CaMEL : C ase M arker E xtraction without L abels (2022)

Abstract: We introduce CaMEL ( Ca se M arker E xtraction without L abels), a novel and challenging task in computational morphology that is especially relevant for low-resource languages. We propose a first model for CaMEL that uses a massively multilingual corpus to extract case markers in 83 languages based only on a noun phrase chunker and an alignment system. To evaluate CaMEL, we automatically construct a silver standard from UniMorph. The case markers extracted by our model can be used to detect and visualise similarities and differences between the case systems of different languages as well as to annotate fine-grained deep cases in languages in which they are not overtly marked.

## introduction what is a case? linguistic scholarship has shown that there is an intimate relationship between morphological case marking on the one hand and semantic content on the other (see @xcite and @xcite for overviews). for example, the latin case marker -ibus foot_0 (ablative or dative plural) can express the semantic category of location. it has been observed that there is a small number of such semantic categories frequently found crosslinguistically @xcite , which are variously called case roles or deep cases. semiotically, the described situation is complicated by the fact that the relationship between case markers and expressed semantic categories is seldom isomorphic, i.e., there is both case polysemy (one case, several meanings) and case homonymy or case syncretism (several cases, one marker) @xcite . as illustrated in figure 1 , the latin ablative marker -ibus can express the semantic the latin case marker -ibus is used for both the ablative (abl) and the dative (dat), which in turn express the three semantic categories of instrument (i), location (l), and recipient (r). this is an example of both case polysemy (one case: abl, several meanings: i and l) and case syncretism (several cases: abl and dat, one marker: -ibus). russian, on the other hand, has an isomorphic relationship between instrumental (inst), locative (loc), and dative (dat), the case markers corresponding to them (-ами/-ami, -ах/-ax, -ам/-am), and the expressed semantic categories (i, l, r). the key idea of this paper is to detect such complex correspondences between case markers and expressed semantic categories in an automated way. specifically, we build on prior work by @xcite , who lays the theoretical foundation for our study by showing that deep cases can be induced from cross-linguistic usage patterns of case markers. as opposed to latin, russian has separate cases (with separate case markers) for the semantic categories of instrument (-ами/-ami), location (-ах/-ax), and recipient (-ам/-am). thus, knowing the russian case marker corresponding to latin -ibus reduces the uncertainty about the expressed case role (figure 1 ). this reduction of uncertainty can be particularly helpful in a low-resource setting where other means of analysis are unavailable. in this work, we rely on the parallel bible corpus (pbc; mayer and cysouw, 2014), a massively multilingual corpus, to investigate the relationship between surface cases and their deep meanings cross-linguistically. to put our idea into practice, we require an exhaustive set of case markers as well as a set of parallel noun phrases (nps) that we can further analyze with respect to deep cases using the set of case markers. both requirements pose a serious challenge for languages with limited available resources. we therefore introduce camel (case marker extraction without labels), a novel and challenging task of finding case markers using only (i) a highly parallel corpus covering many languages, (ii) a noun phrase chunker for english, and (iii) word-level pre-computed alignments across languages. our work uses the parallel nature of the data in two ways. first, we leverage the word-level alignments for the initial step of our pipeline, i.e., the marking of nps in all languages (even where no noun phrase chunker is available). to do so, we mark nps in 23 different english versions of the bible and project these annotations from each english to each non-english version using the word-level alignments, resulting in parallel nps that express the same semantic content across 83 languages. based on the projected annotations, we leverage the frequencies of potential case markers inside and outside of nps as a filter to distinguish case markers from lexical morphemes and other grammatical morphemes typically found outside of nps. second, we leverage the alignments for a finegrained analysis of the semantic correspondences between case systems of different languages. we make three main contributions. to foster future research on camel, we make the silver standard, our code, and the extracted case markers publicly available foot_1 . ## related work unsupervised morphology induction has long been a topic of central interest in natural language processing @xcite @xcite @xcite . recently, unsupervised inflectional paradigm learning has attracted particular interest in the research community @xcite , reflected also by a shared task devoted to the issue @xcite . our work markedly differs from this line of work in that we are operating on the level of case markers, not full paradigms, and in that we are inducing morphological structure in a massively multilingual setting. there also have been studies on extracting grammatical information from text by using dependency parsers @xcite and automatically glossing text @xcite as well as compiling full morphological paradigms from it @xcite . by contrast, our method is independent of such annotation schemata, and it is also simpler as it does not aim at generating full grammatical or morphological descriptions of the languages examined. there has been cross-lingual work in computational morphology before @xcite @xcite , but not with the objective of inducing inflectional case markers. methodologically, our work is most closely related to the superpivot model presented by asgari and schütze (2017), who investigate the typology of tense in 1,000 languages from the parallel bible corpus (pbc; mayer and cysouw, 2014) by projecting tense information from languages that overtly mark it to languages that do not. based on this, @xcite perform a typological analysis of tense systems in which they use different combinations of tense markers to further divide a single tense in any given language. our work differs in a number of important ways. first, we do not manually select a feature to investigate but model all features in our chosen sphere of interest (i.e., case) at once. furthermore, we have access to word-level rather than verse-level alignments and can thus make statements at a more detailed resolution (i.e., about individual nps). finally, we extract features not only for a small selection of pivot languages, but even for languages that do not mark case "non-overtly", i.e., in a way that deviates to a large degree from a simple 1-1 mapping (see discussion in §1). ## linguistic background there is ongoing discussion in linguistic typology about the extent to which syntactic categories are shared and can be compared between the world's languages (see @xcite for an overview). while this issue is far from being settled, there is a general consensus that (while not being a language universal) there is a core of semantic categories that are systematically found cross-linguistically, and that are expressed as morphosyntactic case in many languages. here, we adopt this assumption without any theoretical commitment, drawing upon a minimal set of deep cases detailed in going beyond deep cases, @xcite envisages a more fine-grained analysis of what is conventionally clustered in a deep case or semantic role. briefly summarised, the theoretical concept is this: if every language has a slightly different case system, with enough languages it should be possible to divide and cluster nps at any desired level of granularity, from the conventional case system down to a specific usage of a particular verb in conjunction with only a small set of nouns. for example, the semantic category of location could be further subdivided into specific types of spatial relationships such as 'within', 'over' and 'under'. taken together, it would then be possible to perform theory-agnostic typological analysis of case-like systems across truly divergent and lowresource languages by simply describing any language's case system in terms of its clustering of very fine-grained semantic roles into larger systems that are overtly marked. the approach sketched in the last paragraph is not limited to case systems but has been applied to person marking @xcite , the causative/inchoative alternation @xcite , and motion verbs @xcite . the variety of linguistic application areas highlights the potential of developing methods that are much more automated than the work of cysouw and collaborators. while we stay at the level of traditional deep cases in this paper, we hope to be able to extend our method into the direction of a more general analysis tool in the future. the remainder of the paper is structured as follows. section 4 describes our method in detail. section 5 gives an overview of our results. finally, section 6 presents two exploratory analyses. ## evaluation of retrieved case markers we evaluate our method for case marker extraction without labels using a silver standard. ## exploratory analyses we can use our automatically extracted case markers, in combination with the parallel nps that are extracted as part of the pipeline, for innovative linguistic analyses. we present two examples in this section. ## conclusion and future work we have introduced the new and challenging task of case marker extraction without labels (camel) and presented a simple and efficient method that leverages cross-lingual alignments and achieves an f1 of 45% on 19 languages. we introduce an automatically created silver standard to conduct our evaluation. we have further demonstrated two ways in which our retrieved case markers can be used for linguistic analysis. we see two potential avenues for future work. the first is the further improvement of case marker extraction. the main problem to tackle here is that of small sets of overlapping substrings of which only one is the correct marker, and developing some further measures by which they can be distinguished. furthermore, it would be useful to find data from more low-resource languages and languages that have typological properties different from the extensively studied large language families (indo-european, turkic, sino-tibetan etc.). we could then verify that our method performs well across languages and attempt to expand our silver standard to more languages while still ensuring quality. the second area is that of further automating the analysis of deep case and case syncretism. ideally, we would develop a method that can distinguish the different possible reasons for divergent case marking in languages, with the eventual goal of creating a comprehensive overview of case and declension systems for a large number of languages.