Expanding the Conceptual Description of Verbs in W ord N et with Semantic and Syntactic Information (2023)

Abstract: This paper describes an ongoing effort towards expanding the semantic and conceptual description of verbs in WordNet by combining information from two other resources, FrameNet and VerbNet, as well as enriching the verbs’ description with syntactic patterns extracted from the three resources. The conceptual description of verb synsets is provided by assigning a FrameNet frame which provides the relevant set of frame elements denoting the predicate’s participants and props. This information is supplemented by assigning a VerbNet class and the set of semantic roles associated with it. The information extracted from FrameNet and VerbNet and assigned to a synset is aligned (semi-automatically with subsequent manual corrections) at the following levels: (i) FrameNet frame: VerbNet class; (ii) FrameNet frame elements: VerbNet semantic roles; (iii) FrameNet semantic types and restrictions: VerbNet selectional restrictions. We then link the syntactic patterns associated with the units in FrameNet, VerbNet and WordNet, by unifying their representation and by matching the corresponding patterns at the level of syntactic groups. The alignment of the semantic components and their syntactic realisations is essential for the better exploitation of the abundance of information across resources, including shedding light on cross-resource similarities, discrepancies and inconsistencies. The syntactic patterns can facilitate the extraction of examples illustrating the use of verb synset literals in corpora and their semantic characterisation through the association of the syntactic groups with the components of semantic description (frame elements or semantic roles) and can be employed in various tasks requiring semantic and syntactic description. The resource is publicly available to the community. The components of the conceptual description are visualised showing the links to the original resources each component is drawn from.

## introduction the paper focuses on describing an effort at obtaining a rich semantic and syntactic description of verbs in wordnet through mapping other lexical and conceptual resources to it (framenet and verb-net, in particular). this has been achieved through aligning corresponding elements of the semantic and syntactic description of the entities in these resources. we rely on existing alignments between the resources -part of the verbs in wordnet have been assigned a framenet frame and/or a verbnet class on the basis of equivalent or similar meaning. after the basic units of the resources have been aligned, we implement procedures for mapping their constituent parts: frame elements with semantic roles, syntactic groups with syntactic groups or syntactic positions. this type of alignment makes it possible to study the commonalities and differences in and possibly to perfect the representation of verbs across resources and languages, on the one hand, and to obtain a richer and more reliable description for the purposes of tasks in computational linguistics, on the other. ## related work in recent years, significant efforts have been invested in harnessing the strengths of lexical, conceptual and syntactically-oriented resources through mapping them on various levels. such efforts include works on mapping wordnet, framenet and verbnet (the earliest attempt probably being made by @xcite ) or different combinations of these resources resulting in combined resources, such as wordframenet foot_0 by @xcite and mapnet foot_1 by @xcite , other other framenet-to-wordnet mappings, such as the one by @xcite . the further enhancement of these resources with others has resulted in the emergence of semlink foot_2 @xcite , which unifies word-net, framenet and verbnet with propbank, and semlink+ that brings in a mapping to ontonotes @xcite . efforts such as the synsem-class lexicon 4 centre not on any of the discussed resources, but on a different one (in this case the vallex dictionary family), which is further enriched with conceptual and syntactic information from external semantic resources @xcite , including linking to framenet, wordnet, verbnet, ontonotes and propbank, as well as the czech vallex. as the alignment between resources is limited by the overlap between the lexis covered by them, a major effort has been to expand the coverage of the mapping across resources by way of generalisation and transfer of existing descriptions from already described items @xcite to other units that share the same semantic and syntactic properties. verbatlas foot_4 , proposed @xcite has adopted a representation of synsets as clusters with prototypical argument structures presented as frames (to a large extent inspired by verbnet roles and semantic restrictions). the clustering leads to a significant expansion encompassing the entire verb inventory (13,767 synsets). another approach, adopted by @xcite and further refined in @xcite , involves the mapping of framenet frames to wordnet synsets on the basis of the inheritance of conceptual features in hypernym trees, i.e., by assigning frames from hypernyms to hyponyms where possible and implementing a number of validation procedures based on the structural properties of the two resources, primarily the relations encoded in them. this has resulted in 13,104 automatic alignments, of which 6,000 have been validated and corrected manually in the framework of this project and previous initiatives. another venue of research has been to map relevant information representing fragments of meaning associated with lexical units across resources, especially essential components of the semantic and the syntactic description such as semantic roles or their counterparts in the respective resources (e.g. frame elements, argument positions, valency slots). alignments at the verb arguments' level have been carried out as part of the semlink project and its more recent version semlink 2.0. @xcite . the alignments described there include propbank to verbnet mappings (propbank roleset -verbnet senses, pb arguments -verbnet semantic roles) as well as verbnet to framenet mappings (verbnet senses -framenet frames, verbnet semantic roles -framenet frame elements). another similar task, which makes use of the linking of various semantic resources (framenet, wordnet, verbnet, ontonotes and propbank), has been implemented in the development of the synsemclass lexicon @xcite : the more general synsemclass valency slots have been mapped to relevant framenet frame elements. it has long been discussed that combining word-net and other lexical and conceptual resources such as framenet produces a more complete semantic and syntactic representation of the meaning lexical entries @xcite which expands the possible application of the resources for the purpose of syntactic and semantic parsing. our current effort builds on our previous work described in @xcite and further refined in @xcite 6 , and proceeds onwards. our interests lie in both: (i) expanding the alignment between the most lexically populated resource, wordnet, the rich conceptual apparatus and the more generalised argumentstructure descriptions of framenet and verbnet, respectively, and the syntactic descriptions available in the three resources; (ii) mapping the basic building blocks across resources, where possible, i.e. frame elements and semantic roles and respectively -their syntactic expressions. in this paper we particularly focus on the latter: extending the description of wordnet verbs by mapping semantic and conceptual components of the description extracted from the three resources employed in the study, and supplementing it with syntactic patterns by combining and aligning the available syntactic information. the proposed enhancements are directed to: (a) improving the existing mappings by aligning framenet frames and verbnet verb classes assigned to the same synset; (b) enhancing the conceptual description of synsets with additional infor-mation about the syntactic realisation of framenet frame elements and verbnet semantic roles; and (c) suggesting further procedures for verification and improvements of conceptual descriptions of verb synsets in wordnet. ## lexical and conceptual resources below we describe in brief the used resources and how they are integrated with each other. ## corpus resources the semantically annotated corpus semcor (current version 3.0) @xcite is compiled by the princeton wordnet team and covers texts excerpted from the brown corpus. semcor is supplied with pos and grammatical tagging and all open-class words (both single words and multiword expressions, as well as named entities) are semantically annotated by assigning each word a unique wordnet sense (synset id). bulsemcor @xcite has been generally modelled on the semcor methodology and structure. while only open-class words are annotated with wordnet senses in semcor, all lexical units in bulsemcor have been annotated; for that purpose the bulgarian wordnet has been expanded with closed-class words @xcite . we use semcor and bulsemcor to extract usage examples for the syntactic patterns in which literals in the corresponding synsets appear in corpora. the extracted examples in english are analysed with a view to the differences in the syntactic patterns applicable to different literals. examples from bulsemcor serve the purpose to provide material for the investigation of the possible syntactic knowledge transfer from english to bulgarian. ## compilation of syntactic and semantic description of verbs in wordnet after aligning framenet frames to verbnet classes (assigned to synsets or groups of synsets), and framenet frame elements to verbnet roles, we move towards mapping syntactic patterns from the resources to the end of providing a new, syntactic layer to the conceptual description of the verbs in wordnet. in order to make the alignment between the patterns obtained from verbnet and framenet as precise as possible, we perform this procedure at the literal level and then transferred onto the the framenet frame and verbnet class pair, i.e. for each literal in a synset which is mapped to a lexical unit in framenet and an entry in verbnet, the corresponding patterns from the two resources are aligned according to several criteria. these include: the syntactic pattern alignment procedure is implemented as a set of mapping rules. as a result of their application, we obtain a list of the equivalent syntactic models for a given framenet frame and verbnet class pair (examples 5, 6 and 7). where no correspondence is discovered, the table cell is marked as none. example 5. aligned syntactic patterns for the wordnet synset eng-30-00001740-v breathe; take a breath; respire; suspire, framenet frame breathing and the verbnet class breathe-40.1.2. (fn syntactic patterns with frequency of 3+ are labelled by a *.) example 5 shows the alignment of the syntactic patterns between the frame breathing and the class breathe-40.1.2 following the mapping between the frame elements and semantic roles (agent -agent, air -theme, source -source, goal-destination). misalignment occurs in the cases of additional semantic roles that are not considered core fes (e.g., path, manner, etc.) which have no correspondence to verbnet roles and participants in wordnet basic sentence frames. example 6. aligned syntactic patterns for the framenet frame killing and the verbnet class murder-42.1 for the synset eng-30-01323958-v kill 'cause to die; put to death, usually intentionally or knowingly'. example 7. aligned syntactic patterns for the framenet frame killing and the verbnet class suffocate-40.7 (e.g., asphyxiate, choke, suffocate, etc.). examples 6 and 7 show different degrees of mis-alignment between the syntactic patterns of the corresponding frames and verb classes. the frame killing allows for the instrument to appear as an external argument np which matches a syntactic pattern within the verb class murder-42.1 but not the verb class suffocate-40.7. further, while the verbs evoking the frame killing incorporate the result (the death of the patient / victim), the verb class suffocate-40.7 also allows for a different result as shown in the last row of the table in example 7 (e.g., suffocate to/into unconsciousness). further, in order to increase the number of mapped frames we generalise some unmapped framenet frames by excluding optional or unexpressed arguments, thus reducing the pattern to a more basic form. the asymmetries in the syntactic patterns covered by matched framenet frames and verbnet classes for particular wordnet synsets are indicative of the need for more detailed syntactic analysis and the study of both the alignment between frame elements and semantic roles and their syntactic realisation. example 8 shows sentences featuring the literals from a given synset which are extracted from semcor. corpus examples: we learned this year that our older son, daniel, is autistic. have you ever heard of thuggee? we had merely been discovered by the pool sharks. we want to find_out who knew about it. williams is learning the difficulties of diplomacy rapidly. i was anxious to hear about those dazzling days on the great_white way. what obsessions had she picked_up during these long nights of talk? as illustrated by the examples: (a) some literals appear more frequently in the data while others do not appear at all (e.g., get wind) and for the latter we cannot draw any conclusions; (b) some literals have a restricted number of patterns applicable to them (e.g., multiword expressions such as get word cannot have a phenomenon as a direct object) or accept particular lexical entries (e.g., prepositions hear of but *pick_up of ). ## results the processing of the data included the following key procedures: the verbnet classes represented in the dataset include 32 unique semantic roles which are matched to a total of 217 framenet frame elements. the synsets in the dataset cover 29 (out of the 35) generalised wordnet sentence frames. these are aligned to 451 verbnet syntactic patterns and 13,884 framenet syntactic patterns. the greater number of framenet syntactic realisations is due to: (a) the large number of peripheral and extrathematic frame elements 12 and the variety of configurations they enter in the different realisations; and (b) the representations of alternations and variations (e.g., passives, incorporation of fes, various prepositions in pps, etc.). the framenet patterns have been filtered based on frequency (of examples exhibiting the pattern included in the framenet dataset), which has resulted in 811 framenet syntactic patterns with frequency of 3 or more. the dataset is supplemented with a set of 16,059 corpus examples illustrating the annotated synsets (on average, 14 examples per synset). additionally, we have also included the usage examples provided in all of the resources -wordnet examples (which are often not full sentences but phrases) and framenet and verbnet illustrative examples. the newly developed resource containing pairs of a framenet frame and a verbnet class with their corresponding syntactic patterns for realisation of fes and semantic roles is distributed under a cc by 4.0 license 13 . ## conclusions in this paper we present a dataset of wordnet synsets supplied with extensive semantic, conceptual and syntactic information obtained by combining (i) wordnet's description and semantic relations with (ii) the conceptual information from the relevant framenet frame (including the frame elements and the specific semantic restrictions) and verbnet class assigned to the synsets and (iii) the syntactic patterns compiled from all the three resources and aligned both in terms of the syntactic realisation and the frame element or semantic role of each component. the combination of semantic and syntactic information is seen as a possible way to transfer knowledge across languages (e.g., from english to bulgarian) by relying on the universality of semantic description. various annotated corpora will be further used in studying the syntactic properties of verbs to the end of: enhancing their applicability to nlp tasks such as semantic role labelling, word sense disambiguation, etc. another promising venue of research is related to facilitating the more precise identification of the participants in the situations described by verbs, thus enabling better information extraction, text recognition and generation, question answering, machine translation.