Graph-based multi-layer querying in Parseme Corpora (2023)

Abstract: We present a graph-based tool which can be used to explore Verbal Multi-Word Expression (VMWE) annotated in the Parseme project. The tool can be used for linguistic exploration on the data, for helping the manual annotation process and to search for errors or inconsistencies in the annotations.

## introduction the parseme project @xcite proposes a large set of annotated data with verbal multi-word expressions (vmwe). in version 1.2 @xcite , 14 languages were covered but with older versions and ongoing work foot_0 , there are now data in 26 languages (see parseme data is published with associated morpho-syntactic annotations, in accordance with the universal dependencies @xcite framework. some parseme annotations are directly built on data available in the ud project. in this case, we have both high-quality morphosyntactic annotations and vmwe annotations on the same data. other parts of the parseme data, which are not built on existing ud data, are accompanied by an automatic morpho-syntactic annotation, obtained with udpipe @xcite , thus also following the ud annotation framework. this means that all annotated data from the parseme project can be considered as multi-layer annotated data, with morphosyntactic annotations encoded following ud and vmwe. in this article, we propose an encoding of the two annotation layers in a common structure, using a graph encoding of both ud and vmwe annotations. with this encoding, it is possible to use graph-based tools to work with the data. in this work, we use our grew tool @xcite to make queries on the two layers. the parseme 1.3 data will be released on http://hdl.handle.net/11372/lrt-5124 . at the time of the final version of the paper, these data are not available. the experiments reported in the paper are done on a preliminary version of the data provided by the parseme team. we cannot exclude minor differences between the data we used in our observations and the official 1.3 data. in section 2, we explain the encoding. the next sections give examples of usage with general observations in section 3, applications to error mining in section 4 and some more comprehensive study of the consistency between ud and parseme annotation layers in section 5. ## graph encoding the two annotation layers (ud and vmwe) are stored in a common technical format (cupt) foot_1 , but it is not straightforward to consider both in the same structure. in ud, each sentence is split in a sequence of tokens and each parseme annotation consists in identifying a subset of the tokens of the sentence which correspond to a vmwe. in addition to the subset, a tag is given to each vmwe. the parseme guidelines 3 describes the set of tags and their definitions. the tagset contains three universal tags: lvc.full, lvc.cause for light verb constructions and vid for verbal idioms. three quasi-universal categories are also defined: vpc.full, vpc.semi for verb-particle constructions and mvc for multi-verb constructions. a few other tags are used in the corpora: the iav for inherently adpositional verbs, presented as experimental in the parseme guidelines and the tag ls.icv for inherently clitic verbs (currently only used in italian data). some development versions of the data makes also use of the special tag notmwe which, as its name indicates, does not encode a vmwe, it is used in the consistency checking mechanism 4 . there are two difficulties in the encoding. in order to take into account theses difficulties, we propose to encode the two layers in a single graph structure. our graphs contain two kinds of nodes and two kinds of edges: note that parseme nodes are not really inserted in the linear structure of the sentence. by convention, these nodes are drawn before the first token of the subset, just to ease the reading of the figures. ## multi-layer queries the benefit of having the two annotation layers in the same structure is that it is possible to make queries which refer to both layers and then to make cross observations. we use the grew tool which allows to write graph queries that can be executed on the parseme corpora represented has above. the tool is available in an online web interface: grew-match 6 on a predefined set of treebanks. a python library, named grewpy, is also available to use queries in scripts. we give a few examples of grew queries on the parseme graph encoding. ## error mining one of the common usage of grew and mainly grew-match is error mining. by looking at all examples of a given query, we can spot inconsistencies and potential annotation errors. a first example of error mining is to explore the occurrences of one-token vmwes (see subsection 3.2) which are unexpected and require manual inspection. let us see a few other examples. ## consistency ud/parseme in this section, we give a few examples of requests which can be used in grew-match to explore how some specific class of vwme is annotated in one treebank. ## conclusion we have shown in this paper that using a graph encoding to represent a multi-layer annotation in a common structure is useful and can be exploited for different purposes, like error mining or linguistic exploration of the data. this methodology opens new perspectives for corpora maintenance and is complementary to existing tools like the ud validation script foot_11 and the parseme consistency checking. using the same idea, it would be possible to encode other annotation layers, like the ones available in a corpus like the gum corpus foot_12 @xcite .