=Paper=
{{Paper
|id=Vol-1347/paper32
|storemode=property
|title=Extraction and analysis of proper nouns in Slovak texts
|pdfUrl=https://ceur-ws.org/Vol-1347/paper32.pdf
|volume=Vol-1347
|dblpUrl=https://dblp.org/rec/conf/networds/GarabikB15
}}
==Extraction and analysis of proper nouns in Slovak texts==
https://ceur-ws.org/Vol-1347/paper32.pdf
Extraction and Analysis of Proper Nouns in Slovak Texts
Radovan Garabík Radoslav Brída
Ľ. Štúr Institute of Linguistics Ľ. Štúr Institute of Linguistics
Slovak Academy of Sciences Slovak Academy of Sciences
Bratislava, Slovakia Bratislava, Slovakia
garabik@kassiopeia.juls.savba.sk brida@korpus.sk
1 Introduction rare), and the inflection is mostly realized by chang-
ing the suffix and root vowel alteration, we can ex-
Unknown named entity recognition in inflected lan- pect the overall distance between lemma and its
guages faces several specific problems – the first word forms to be not only bounded from above, but
and foremost is that the entities themselves are in- also have a regular distribution (roughly speaking,
flected1 (Dvonč et al., 1966) leading to a problem the less typical the suffix length, the less likely is
of identifying word forms as belonging to the same such a word form to appear).
lexeme, and also the problem of finding correct
lemma. In this article we analyse the distribution We used the morphological database of Slovak
of word forms for proper nouns in Slovak and de- language (Garabík and Šimková, 2012; Karčová,
scribe an algorithm for their automatic extraction 2008; Garabík, 2007), which contains (at the time
and lemmatisation. of writing) complete morphological information
of 35 009 nouns (lemmata), out of which 1031 are
The task of lemmatisation and morphological an-
proper nouns. We randomly divided the database
notation of flective (and more specifically, Slavic)
into two parts, the training set and the evaluation
languages is reasonably researched and developed
set, ensuring that about 90% of both common and
(Hajič, 2004). Since we cannot expect a morpho-
proper nouns is present in the training set. The eval-
logical database (data relating lemmata to inflected
uation set contained 101 lemmata and 694 unique
word forms and their grammatical tags) to cover all
word forms for proper nouns.
or almost all the words present in the corpus (espe-
cially proper names that keep appearing depending
on who or what has become a hot topic in mass 1
common nouns
media), using a well tuned guesser can improve the 0.8
accuracy of lemmatisation and tagging.
0.6
Common sense says that named entities (proper
n/|N|
names in particular) behave differently from com- 0.4
mon names, which translated into information the-
0.2
ory terms means that the information about whether
a word is a proper name is not independent from the 0
information about its morphology paradigm. This 0 1 2 3 4
ρ(lemma,word)
5 6 7
means we can use the information about proper 1
proper nouns
names to decrease the entropy of inflections, which
is good because it helps the guesser choose between
0.8
the possible lemmata and morphological tags. 0.6
n/|N|
2 Datasets 0.4
We denote Levenshtein distance (Ëåâåíøòåéí,
0.2
1965) between two words l and w by ρ(l, w). Since 0
a typical Slovak noun has up to 12 different word 0 1 2 3 4
ρ(lemma,word)
5 6 7
forms (two numbers, six cases – the vocative is
e.g. for the lemma Galileo, genitive would be Galilea,
1 Figure 1: Distribution of word forms according to
dative Galileovi etc. their Levenshtein distance from lemma.
Copyright c by the paper’s authors. Copying permitted for private and academic purposes.
In Vito Pirrelli, Claudia Marzi, Marcello Ferro (eds.): Word Structure and Word Usage. Proceedings of the NetWordS Final
Conference, Pisa, March 30-April 1, 2015, published at http://ceur-ws.org
140
† ‡ ‡ ‡ ‡ † †
... Toska Toskala Toskalu Toskánec Toskánska Toskánske Toskánsko Toskánskom Toskánsku Toske Tosky Toso ...
10 33 28 20 221 11 110 20 304 15 26 16
Table 1: Alphabetic list of proper noun candidates, with number of occurances in the corpus. Note the
extracted lemmata/lexemes Toska † (La Tosca), Toskánsko‡ (Tuscany), as well as unrelated Toskala,
Toso and related Toskánec (inhabitant of Tuscany) and Toskánske (Tuscan, adjective).
Fig. 1 displays the distribution of known com- interval width to be 2000 words – increasing it
mon (top) and proper (bottom) nouns, summed and above this number does not improve the accuracy
normalized through all the nouns in the training anymore and the speed is acceptable. It should be
set. Vertical error bars display the standard devi- noted that this interval is not a width of the context
ation for the given distance of word form from of the concordance – this is an interval in the lexico-
the lemma. From the graphs, we derive several graphically ordered set of proper noun candidates
conclusions – proper nouns are “less inflected”, extracted from a given text, e.g. from a novel if we
higher ratio of them is in the basic form (lemma), want to extract the whole inflectional paradigms of
and the maximum distance is ρ = 7 for common (new, unknown) proper nouns from the novel, or in-
nouns (nouns with greater distance are those with deed from the whole corpus, if we aim to augment
very irregular declension, e.g. človek → ľudia “hu- a morphological database.
man/humans”) and ρ = 5 for proper nouns. Dis- We formally describe a Levenshtein edit opera-
tributions of common and proper nouns from the tion e = (o, is , id ) – a triple of operation type o,
evaluation set match those from the training set, so position is in the source string s and position id
there appears to be some difference between com- in the destination string d, where operation type o
mon and proper nouns globally. However, categoris- is one of replace, insert or delete. For replace or
ing single nouns using these differencies between insert, the replacement/new character is taken from
distributions is not reliable. the destination string d.
Sequence of edit operations q = (e1 , e2 , e3 , ...),
3 Extracting Candidates together with the destination string d, when applied
to a string s ∈ S defines a mapping fq,d : Sq,d 7→ S,
Our algorithm extracts plausible candidates for where Sq,d and S are sets of strings.2
proper nouns (those beginning with a capital let- If we denote by t a morphological tag for a given
ter but not at the beginning of a sentence, together word form w, then for a lexeme with a lemma l a
with some additional filters) and for each candi- tuple (w, t) unambiguously refers to one inflected
date, it considers the set of words with ρ ≤ 5. This word form and its grammatical categories. We can
would require calculating the Levenshtein distance then construct a sequence of edit operations leading
between all pairs of words in the set and the com- from l to w, denoted by q(l, t).
plexity would be O(n2 ), which is unacceptable for For each proper noun from the training set, we
corpus sized inputs. Unfortunately, Levenshtein dis- precompute the functions fq(l,t),l (this can be im-
tance is a metric but cannot be used to make an proved by dividing the nouns into categories based
ordered set out of a list of words (in particular, it on their declension rules and using only one noun
cannot be used to define an ordering binary rela- from each category), to get the sequence of oper-
tion ≤). ations leading from the lemma to the tuple (w, t)
However, a trick can be applied – in a lexico- of the word form and morphological tag. Then, for
graphically ordered list of words (see Table 1) we each extracted word, we apply the functions fq(l,t),l
need to look only at some interval around the word; to every word from the abovementioned interval
word forms from beyond the interval are very un- and the word with greatest coverage (sum of the
likely to belong to the same lexeme. The complex- frequencies of generated word forms within the in-
ity will be O(Cn); where C is the (constant) size of terval) is declared the lemma to the extracted word.
the interval. This means that for some of the nouns Of course, this maximum can be attained by more
not all word forms will be covered; especially for than one word, especially if the lexeme is incom-
the shorter ones, where there is a higher probabil- 2
It is not possible to define the function f for every source
ity that many unrelated words will be within the string, since some of the operations might not be applicable to
interval. Empirically we estimated the reasonable the given strings.
141
plete. We assume that at least the most common emes have recall ≈ 1, and about 50 lexemes have
inflectional paradigms (used for proper nouns) are 0.9 ' recall ' 0.6, while only a small number
present in the training set. of lexemes have lower precision. The lower recall
is caused by insufficient data coverage – not all
number of lemmata assigned
word per word form the word forms were present in the analysed cor-
forms [%] correct all pus. The precision we obtained is excellent and the
100 18.9 0 1 accuracy of automatic lemma assignment is good.
4 0.8 0 2
418 79.2 1 1
6 1.1 1 2 5 Augmenting Morphological Database
Σ 528 100.0
The abovementioned process was used to increase
Table 2: Number of automatically assigned lem- the number of proper nouns in Slovak morpholog-
mata per word form. ical database. We used the extracted candidates
from the prim-6.1-public-all corpus with a number
of occurrences at least 100 (count of all possible
4 Evaluation word forms derived from a given lemma). We cal-
We used the algorithm to extract proper nouns culate the coverage of word forms for one lemma
from the Slovak National Corpus, version prim- as r = C(w, t)/C(g), where C(w, t) is the num-
6.1-public-all3 , of the size 829 million tokens, and ber of generated tuples of word forms and their
evaluated the results on the proper nouns from the corresponding morphological tags, and C(g) the
evaluation set. The percentage of correctly auto- number of grammar categories (usually 7 or 14; 7
matically assigned lemmata is shown in Table 2 – cases including the vocative and one or two gram-
we see that 79.2% word forms had been assigned matical numbers, with many proper nouns present
a unique lemma, which was also the correct one, only in singular).
while 18.9% had been assigned a unique, but incor- After removing generated word forms with no
rect lemma4 . corpus evidence, the average coverage of word
forms per lemma is r = 0.84 ± 0.23, i.e. 84%
70
precision
of word forms is present in the corpus, 0.23 is the
60
recall
standard deviation of the coverage. Generated word
50 forms still contain a lot of noise, therefore we also
removed those word forms whose contribution to
frequency
40
30
the number of occurrences of given lemma was less
20
than 1% (it is rare for a grammatical case to have
10
such a low percentage compared to other cases). Af-
ter this, the coverage changed to r = 0.75 ± 0.24,
0
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 where again 0.24 is the standard deviation of the
precision, recall
coverage. Then we manually proofread, corrected
and filled in the word forms for the several hun-
Figure 2: Histogram of precision and recall on au-
dred most frequent lemmata. After adding these
tomatically assigned word forms of the lexeme(s)
words to the morphological database, we iterated
for the evaluation data.
the process, re-training the algorithm and generat-
Figure 2 displays the precision and recall on ing another list of less frequent proper nouns.
word forms for proper nouns (i.e. how much of 6 Conclusion
the lexeme has been extracted; the numbers are not
weighted by the frequency of word forms in the The method has been used to improve the cover-
corpus) from the evaluation set; we note that about age of proper nouns in the Slovak morphological
70 lexemes5 have precision ≈ 1; about 40 lex- database and is used as a part of morphological
3
guesser, providing candidate lemmata and morpho-
http://korpus.juls.savba.sk/res.html
4
For 13 word forms (2.5%) the correct lemma was not
logical tags for unknown proper nouns, as part of
present in the interval of 2000 words. the morphosyntactic analysis and part of speech
5
Since the number of proper nouns in our evaluation set tagging of the Slovak National Corpus.6
was 101, these numbers are fortuitously almost identical to
6
percentage. http://korpus.juls.savba.sk
142
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