Enriching Multiword Terms in W iktionary with Pronunciation Information (2023)

Abstract: We report on work in progress dealing with the automated generation of pronunciation information for English multiword terms (MWTs) in Wiktionary, combining information available for their single components. We describe the issues we were encountering, the building of an evaluation dataset, and our teaming with the DBnary resource maintainer. Our approach shows potential for automatically adding morphosyntactic and semantic information to the components of such MWTs.

## introduction in this paper, we describe our approach to enrich english multiword terms (mwts) included in wiktionary by generating pronunciation information using the existing pronunciation(s) of their sub-parts. results of our work can also be integrated in other lexical resources, like the open english wordnet (mccrae et al., 2020), foot_0 where pronunciation information has been added only for single word entries, as described in (declerck et al., 2020a). the main focus of our work is on generating pronunciation information for mwts that contain (at least) one heteronym foot_1 , as for this a specific processing of the wiktionary data is needed, disambiguating between the different senses of the heteronym for selecting the appropriate pronunciation of this one component to be attached to the overall pronunciation. an example of such a case is given by the wiktionary entry "acoustic bass", for which our algorithm has to specify that the pronunciation /beis/ (and not /baes/) has to be selected and combined with /@"ku:.stik/. it is important to mention that although wiktionary often lists several pronunciations for various variants of english, in this work we focus only on the standard, received pronunciation as encoded by the international phonetic alphabet (ipa) foot_2 (more about this in the limitations section). since we need to semantically disambiguate one or more components of a mwt for generating its pronunciation, our work can lead to the addition of morphosyntactic and semantic information of those components and thus enrich the overall representation of the mwts entries, a task we have started to work on. ## wiktionary wiktionary 4 is a freely available web-based multilingual dictionary. like other wikimedia 5 supported initiatives, it is a collaborative project. this means that there might be inaccuracies in the resource, but the editing system is helping in mitigating this risk. the fact that wiktionary is build by a collaborative effort means that the coverage and variety of lexical information is much larger than any single curated resource, while wiktionary is integrating information from expert-based dictionary resources, when their licensing conditions allow it. nastase and strapparava (2015) discussed already the quality (and quantity) of information included in the english wiktionary edition, also in comparison with wordnet. @xcite wiktionary includes, among others, a thesaurus, a rhyme guide, phrase books, language statistics and extensive appendices. wiktionary's information also (partly) includes etymologies, pronunciations, sample quotations, synonyms, antonyms and translations. @xcite wiktionary has also developed categorization practices which classify an entry along the lines of linguistics (for example "developed terms by language") but also topical information (for example "en:percoid fish"). @xcite it has been shown that the access and use of wiktionary can be helpful in natural language processing (nlp). kirov et al. (2016) and mc-carthy et al. (2020), for example, describe work to extract and standardize the data in wiktionary and to make it available for a range of nlp applications, while the authors focus on extracting and normalizing a huge number of inflectional paradigms across a large selection of languages. this effort contributed to the creation of the uni-morph data ( http://unimorph.org/ ). metheniti and neumann (2018, 2020) describe a related approach, but making use of a combination of the html pages and the underlying xml dump of the english edition of wiktionary, 9 which is covering also 4,315 other languages, but some of them with a very low number of entries. @xcite describe the use of wiktionary data as a resource for word sense disambiguation tasks. babelnet 11 is also integrating wiktionary data, @xcite with a focus on sense information, in order 6 see (fellbaum, 1998) and http://wordnetweb. princeton.edu/perl/webwn for the on-line version of princeton wordnet. the categorization system is described at https://en.wiktionary.org/wiki/ wiktionary:categorization 9 wiktionary data dumps are available at https://dumps. wikimedia.org/ . the result of our work could be relevant for babel-net, as the audio files displayed by babelnet are not based on the reading of pronunciation alphabets but on external text-to-speech systems, which are leading to errors, as can be seen in the case of the heteronym "lead", for which babelnet offers only one pronunciation. 13 ## multiword terms in wiktionary wiktionary introduces the category "english multiword terms" (mwts), which is defined as "lemmas that are an idiomatic combination of multiple words," @xcite while wiktionary has its page "multiword expression", categorized as a mwts and defined as "lexeme-like unit made up of a sequence of two or more words that has properties that are not predictable from the properties of the individual words or their normal mode of combination". @xcite we see these two definitions are interchangeable, since they both focus on the aspect of non-compositionality of a lexeme built from multiple words. we will therefore use in this paper the terms mwe and mwt interchangeably, but stressing that we are dealing with mwes as they are categorized as mwts in wiktionary. ## related work wiktionary is often used as a source for various text-to-speech or speech-to-text models, as described in our previous work (bajčetić and declerck, 2022). for instance, the work of schlippe et al. (2010) developed a system which automatically extracts phonetic notations in ipa from wiktionary to use for automatic speech recognition. a more recent example is the work by peters et al. (2017) which is aimed at improving grapheme-to-phoneme conversion by utilizing english edition of wiktionary, including all the languages covered by this edition. @xcite see the audio file associated with the two different senses of the entry for "lead": https://babelnet.org/ synset?@xmath0://babelnet.org/synset?@xmath1. wiktionary. grapheme-to-phoneme is necessary for text-to-speech and automatic speech recognition systems. besides text-to-speech, there are various other applications which rely on extracting pronunciation information from wiktionary. a recent tool is wikipron (lee et al., 2020), which is an opensource command-line tool for extracting pronunciation data from wiktionary. it stores the extracted word/pronunciation pairs in tsv format. @xcite we observe that no wiktionary multiword terms are included in those lists. also, no (semantic) disambiguation is provided and, for example, the word "lead" is listed twice, with the different pronunciations, but with no sense information, as wikipron is providing solely word/pronunciation pairs. results of our work consisting in generating pronunciation information to multiword terms could thus be included to wikipron directly or via wiktionary updates. in the other direction, wikipron could be re-used for our purposes, as it harmonizes phonemic pronunciation data across various wiktionary language editions, while the pronunciations are segmented, and stress and syllable boundary markers removed. especially the latter is relevant for our work, as it will ease future evaluation work (see the issues described in section 6). another related effort, and a very relevant resource for our approach, is dbnary. @xcite dbnary extracts different types of information from wiktionary (covering 23 languages) and represents it in a structured format, which is compliant to the guidelines of the linguistic linked open data framework. @xcite in the dbnary representation of wiktionary we find lexical entries (including words, mwes or affixes, but without marking those explicitly, an issue that has been fixed in new release of dbnary, as this is requested for continuing our approach in the context of dbnary), their pronunciation (if available in wiktionary), their sense(s) (definitions in wiktionary), example sentences and dbnary glosses, which are offering a kind of "topic" for the (disambiguated) entries, but those glosses are not extracted from the category system of wiktionary. they are taken from available information used to denote the lexical sense of the source of the translation of an entry from english to other languages. dbnary does not include categorial information from wiktionary, and also did not offer support for dealing with mwts lacking pronunciation information and that contain (at least) one heteronym. therefore, we still need(ed) to access and consult wiktionary directly, using methods that are described in section 5, also for building the gold standard for evaluating our work (mwts in wiktionary that are carrying pronunciation information). hence, our results can also be integrated in dbnary, directly or via the updated wiktionary entries. in fact, our work lead to the adaptation of dbnary, as this is briefly described in section 5.3 ## method we describe in this section the various approaches we implemented and tested, leading finally to a closer cooperation with the maintainer of dbnary, as it became apparent that the release of a new version of this resource is the most efficient way for achieving and widening our goals. ## an initial evaluation study in order to evaluate the newly created pronunciations, we use those mwts which already carry pronunciation information in wiktionary. in a first "naive" approach, we just compared the result of combining the extracted pronunciation information from the components of the mwts with those mwts which are equipped with pronunciation in wiktionary. this simple string matching lead to poor results, as it might have been expected. one of the reasons being that in some cases the pronunciation information included in the mwt is containing either space(s), suprasegmental information, or other markers. the combination of pronunciation information extracted from the components do not contain those additional information (at least not in the same way). another issue we were confronted with, lies in the fact that in many cases, wiktionary is listing more than one pronunciation information for a single word. our algorithm needs to be tuned in order to select only the one pronunciation information that is included in the corresponding mwt. some editing of the evaluation set is also needed, towards the creation of an evaluation set that is containing no suprasegmental pronunciation information (and other markers) or spaces. a first analysis of such a cleaned evaluation data set showed already an improved computation of recall and precision. we plan to use for this also the data set generated by the wikipron initiative (see the description in section 4. principle as explained in the paper, with one extra layer of variant matching. another limitation of our work lies in the fact that wiktionary is ever-changing. so anything done at one point in time needs to be re-done in the future due to changes in the data and also newly added data. the fact that wiktionary grows quite fast means that the best approach would be incremental or recursive in some way, and automatically check for newly added pronunciations which can create new mwes pronunciations, while also confirming that the previously created ones have not been altered and need updating. this is a reason why we teamed with the makers of dbnary for this, as dbnary is updated twice a month.