F rame N et-like Annotation of Olfactory Information in Texts (2021)

Abstract: Although olfactory references play a crucial role in our cultural memory, only few works in NLP have tried to capture them from a computational perspective. Currently, the main challenge is not much the development of technological components for olfactory information extraction, given recent advances in semantic processing and natural language understanding, but rather the lack of a theoretical framework to capture this information from a linguistic point of view, as a preliminary step towards the development of automated systems. Therefore, in this work we present the annotation guidelines, developed with the help of history scholars and domain experts, aimed at capturing all the relevant elements involved in olfactory situations or events described in texts. These guidelines have been inspired by FrameNet annotation, but underwent some adaptations, which are detailed in this paper. Furthermore, we present a case study concerning the annotation of olfactory situations in English historical travel writings describing trips to Italy. An analysis of the most frequent role fillers show that olfactory descriptions pertain to some typical domains such as religion, food, nature, ancient past, poor sanitation, all supporting the creation of a stereotypical imagery related to Italy. On the other hand, positive feelings triggered by smells are prevalent, and contribute to framing travels to Italy as an exciting experience involving all senses.

## introduction in the humanities and social sciences, the senses, which have traditionally received little attention from researchers, are now very high on the academic agenda @xcite . in fact, in the last two decades, scholarly attention has shifted away from the visual and textual to the embodied and multi-sensory, following a so-called 'sensorial revolution' @xcite . in the field of history, for example, recent works have focused on describing the meaning of odours in particular places and times @xcite , or on the role played by smells in signalling identity, community and otherness in the past -especially race @xcite . this turn, however, has received little attention from the nlp community, probably due to a number of inherent challenges related to the intangible nature of scents and odours. the few existing works in the field have mainly focused on building resources aimed at capturing and modelling sensory vocabularies @xcite and analysing how the different senses interfere from a lexical point of view @xcite . this lack of attention may be partly explained by the fact that western languages, which are prevalent in nlp studies, do not contain rich vocabularies for describing odorants as opposed to other senses @xcite . the only exception in this direction is the work in @xcite , where the authors present two semi-supervised approaches to identify smell experiences in english literature after annotating a gold standard of 700 sentences with smell-related information. in this work, we present a more comprehensive scheme for the annotation of olfactory information, which has been developed within the odeuropa h2020 project 1 . the goal of this effort is to capture smell events and situations in texts and manually label the main participants in the scene. our guidelines are inspired by frame semantics @xcite and the framenet annotation project @xcite , although we carry out some modifications both to the olfactory-related frames and to the annotation practice. this work will guide a subsequent annotation task, which will lead to the creation of a multi-domain multilingual benchmark of historical texts annotated with olfactory information. the guidelines and the benchmark represent also the backbone upon which an 1 https://odeuropa.eu/ automated system for olfactory information extraction will be built. as a preliminary study, we apply the annotation scheme to a set of english travel narratives describing trips to italy, and present the results obtained by analysing the most frequent role fillers and grouping them by domains. ## related work only very little research within the nlp community has dealt with sensory information extraction. most works have focused on the creation of structured resources to capture the sensory domain, automatically deriving them from wordnet @xcite . other works have dealt with the automated analysis of texts related to specific domains like wine reviews, where olfaction plays a central role @xcite . other studies have focused on synaesthetic aspects of language, starting from a controlled lexicon of perception @xcite . to our knowledge, only the work by @xcite proposes both a simple annotation scheme to capture smelly experiences and two semi-supervised approaches to automatically replicate this annotation. another line of research has addressed so-called urban smellscapes, i.e. how modern cities can be described from an olfactory point of view. more specifically, @xcite obtain descriptions of different urban areas by asking annotators to walk around cities and take note of the smell characterising different places. such descriptions are then combined with social media posts about the same places, allowing the authors to build an olfactory representation of different cities and categorise urban smells into odour wheels. ## framenet and olfactory information our annotation of olfactory information is inspired by frame semantics @xcite , a theory which has been implemented through the framenet annotation project @xcite 2 , whose goal is to capture situations and events present in texts. in framenet, events and situations are so-called frames, which from a cognitive point of view, are defined as components of the internal model of the world that a language-user has created by interpreting his/her environment @xcite . frames are used as synonyms for schemata, semantic memory or scenarios, and represent the perceptual base of our knowledge that is 2 https://framenet.icsi.berkeley.edu necessary to understand the meaning of words. for example, the predicate 'marry' refers to a scenario where two partners get involved in some kind of social relationship. according to frame semantics, a frame includes two main components: lexical units (lus) and frame elements (fes). the former are words, multiwords or idiomatic expressions that evoke a specific frame, while the latter are frame-specific semantic roles that, in case of verbal lus, are usually realized by the syntactic dependents of the verb. for example, the commerce pay frame includes as lexical units 'pay', 'payment', 'disburse', 'disbursement', 'shell out', and has the following frame elements: buyer, goods, money, rate, seller. framenet aims at being a general-purpose resource, capturing all possible situations and events that may happen in real life. therefore, in the last framenet version, more than 1,200 frames are listed. since in our study we are only interested in olfactory situations, we narrow the scope of the annotation and we simplify the frame repository, while adopting the same structure as the original framenet based on lexical units and related frame elements. for example, in framenet the 'smell' lexical unit is present as a noun or verb in five frames: sensation, perception active, give impression, perception experience and chemical sense description. their distinctions are rather fine-grained, for example perception active and perception experience both capture perception events but in the former the perceptual experience is intentional, while in the second it can be unintentional. on the other hand, chemical sense description captures mainly descriptions of tastes and smells, while give impression includes characterisations and appraisals of smells and sensation includes nouns that refer to sensations in different modalities. all these distinctions are not needed in our framework, whose final goal is to build a methodology and techniques for olfactory information extraction. therefore, we only define a single frame, the olfactory event, where all relevant roles of the above frames are captured. we also create domain-specific semantic roles that are the outcome of discussions with experts in olfactory heritage and history. for example, we introduced the roles of smell source, evoked odorant and odour carrier, while we borrowed from framenet some generic roles such as perceiver, time, location and circumstances. for details, see section 4.2. also the list of lexical units (lus) is defined with the help of domain experts, choosing smell-related lexical units that evoke olfactory situations and events. our goal is to develop an annotation scheme that may be applied to different languages, therefore the lu lists are being created in seven languages, namely english, dutch, italian, french, german, slovenian, latin. they include both translations and synonyms of words such as 'to smell', 'odour' 'odorous', 'smelly', 'perfume' and language-specific terms. for example, german includes a list of compound nouns created with the roots '-gestank', '-geruch', '-duft' (e.g. regengeruch, viehgestank, lavendelduft). we report in with this annotation, we pursue several objectives: we want our guidelines to be generic enough to accomodate all possible smell-related events which may be mentioned in a text, and also to cover all languages, without the need to provide languagespecific adaptations from a semantic point of view. we also want our framework to be flexible enough to deal with meaning change of terms over time, enabling annotators to add new smell-related terms to our initial list during the annotation process. finally, we define semantic roles using labels that are self-explanatory and not ambiguous (e.g. evoked odorant, smell source), so to facilitate the role selection ensuring a good agreement among annotators. ## case study: olfactory annotation in travel writings in order to validate the proposed annotation guidelines, we test them on a set of smell-related sentences extracted from 51 books on travel narratives presented in @xcite and @xcite . the books include non-fictional narratives (reports, diaries, letters) and guidebooks about italy written by english native authors and published between 1860 and 1930 3 . this period was chosen because it followed italy unification and was characterised by the new phenomenon of leisure-oriented tourists' travels. indeed, after unification, new routes to southern italy and the islands were opened, so that travelers could reach less visited sites, beyond the classic destinations such as venice, florence and rome. we first extract from the corpus all sentences containing an english smell word, taken from the 3 https://sites.google.com/view/travelwritingsonitaly/ lu list for english. we try to exclude highly ambiguous words such as "essence" or "atmosphere". overall, we obtain 650 sentences, corresponding to olfactory events or situations. then, the sentences are annotated following the guidelines described in the previous sections. ## conclusions in this paper, we first present annotation guidelines to capture olfactory information in texts, and then describe a first use case aimed at analysing travel writings through the lens of smell descriptions. although this analysis is carried out on english texts, our guidelines have been designed to be languageindependent, and the preparation of lu lists covering different european languages is ongoing. as a next step, we plan to extend the annotation with emotion information, so to capture the effect or reactions triggered by smells. once a multilingual benchmark with manually annotated olfactory information is available, we will also investigate different approaches to perform the task automatically, exploring cross-language frame information transfer @xcite as well as semisupervised and few-shot classification. more broadly, analysing how olfactory experiences are described in texts can offer fresh perspectives for humanities research in this field. through the connection of different, multilingual sources, especially historical ones, we can promote scholarship in the field of sensory history, and engage audiences with more (nuanced) stories about their olfactory past. once information extraction systems trained on our annotated data are developed, we will be able to analyse extensive digital archives and trace how scent and smelling were instrumental in shaping communities in the past and how these bound and separated groups and nations.