Compressing Noun Phrases to Discover Mental Constructions in Corpora – A Case Study for Auxiliaries in H ungarian (2024)

Abstract: The quantitative turn in functional linguistics has emphasised the importance of data-oriented methods in describing linguistic patterns. However, there are significant differences between constructions and the examples they cover, which need to be properly formalised. For example, noun chains introduce significant variation in the examples, making it difficult to identify underlying patterns. The compression of noun chains into their minimal form (e.g. as they appear in abstract constructions) is a promising method for revealing linguistic patterns in corpora through their examples. This method, combined with identifying the appropriate level of abstraction for the additional elements present, allows for the systematic extraction of good construction candidates. A pilot has been developed for Hungarian infinitive structures, but is adaptable for various linguistic structures and other agglutinative languages.

## introduction functional construction grammars @xcite have recognised that human language consists of a network of symbolic form-meaning pairs @xcite , which are influenced by frequency of use @xcite . as a result, several methods have been developed and applied to support empirical exploration, using corpus-based and corpus-driven methods to identify linguistic patterns by considering frequency data, rather than relying on introspection, such as collocation metrics and collostructural analysis @xcite @xcite . the trend, which has led much of functional linguistic research to adopt a data-driven and quantitative approach, is referred to as the quantitative turn @xcite . in linguistic description, collecting datasets of sufficient quantity and quality, and processing them in an unbiased manner has become a clear challenge for linguists. however, without a well-structured technological apparatus, intuition distorts the objectivity of the query and the analysis used to identify patterns. the ability to validate patterns derived from theory using corpus-driven methods has, therefore, become a pressing issue. it is not sufficient for the results precisely match the theory, each step and the chain of reasoning must be examined to correct intuition. extracting constructions from corpora requires the processing of a large number of individual examples. we have found that the combinatorial explosion mainly arises from noun phrases that vary greatly in length and internal structure. however, the constructions we are looking for are not primarily concerned with the noun phrases they contain and, therefore, do not define their form precisely. to simplify entries, we compressed the noun phrases into their minimal form, which allowed us to extract shorter, more schematic patterns that better aligned with our theoretical expectations. to test our hypothesis, we developed a rulebased method for compressing noun chains using practically pos tags, which we applied to the example clauses. the remaining elements in the constructions are replaced by either a word form, lemma or pos tag, depending on the expectations and their statistical variations, to achieve the best coverage. in this paper, we focus on the noun phrase compression method in light of the constructions found. ## data sources we used two corpora for the measurements, allowing us to compare and validate our method against overfitting. the first corpus, the hungarian gigaword corpus 2.0.5 (hgc2) @xcite , with 1.04 billion words, contains texts from six stylistic and five regional varieties. the second corpus, the hungarian webcorpus 2.0 ## the mosaic method the mosaic n-gram method can model linguistic data at different levels of abstraction, such as word, lemma, and pos tag, simultaneously @xcite . this concept aligns with the usage-based approach, as linguistic schemas become entrenched at various levels of sematicity, and patterns are conventionalised based on their frequency of use @xcite . the mosaic n-gram method efficiently generates and ranks all possible abstractions of linguistic data, thereby reducing the reliance on linguistic intuition to identify statistically sound construction candidates that have the appropriate level of abstraction for each element to maximise coverage. the method includes a classification step to iden-tify inferior mosaic n-grams (i.e. subset relation): any less frequent mosaic n-gram that generalises from a set of examples that are a subset of those covered by a more frequent mosaic n-gram. conversely, the more frequent n-gram is labelled as major compared to the less frequent one if the latter only covers examples that are also covered by the former. among entries of equal frequency covering the same set of examples, all but the least abstract are deleted as redundant. in addition, by setting a frequency threshold to discard entries that are rare despite their abstraction, the processing time and the number of entries to be manually checked can be further reduced. this approach allows for a high degree of customisation, and the generated mosaic n-gram patterns can be easily converted into a query expression (e.g. the cql in sketch engine @xcite to check them in the corpus and explore linguistic data matching the pattern). in an abstraction of the method to additionally handle free word order n-grams are substituted for bag of words @xcite . ## compressing noun phrases the formal definition of constructions typically includes only the bare minimum of noun phrases, as the focus is on the whole structure. when clauses that may contain elements at various levels of abstraction are automatically compared (e.g. element by element), it becomes difficult to match patterns of different lengths without introducing additional measures. however, noun phrases with modifiers can separate functionally and structurally similar samples (see the first three rows of on the other hand, sequences of the same length with different modifiers in the noun phrases present a different issue. the last three rows of we chose a rule-based approach because, in hungarian, apart from a few well-separated cases, noun phrases can be trivially compressed using word order, morphology, and pos tags with simple regular expressions, and we can retain the property of converting the resulting patterns to cql. a challenging case is possessives, where there are two ways of expressing the genitive function, both of which are homonymous. the first is marked by the nominative case, followed immediately by its property (e.g. a [/det][art|def] kutya [/n][nom] háza [/n][poss.3sg][nom] 'the dog's house'). the second is expressed with the dative case (e.g. a [/det][art|def] kutyának [/n][nom] a [/det][art|def] háza [/n][poss.3sg][nom] 'the house of the dog'), which allows for flexible word order and even interruption (e.g. a kutyának [/n][dat] lefestette [/v][pst.def.3sg] a [/det][art|def] házát [/n][poss.3sg][acc] 'he/she painted the house of the dog'). while nominative homonymy is easy to handle because of its word order (ligeti-nagy et al., 2019) (see the last row in the procedure could be trivially refined and applied similarly to other agglutinative languages (e.g. uralic languages). however, such rule-based transformations rely heavily on morphological markedness, and the results depend on both the language analysis tool and the quality of the corpus used (e.g. the amount of noise present). ## evaluation we evaluated the results in two ways. first, we examined the changes in the distribution of frequencies and the total number of resulting entries. next, we analysed how the most frequent and general patterns are reflected in the usage patterns found, which enables further analysis. ## conclusion and future work we have presented the steps foot_0 for reducing examples in the corpus to a form a form that is nearly identi-cal to theoretical constructions in a corpus-driven manner through a case study. the semi-automatic clause extraction and feature reduction are covered by @xcite , from which we used the samples, while the selection of the optimal level of abstraction is obtained automatically with mosaic methods @xcite . this work focuses on the precise compression of the noun phrases (cf. np chunking), which halved the number of the remaining candidates, yielding manually comparable results that still need further evaluation and reduction. the method is currently under development, so the latter steps are not yet in their final form. the limitations of the compression include the handling of interrupted possessive structures. however, we have shown that their small number is more of a usage pattern than an oddity, opening up new directions for research. free argument order poses another challenge, which we plan to address by using bag of words instead of n-grams after the compression, allowing for the classification of otherwise similar neutral and focused structures. the idiomatic structures identified (e.g. entries with specific elements kept) are not included in this evaluation and should be examined separately. we have used rule-based components (cf. llms) to maintain maximum control over the workflow and to be able to examine and validate the chain of reasoning at each step in order to develop a more correct intuition from which theories can benefit. for example, the need for a rigorous formalisation of the constructions sought and the development of tools to achieve such reduction of examples. this approach has revealed unexpected behaviours (e.g. the absence of interrupted possessive structures) that would otherwise remain hidden. if studied further, they could provide more insight into the reasoning of the language users through their usage patterns and would shape theoretical thinking. on the other hand, the empirical validation of the revealed theoretical results based on human cognition is essential to support the cognitive aspect of the approach. therefore, in the next step of the research we plan to validate the identified patterns with cloze tests in the form of a language game (indig and lévai, 2023)), as this approach also supports the testing of llms for the same task. the proposed steps are loosely language and task dependent as they can be easily adapted to other languages and phenomena. they are tested on hungarian, so they are particularly suitable for other uralic and morphologically rich languages. dávid márk nemeskey. 2020. natural language processing methods for language modeling. ph.d. thesis, eötvös loránd university. attila novák, borbála siklósi, and csaba oravecz. 2016. a new integrated open-source morphological analyzer for hungarian. in proceedings of the tenth international conference on language resources and evaluation (lrec'16), pages 1315-1322, portorož, slovenia. european language resources association (elra). csaba oravecz, tamás váradi, and bálint sass. 2014. the hungarian gigaword corpus. in proceedings of the ninth international conference on language resources and evaluation (lrec'14), pages 1719-1723, reykjavik, iceland. european language resources association (elra). an van linden. 2010. from premodal to modal meaning: adjectival pathways in english. cognitive linguistics, 21:537-571.