequent LSS from B1_C (1,000 sentences in total)
      • B2A = 1 sentence for each 1,000 most frequent LSS from B2_C (1,000 sentences in total)
      • C1A = 1 sentence for each 1,000 most frequent LSS from C1_C (1,000 sentences in total)
      • C2A = 1 sentence for each 1,000 most frequent LSS from C2_C (1,000 sentences in total)
    • The testing corpora consists of 4 exemplars of each LSS included in the training corpora. The exemplars are randomly selected in the Annotated Corpus.
      • A1B = 4 sentences for each 1,000 most frequent LSS from A1_C (4,000 sentences in total)
      • A2B = 4 sentences for each 1,000 most frequent LSS from A2_C (4,000 sentences in total)
      • B1B = 4 sentences for each 1,000 most frequent LSS from B1_C (4,000 sentences in total)
      • B2B = 4 sentences for each 1,000 most frequent LSS from B2_C (4,000 sentences in total)
      • C1B = 4 sentences for each 1,000 most frequent LSS from C1_C (4,000 sentences in total)
      • C2B = 4 sentences for each 1,000 most frequent LSS from C2_C (4,000 sentences in total)
  7. The whole NC corpus (i.e., 5 exemplars for each LSS) is used to calculate the F-measure, which is the parameter for assessing the precision and the recall of the grammars.

Files

Language Training Corpora (A) Test Corpora (B) Annotated Corpora (C) Percentiles
(number of tokens) 		Sentences (Total)
A1A A2A B1A B2A C1A C2A A1B A2B B1B B2B C1B C2B A1C A2C B1C B2C C1C C2C A1 A2 B1 B2 C1 C2 A1C A2C B1C B2C C1C C2C
Arabic [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] 1-8 9-12 13-16 17-21 22-31 32- 118,067 155,495 153,312 149,948 170,893 163,214
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