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Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

 | 
Felice Dell'Orletta
, 
Johanna Monti
, 
Fabio Tamburini

Contributed Papers

Suoidne-varra-bleahkka-mála-bihkka-senet-dielku ‘hay-blood-ink-paint-tar-mustard-stain’ – Should compounds be lexicalized in NLP?

Linda Wiechetek, Chiara Argese, Tommi A. Pirinen et Trond Trosterud

Résumé

Lexicalizing compounds, in addition to treating them dynamically, is a key element in giving us idiomatic translations and detecting compound errors. We present and evaluate an e-dictionary (NDS) and a grammar checker (GramDivvun) for North Sámi. We achieve a coverage of 98% for NDS-queries and of 96% for compound error detection in GramDivvun.

Texte intégral

Thank you to Thomas Omma for doing the error corpus mark-up and for fun linguistic discussions, and to Lene Antonsen for digging in our corpus and helping to find just the right example.

1. Introduction

  • 1 Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attrib (...)

1In this paper1, we discuss the use and necessity of the lexicalization of compounds – in addition to the dynamic approach to compounding – in two rule-based Natural Language Processing (NLP) applications, a grammar checker GramDivvun and an electronic dictionary NDS (short for Neahttadigisánit). We argue for a dual approach and support this view with an evaluation of these tools. For comparison, we also look at a third application, a corpus tool (Korp) for the North Sámi corpus SIKOR. SIKOR, the Sámi International KORpus, is the collection of texts in different Sámi languages compiled by UiT The Arctic University of Norway and the Norwegian Sámi Parliament.

2In the past, we have mostly focussed on the dynamic approach to morphological analysis. This means that we have a lexicon with lemmata and stems, which in a finite-state manner are combined with inflectional and derivational affixes and other stems and modified when morpho-phonological processes apply. In this way the linguistic processes inflection, derivation and compounding are modelled in a dynamic way, i.e. by means of concatenation and composition as opposed to listing of all forms. Lexicalization, i.e. listing compounds or inflected word forms as such, is the alternative approach to the dynamic one. In addition to these two approaches we also use guessers for certain tasks, i.e. proper name guessing in morpho-syntactic parsing. Our approach is entirely rule-based and open source. Within our 20 year experience with language tools for the Sámi languages and other languages with complex morphology, we have achieved good results and produced reliable tools.

3There are a number of approaches to error detection of a few errortypes for morphologically complex - although less complex than North Sámi - languages like Latvian (Deksne 2019) and Russian (Rozovskaya and Roth 2019). The Latvian neural network grammar checker focusses on preposition-postposition confusion, adjective-noun agreement, mood errors in verb forms, number and case in noun forms, definiteness of adjectives and missing commata. All of these error types have a good performance with precisions between 78% and 98.5%. Judging from their regular expressions to insert artificial errors, most of their error types seem to be fairly local errors that can be resolved based on bigrams.

4The Russian system focusses on more advanced error types - case, number agreement, gender agreement, preposition and aspect. However, the results show that the system is still in its initial phase with low precision and recall for most error types (precision is between 22% and 56%, only gender agreement reaches 68%, and recall is significantly lower, between 9% and 36%). None of these approaches deals with compound error detection.

5For neural network approaches, large corpora with error mark-up are necessary, which are not available for North Sámi. The error marked-up corpus contains 120 459 words, and when looking at specific error types – as in this case compound errors – the corpus is even smaller. The Russian system is based on an error-marked corpus of 200k words (deemed too small by its authors), the Latvian system works with artificial errors, an approach that can be problematic as it does not reflect real text errors.

  • 2 To avoid confusion with hyphenated compounds, “|” is used to mark word boundaries in compounds
  • 3 Although there are a number of real compounds in Italian, such as fruttivendolo, as well.

6In compounding, two or several words are combined to form a new word. In Sámi, Finnic and Germanic languages, compounding is a productive process and new compounds like in can be made on the fly.2 In Romance languages, these compounds typically correspond to prepositional constructions (ital. ‘la federa del cuscino del divano’).3

(1) soffá|guoddá|olggoža (North Sámi)[sami]
sofa|pute|trekk (Norwegian)
‘sofa pillow cover (English)’

7The initial motivation for extensive lexicalization of compounds of North Sámi goes back to adapting the spellchecker to users’ needs, i.e. avoiding false alarms in Ávvir newspaper’s texts.

8North Sámi is a Uralic language spoken in Norway, Sweden and Finland by approximately 25 700 speakers (Simons and Fennig 2018). It is a synthetic language, where the open parts of speech (PoS) – nouns, adjectives, etc. – inflect for case, person and number. The grammatical categories are expressed by a combination of suffixes and stem-internal processes affecting root vowels and consonants alike, making it perhaps the most fusional of all Uralic languages. In addition to compounding, inflection and derivation are common morphological processes in North Sámi.

9North Sámi has seven morpho-syntactic cases, i.e. nominative (Nom.), genitive (Gen.), accusative (Acc.), illative (Ill.), locative (Loc.), comitative (Com.), and essive (Ess.). Case plays a more central role in Sámi than in preposition-based case languages, since here syntactic functions are identified based on case only. In addition, nouns can bear possessive suffixes. Verbs are inflected for person, number (singular, dual, plural), tense (present and past tense) and mood (indicative, conditional, and potential). Derivational processes (passive, causative, inchoative, diminutive, reflexive, to name only some of them) enhance the combinatory possibilities of each verb.

  • 4 The following abbreviations are used: N=noun, V=verb, A=adjective, Attr=attributive, Adv=adverb, Pr (...)

10Table 1 illustrates that compounding in North Sámi is by no means restricted to noun noun combinations, but includes a number of other parts-of-speech (PoS) as well, also as heads.4

Table 1: Compound types according to PoS; ‘|’ is used to mark word boundaries

Type

Example

Gloss and translation

N N

láhka|rievdadusat

law|change.pl ‘law changes’

A.Attr N

boahtte|áigi

coming|time ‘future’

Adv N

dáppe|olmmoš

here|person ‘person from here’

Pron A

iešguđet|lágan

each|alike ‘different kinds of’

Pron N

eanet|lohku

more|number ‘majority’

Adv Pcle

dušše|fal

only|really ‘just’

Adv V

vuostái|váldojuvvo

against|take.pass.3sg ‘received’

PrfPrc N

mearridan|fápmu

decide.prfprc|power ‘authority’

Num Num

okta|nuppe|lohkái

one|second|ten.ill ‘eleven’

Num N

1978|-láhka

1978|-law ‘1978 law’

Num A

3|-ivnnat

3|-colored ‘3-colored’

Num A

golmma|ivnnat

three|colored ‘three colored’

11In North Sámi, compounds are formed without a hyphen, except for those involving a proper noun, a digit, or an acronym like Davvi-Norgii ‘Northern Norway (Ill.)’, 3-juvllatsykkel ‘tricycle’, and ILO-álgoálbmotsoahpamuš ‘ILO-indigenous people agreement’ (Riektačállinrávvagat 2015, 46). There are a number of multiwords where a space is obligatory (albma ládje ‘properly’ and duollet dálle ‘sometimes’). Also genitive first compounds have an alternative interpretation when written apart, which makes error detection more difficult.

2. Background

12The North Sámi tools described in this article – NDS, Korp for SIKOR and GramDivvun (Wiechetek, n.d.) – all rely on the GiellaLT infrastructure (Moshagen, Pirinen, and Trosterud 2013), a technological framework for managing lexical data and building it into language technology applications including e-dictionaries and grammar checkers. All of them make use of a morphological analyzer, an FST (Finite-State Transducer) described in Pirinen , where word formation processes are moduled. Additionally, SIKOR and GramDivvun include a Constraint Grammar-based syntactic analysis. The full modular structure of the latter is described in Wiechetek .

  • 5 The table is based on the dictionary size at the time of the writing (September 2020); it is active (...)

13The computational modeling of the language is done using finite-state morphology (Beesley and Karttunen 2003). The method of recognizing grammatical words as well as querying their grammatical information is based on looking up the words in an FST that contains the morphological dictionary of the language. There are two types of compounds in the language model: the ones that are stored in the lexicon as lexicalized units and the ones generated dynamically using a compounding model. Table 2 gives the statistics over the length of lexicalized compounds.5

14Lexicalized four-element compounds are quite common in the noun lexicon, e.g. davvisámegielterminologiija ‘North Sámi language terminology’. Even six-element compounds (sáivačáhceguollevuostáiváldindilli ‘fresh water fish receive situation’) can be found.

15The different types of North Sámi compounds in Table 1 are not treated equally in the morphological analyzer. Only the compounds in the first two lines can be derived dynamically. All others need to be lexicalized, i.e. listed in the lexicon, to receive a compound analysis. Numeral compounding is not treated dynamically in the FST. The dynamic compounds are generated from the dictionary by concatenating word forms (such as a genitive or nominative noun followed by other noun) and adding a compound tag +Cmp. The main dynamic compounds are (derived and non-derived) noun + noun pairs. One feature of the underlying technology is that the compounding mechanism is capable of modeling infinitely long compounds: for example nouns of any magnitude are compounds and modeled by the finite-state automaton. Since the compounding mechanism of an FST is very powerful, it also leads to ambiguity. When we allow arbitrary lexemes to combine to form compounds, some will overlap other existing lexemes, cf. ex. (2).

(2) Davvi regiuvdna[regivudna]
North region;direction.oven
‘The northern region’

16Here, regiuvdna ‘region’ has a typical spelling error, o>u. The FST analyzes it as a misspelling of regiovdna ‘region’, but also as a compound with the elements regi, a common wrong form of regiija ‘direction’, and uvdna ‘oven’. While this example has only two possible analyses, twenty or more different analyses are not uncommon.

Table 2 : Lexical compounds in the lexicon by the PoS of their head and the number of their roots

Roots

2

3

4

5

6+

Pos

N

16 603

1 048

1 665

86

15

Num

408

1 048

42

0

4

Prop

11 680

3 005

115

9

1

A

3 854

333

13

0

0

V

478

4

0

0

9

Adv

896

109

1

0

0

Adp

152

49

0

0

0

Conj

3

0

0

0

0

3. Compounds in three NLP applications

17We present three applications, an e-dictionary, a corpus tool, and a grammar checker tool.

3.1 An e-dictionary (NDS)

18The North Sámi – Norwegian dictionary contains 25 000 lemmata and uses an FST. The e-dictionary was first implemented in 2013 with no use of relational databases (all linguistic resources are contained within static files and external command-line tools) (Ryan Johnson 2013). It is an intelligent dictionary in the sense that is able to look up North Sámi word forms and find lemmas via the FST. It also allows a tolerant mode, which accepts the letters acdnstz for áčđŋšt-ž in addition to their usual values. The e-dictionary can split compounds to provide the user with its elements as well as the whole compound if a translation is available. The lexicalization of compounds is important since the translation of the compound cannot necessarily be derived from the translation of its parts (Antonsen 2018, 54).

19In the FST 90% of the 100 000 nouns, and in the dictionary 75% of the 25 000 nouns are compounds.

3.2 A corpus tool

20The web application and corpus search tool Korp (Borin, Forsberg, and Roxendal 2012) does not show the internal structure of compounds in SIKOR. Neither lexicalized, nor dynamic compounds are searchable as either the lexicalized analysis is picked instead of the dynamic one or – in the case of compounds that are not listed in the lexicon – a lexicalized compound is made by the preprocessor. This is a problem inherent in the implementation of the tool. However, when searching for the compound tag used in the FST (+Cmp), there are 94 658 results. The reason for that is that the first element in split compounds in coordination receives a specific compound tag (+Cmp/SplitR) as well.

21Table 3 shows the statistics for compounds in SIKOR.6 The results are obtained using the scripts that can be found in GiellaLT.7 According to our analyses 8.6% of the tokens in corpus are compounds, and 86% are lexicalized. The rest is mainly composed of 2-elements compounds (13.4%) and a very small part of 4-7 elements (0.5%).

22Many of the longer compounds in SIKOR are quite creative and are hyphenated as the one in ex. (3).

(3) suoidne-varra-bleahkka-mála-bihkka-senet-dielku mu báiddis lei dušše lihkohisvuohta.[productivecompoundsikor]
hay-blood-ink-paint-tar-mustard-stain my shirt.loc was only mishap
‘The hay-blood-ink-paint-tar-mustard-stain on my shirt was only a mishap.’

Table 3: Compound types in SIKOR by the PoS of their head and the number of their root (amounts given in percentage

Roots

2

3

4

5

6/7

Pos

N

96.2

98.9

89.2

80

66.7

Prop

3.8

1.1

10.8

20

33.3

23The current public version of the Sámi corpus SIKOR (SIKOR 2018) (in Korp) consists of 32.2 million words. It was analyzed with a preprocessor that does not distinguish between lexicalized and dynamic compounds. The (non-public) version of SIKOR used in this article makes this distinction, though, as will future versions in Korp.

24A search for compound tags only returns split compounds, i.e. the first coordinated hyphenated nominal element, cf. in ex. , i.e. riddo- ‘coast-’.

(4) riddo- ja vuotnaguovlluin[split]
coast- and fjordregion.loc.pl
‘in coastal and fjord regions’

25GiellaLT has already produced a solution, i.e. a tag for cohorts with a dynamic compound (<with-dynamic-compound>) added by a Constraint Grammar module. However, this tag does not provide any information about the number of elements and the beginning and ending of each element.

3.3 A grammar checker (GramDivvun)

26GramDivvun, the North Sámi grammar checker (Wiechetek et al. 2019) takes input from the FST to a number of other modules, the core of which are several Constraint Grammar modules. Constraint Grammar is a rule-based formalism for writing disambiguation and syntactic annotation grammars (Karlsson 1990; Karlsson et al. 1995). In our work, we use the free open source implementation VISLCG-3 (Bick and Didriksen 2015). All components are compiled and built using the GiellaLT infrastructure (Moshagen, Pirinen, and Trosterud 2013).

27Lexicalization of compounds is relevant for grammar checking within compound error detection. One common error that cannot be resolved by a spellchecker is the spelling of compounds as two or more words. GramDivvun performs this type of error detection as part of the tokenization. The tokenization is done in two steps. In the first step potential compounds are tokenized ambiguously (either as one or as two words, the first of which is accompanied by an errortag). In the second step, a Constraint Grammar module8 selects or removes the error reading. Two conditions need to be met to find the compound error: 1. the compound needs to be lexicalized, and 2. the syntactic context needs to support the compound reading.

28The syntactic context is specified in hand-written Constraint Grammar rules. The REMOVE-rule below removes the compound error reading (identified by the tag Err/SpaceCmp) if the head is a 3rd person singular verb (cf. l.2) and the first element of the potential compound is a noun in nominative case (cf. l.3). The context condition further specifies that there should be a finite verb (VFIN) somewhere in the sentence (cf. l.4) for the rule to apply.

REMOVE (Err/SpaceCmp)
(0/0 (V Sg3))
(0/1 (N Sg Nom))
(*0 VFIN);

29All possible compounds written apart are considered to be errors by default, unless the lexicon specifies a two or several word compound or a syntactic rule removes the error reading. There are numerous syntactic contexts where the potential parts of compounds make perfectly sense. In the case of noun-noun compounds, the second element can for example be a simple adverbial, as in ex. (5). The second element can be homonymous with another PoS, it can be a finite verb or an infinitive.

(5) son lea boarráseamus mánná joavkkus.[manna]
s/he is oldest child group.loc
‘s/he is the oldest child in the group.’

4. Evaluation

30We evaluate the e-dictionary (coverage) and the grammar checker (precision, recall) for compounding (errors). The corpus search tool does not exhibit compounding information and is therefore not evaluated.

4.1 An e-dictionary (NDS)

31We analyzed the logs for NDS (Neahttadigisánit) for 2019, and found that 12.6% of the types in the user queries are compounds. The results are obtained using the scripts that can be found in GiellaLT. The amount of lexicalized compounds in the logs (72.1%) is approximately the same as in the dictionary, where it is 75% (cf. Section 3.1 above). As much as 98% of the compound queries get a translation, either a lexicalized one or of its parts. Thus dynamic compounding contributes with a substantial improvement to dictionary coverage. If the alternatives are “getting no help from the dictionary” and “getting help to translate the parts” then the latter is to be preferred, even though the correct translation would be different from just joining the parts. For example, the compound word ruhtahearrá ‘rich man’ is not lexicalized in NDS but it does get a translation of its parts ruhta ‘money’ and hearrá ‘man’, which can help the user to understand the meaning of the compound word itself.

32Most of the non lexicalized compounds are composed of 2 elements (96% in the logs and 93% in the entries). When analyzing the entries in the dictionary, we found that 24.8% are compounds and of those 97.6% are lexicalized. Table 4 shows PoS for compounds in NDS logs and entries.

Table 4: Compounds according to the number of their parts and PoS in NDS logs and entries (L=lexicalized)

Logs

Entries

Roots

L

2

3

4

L

2

3

4

Pos

N

90

87

85

100

86

87

82

0

A

3

0

0

0

2

0

0

0

Prop

3

0

0

0

12

4

0

0

V

2

13

14

0

0

8

18

0

Adv

1

0

0

0

0

0

0

0

4.2 A grammar checker (GramDivvun)

33We evaluate error detection for syntactic compound errors (i.e. words that are written apart and should be a compound) in GramDivvun in two ways. Firstly, we compare last year’s results in Wiechetek  with a newer version of GramDivvun, from now on referred to as the Nodalida-corpus. Last year’s results are based on version r183544 (Wiechetek, Brubeck Unhammer, and Nørstebø Moshagen 2019)9. The new results are based on version r2851010 of GramDivvun.

34However, as the focus in the last analysis was a different one, i.e. we evaluated other error types as well, we ran a second evaluation on a 2 363 word-corpus11 specifically made to test compound error detection, i.e. every sentence contains a potential compound. These sentences are hand-selected from SIKOR.

35The results of the evaluation are presented in Table 5. We can see that precision has gone significantly up, i.e. the average precision is 95.5%. However, the recall has gone down to average 46%. We are investigating the reasons for that. But in general, a high precision is desirable in grammar checking, even at the cost of a lower recall.

36The results of the evaluation of GramDivvun compound grammar checking are shown in Table 5.

Table 5: Measures for GramDivvun (TP/FP= true/false positives, FN=false negatives)

Measure

(2019)

(2020)

Nodalida Corpus

Compound

corpus

Precision

75.0%

93.1%

98.0%

Recall

72.9%

43.2%

48.5%

F1-Score

73.9

59.0

64.9

TP

51

54

50

FP

17

4

1

FN

19

67

53

37False negatives are typically due to the lack of lexicalization. Many of those are proper noun combinations which are very productive, e.g. Murmánska-aviisa ‘Murmansk newspaper’, Várggát-festiválas ‘at the Várggát festival’, km-galba ‘km sign’ and Divttasvuotna-regiovnna ‘Divttasvuotna region’.

38Other reasons are certain (unlikely) analyses of especially the first element, e.g. that generally suggest a syntactic construction rather than a compound as in ex. (6). Here the first element duorastat ‘Thursday’ has a finite verb reading as well.

(6) dán duorastat veaiggi.[duorastat]
this.gen Thursday twilight.gen
‘this Thursday evening’

39The false positive is due to an error in the recognition of the span of the target. In ex. (7), lulli sámi guvlui is concatenated, but it should only be lulli sámi.

(7) dohko lulli sámi guvlui.[lullisami]
thither South Sámi area.ill
‘thither towards the South Sámi area.’

5. Conclusion

40We have shown that the lexicalization of compounds – in addition to their dynamic treatment – is useful and necessary for two language applications for North Sámi, an e-dictionary (NDS) and a grammar checker (GramDivvun). The evaluation of NDS shows that we get a good coverage: 98% of the compounds logged do get a translation and 72% are lexicalized in the FST. The evaluation of GramDivvun has shown that we manage to identify compound errors with a precision of 98% and a recall of 49% utilising a combination of information from the lexicon and syntax.

41We conclude that there are perfectly good reasons for lexicalizing compounds, i.e. providing idiomatic translations for when it cannot be derived from the parts, and to support compound grammar checking. At the same time, lexicalization can dissimulate word formation information in corpus tools. This can be resolved and we have already implemented a solution in Constraint Grammar to make the information available in a future version of the corpus tool. As dynamic compounding is limited to few PoS at the moment, in the future we want to investigate and model compounding of other PoS (in the FST). Also experiments with neural network approaches and a comparison of the results to our rule-based grammar checker could be an interesting future project.

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Notes

1 Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

2 To avoid confusion with hyphenated compounds, “|” is used to mark word boundaries in compounds

3 Although there are a number of real compounds in Italian, such as fruttivendolo, as well.

4 The following abbreviations are used: N=noun, V=verb, A=adjective, Attr=attributive, Adv=adverb, Pron=pronoun, Pcle=particle, PrfPrc=past participle, Num=numeral, Prop=propernoun.

5 The table is based on the dictionary size at the time of the writing (September 2020); it is actively developed daily. Further abbreviations are Adp=adposition, Conj=conjunction.

6 The search was done on 2020-09-07.

7 [footnote_scripts]https://github.com/giellalt/conf-clicit2021

8 https://github.com/giellalt/lang-sme/blob/3a43911929458fd39da309ed23178bf5dbd04bcd/tools/tokenisers/mwe-dis.cg3

9 https://github.com/giellalt/lang-sme/releases/tag/nodalida-2018 on 2019-09-26

10 https://github.com/giellalt/lang-sme/releases/tag/clicit on 2020-09-07

11 http://gtsvn.uit.no/freecorpus/orig/sme/odda_mahppa/compounds.correct.txt

Auteurs

Divvun & Giellatekno, UiT Norgga árktalaš universitehta – linda.wiechetek@uit.no

Divvun & Giellatekno, UiT Norgga árktalaš universitehta – chiara.argese@uit.no

Divvun & Giellatekno, UiT Norgga árktalaš universitehta – tommi.pirinen@uit.no

Divvun & Giellatekno, UiT Norgga árktalaš universitehta – trond.trosterud@uit.no

© Accademia University Press, 2020

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