WAFFLE : A Graph for W ord N et Applied to F ree F orm Linguistic Exploration (2020)

Abstract: The WordNet database of English (Fellbaum, 1998) is a key source of semantic information for research and development of natural language processing applications. As the sophistication of these applications increases with the use of large datasets, deep learning, and graph-based methods, so should the use of WordNet. To this end, we introduce WAFFLE: WordNet Applied to FreeForm Linguistic Exploration which makes WordNet available in an open source graph data structure. The WAFFLE graph relies on platform agnostic formats for robust interrogation and flexibility. Where existing implementations of WordNet offer dictionary-like lookup, single degree neighborhood operations, and path based similarity-scoring, the WAFFLE graph makes all nodes (semantic relation sets) and relationships queryable at scale, enabling local and global analysis of all relationships without the need for custom code. We demonstrate WAFFLE’s ease of use, visualization capabilities, and scalable efficiency with common queries, operations, and interactions. WAFFLE is available at github.com/TRSS-NLP/WAFFLE.

## introduction wordnet @xcite is a database of english words with associated lexical properties and semantic relations. for example, wordnet includes seven semantically distinct senses for the noun "establishment": figure 1 is based on noun hypernym and hyponym relations, but wordnet includes additional parts of speech @xcite and associated relations -e.g. entailment between verbs, antonyms between adjectives, and derivationally related forms for all parts-of-speech. wordnet has been used for building dictionary and thesaurus applications as well as a range of natural language processing tasks such as: word sense disambiguation tasks @xcite @xcite , document retrieval @xcite , information extraction @xcite , and querying @xcite for recommender @xcite and question-answer (tapeh and rahgozar, 2008) systems. the current version of wordnet (117,000 synsets in version 3.1 with 27 relation types) is available through an interactive browser, apis, and stand alone database files which can be customized. 1 however, beyond recreating common functionality, we believe there is an increasing need for the availability of wordnet in an open source graph-based data structure to support large-scale use and research (e.g. for deep learning (yuan et al., 2016; diao et al., 2018; vial et al., 2019; kobylinski and wasiluk, 2019), hierarchical embeddings (bernardy and maskharashvili, 2019), and graphbased approaches generally (naskre ¸t et al., 2018; pinter and eisenstein, 2018). these use cases leverage not only the content of wordnet, but need to do so with increasing sensitivity to the structure of wordnet. this is not only to operate more efficiently, but to open up additional potential avenues of research. to satisfy this need, we present waf-fle: wordnet applied to freeform linguistic exploration as a fully-connected queryable graph representation of wordnet to provide: (1) flexibility in exploring all of wordnet's relations across synsets and hierarchies rather than particular partof-speech-based subgraphs; (2) scalable processing for large datasets; and (3) support for all common operations on wordnet (look-up, similarity measures). the remainder of this paper is structured as follows: section 2 introduces the details of waf-fle's graph structure, computation and descriptive statistics. section 3 demonstrates common word-net operations compared to non-graph structure 1 http://wordnetweb.princeton.edu/perl/ webwn approaches. section 4 discusses related methods of wordnet access. section 5 concludes with waf-fle's access and licensing details with plans for future versions. ## features, strengths, and comparisons while novel in structure, waffle parallels but does not present itself as a replacement to existing representations and forms of access to word-net. for comparison, we present several canonical operations performed on wordnet through its waffle-processed form and as accessed through nltk. we break common functionality associated with wordnet into: (1) information retrieval; (2) synset relationship-finding; (3) computation of semantic "distance"; and (4) visualization. for each category, we present examples and syntax in both waffle and the nltk wordnet wn library. foot_7 ## related work there are many ways of accessing the content wordnet beyond the the database files which allows for a tremendous amount of choice in developing against wordnet. 12 further, nltk and comparable packages from wordnet::similarity (pedersen @xcite and spacy, to name a few, provide comprehensive approaches to exploring not only the content, but also the structure of wordnet. 13 however, compared to waffle, these existing offerings facilitate one-off to moderately-scalable investigations; given more to exploratory research and processing rather than use with large data sets, sophisticated applications, or classes of problems that need to leverage the structural elements of wordnet. some existing offerings are more oriented to graph-based structures. for example, the global wordnet association does produce formats (json, xml, rdf) that could be easily translated into graph data structures but introduce a number of additional relationships that reflect ongoing research and linkage to multi-lingual wordnets. 14 similarly, framenet @xcite is graph-based and accessible with nltk, but provides more semantic and syntactic connections within the context of frame semantics with no direct link to wordnet. conceptnet @xcite is graph-based as well with a closer relationship to wordnet with explicit external linking, but is focused on a much broader range of information for natural language understanding, common sense reasoning, crowd sourced knowledge, etc. future connections to these offerings will be explored, but, as is introduce a number of additional complexities that waffle seeks to avoid. ## availability and future work we have presented waffle, an open source graph data structure that relies upon platform-agnostic formats to facilitate robust interrogation and flexibility when using wordnet in research or applications. waffle's software, example load scripts, and the associated figures and graph files are available at github.com/trss-nlp/waffle. while we encourage users to capitalize on the advantages afforded by the design and transformations presented in section 2 to implement the word-net data in entirely new ways, we envisage several avenues of augmentation: (1) linking waffle to corpora to perform more sophisticated path measures using information content @xcite @xcite and associated word embeddings; (2) connecting additional word-net information such as morphosemantic links, log-ical forms and other semantic annotations (existing as "standoff" files; and (3) multi-lingual connections through with open multilingual wordnet and potentially others referenced in section 4. 15