Towards the Addition of Pronunciation Information to Lexical Semantic Resources (2021)

Abstract: This paper describes ongoing work aiming at adding pronunciation information to lexical semantic resources, with a focus on open wordnets. Our goal is not only to add a new modality to those semantic networks, but also to mark heteronyms listed in them with the pronunciation information associated with their different meanings. This work could contribute in the longer term to the disambiguation of multi-modal resources, which are combining text and speech.

## introduction the work described in this paper aims at enriching lexical semantic databases by adding the modality of pronunciation, primarily targeting in our current work the open english wordnet (mccrae et al., 2019a, 2020). @xcite pronunciation information is typically not associated with wordnet, but can be particularly relevant within the vision of contributing directly or indirectly to integrated lexical resources and architectures, like the elexis dictionary matrix (mccrae et al., 2019b) or babelnet (navigli and ponzetto, 2010), as well as text-to-speech systems which use wordnet or wordnet-based lexical resources or tools. in a number of cases, homographs with different meanings are also characterised by different pronunciations. this can be the case across syntactic categories, but also within one category, like for example for the noun "lead", 2 which is having a different pronunciation per sense, as this is exem-1 see also https://github.com/ globalwordnet/english-wordnet . @xcite the two pronunciation and definition pairs for the noun "lead" displayed here are taken from the xml dump of the english edition of wiktionary. the human readable page can be consulted at https://en.wiktionary.org/ wiki/lead#noun . the frequency of heteronymy varies across different languages. for example, as for today, wiktionary counts 723 cases for english, foot_1 while only 21 cases are listed for french. @xcite but the number of concerned entries increases considerably if we take into account all the derived terms (including compounds and phrasal expressions) in which a heteronym entry is occurring. so, for the "metal" sense of the "lead" entry, wiktionary is listing 77 derived terms, 32 of them being currently included as an entry in the dictionary. some of them are carrying pronunciation information ("leadsman"), and some are not ("lead pencil"). similarly, for the "curved" sense of "bow" wiktionary lists 19 derived terms, like for example "longbow", all included as an entry in the dictionary. some of them are also not carrying pronunciation information, like for example "bow harp". hence, a much larger number of wiktionary entries can be considered as instances of heteronymy, if one lexical item in a compound or in a phrasal entry is itself included in wiktionary as a heteronym. ## targeted lexical databases although our current work is primarily intended at enriching wordnet, ultimately we aim at adding disambiguated pronunciation information to a series of lexical databases. once the phonetic transcriptions are correctly stored in wordnet, this information can be propagated to babelnet (navigli and ponzetto, 2012) foot_3 and all other lexical resources which are making use of wordnet. ## our approach the first step of our work consisted in accessing the xml dump of the english wiktionary resource, foot_7 and extracting from there, with the help of customised python scripts, the pronunciation information associated with nouns, verbs, adjectives, and adverbs. as we can see in figure 1 , we also extracted the corresponding senses and associated examples sentences, as we need to keep the relation of the pronunciation information with the corresponding meaning and the associated example sentences, if any is provided. while we can report good progress in this task, there are still a few issues to solve, mainly due to the sometimes idiosyncratic way of encoding information in wiktionary. while the overall xml structures of the lexical entries in wiktionary is quite consistent, the linguistic information itself is encoded by making use of the wiki mark-up language and with a number of options left to the (volunteering) encoders of the entries, so that extra lines of codes are necessary for dealing with those recurrent idiosyncratic cases. still, we have extracted a large amount of lexical information that we have checked for validity. the numbers are given and discussed in the next section. ## formal lexical representation in order to make the information we extracted from wiktionary available in an interoperable and reusable format, we make use of the ontolex-lemon model, resulting from the w3c community group "ontology lexica" (cimiano et al., 2016). @xcite figure 2 displays the general organisation of the core module of the ontolex-lemon model. ## sense linking in the following phase of our work, we plan to connect the extracted information with the correct wordnet synsets. after extracting the pronunciation information from wiktionary, the subsequent step of our work lies in sense disambiguation and linking. more specifically, this task requires correctly inferring which of the heteronym synsets is the right match for the pronunciation information we have extracted from the wiktionary entry. in order to disambiguate the word sense, we can utilize the wordnet synsets of the heteronymous senses as well as their description and examples from wiktionary. our initial approach relies on comparing the document similarity between wordnet synsets and the matching wiktionary entries. firstly, we create 'documents' by concatenating the definitions and examples for all the senses of the ambiguous word. in the case of "lead", we have decided to combine all the possible sub-senses using their pos tag. in this way, according to wordnet, we end up with two broader senses for "lead": a broad noun sense and a broad verb sense. these two documents need to be compared with the two documents extracted from wiktionary, using the same approach. after tokenization, punctuation cleaning and stopwords removal, document similarity is calculated using tfidf and the bag-of-words approach. for this purpose we have utilized the docsim library from gensim @xcite . the preliminary work shows promising results. in ## related work our work with the english wiktionary is an extension and a refinement of a first experiment dealing with the german version of wiktionary, with the aim of enriching a new open wordnet for german with pronunciation information (declerck et al., 2020). in both cases, we make use of the ontolex-lemon community standard for encoding the heteronyms (and other entries). our current development is aiming at including the results into various integrated or interlinked lexical databases. we are also aiming at automatically adding pronunciation information to derived terms, on the base of senselinking algorithms. the work presented in (declerck, 2020) describes an approach for linking the open dutch wordnet to external lexical resources, including the dutch version of wiktionary, with the goal of enriching the lemmas in the wordnet entries with morphological variants. but the work was not dealing with pronunciation information. (schlippe et al., 2010) assess the quality of pronunciation information in wiktionary for four languages (english, french, german, and spanish) and come to satisfying results, especially in the case of french, when it comes to the evaluation of the coverage and also to the impact on automatic speech recognition (asr) systems, especially in the case of spanish. this already older study comforted us in the opinion that extracting pronunciation information from wiktionary can deliver a relevant source of data for our experiment consisting in equipping wordnets with pronunciation information. in recent years, relevant research regarding heteronyms is done in the field of speech synthesis. for example, the work of (samsudin and rahim, 2019) focuses on handling heteronym ambiguity for a text-to-speech (tts) system for malay language. although there are only 12 unique heteronyms in malay, this research emphasises the importance of conducting a specific study on heteronym words and their pronunciation by tts systems. other important work in this field includes the patents of (henton and naik, 2014) and (wang et al., 2011). both models focus on heteronym pronunciation for dialogue systems, using the user's input to correctly predict the pronunciation of the output heteronym. ## future work a crucial phase of the future work involves evaluation. for this we could use some existing dictionaries which contain pronunciation information. since pronunciation is an inevitable part of translation dictionaries, extracting the information from such sources could substantially enlarge the underlying resource and also serve as a basis for evaluation. one interesting possibility for an expansion of the scope of this work can be found in compound words and derived terms. after correctly disambiguating the heteronymous lemma, we can use this information to produce the ipa of the compound words which contain it. this would be done following the rules of metrical phonology (kreidler, 2004). if that would prove too complex we could produce the ipa without stress information. we could also use etymology information from wiktionary to produce pronunciation descriptions for compounds.