The use of automatic methods for the study of lexical semantic change (LSC) has led to the creation of evaluation benchmarks. Benchmark datasets, however, are intimately tied to the corpus used for their creation questioning their reliability as well as the robustness of automatic methods. This contribution investigates these aspects showing the impact of unforeseen social and cultural dimensions. We also identify a set of additional issues (OCR quality, named entities) that impact the performance of the automatic methods, especially when used to discover LSC.
We analyze the effect of further retraining BERT with different domain specific data as an unsupervised domain adaptation strategy for event extraction. Portability of event extraction models is particularly challenging, with large performance drops affecting data on the same text genres (e.g., news). We present PROTEST-ER, a retrained BERT model for protest event extraction. PROTEST-ER outperforms a corresponding generic BERT on out-of-domain data of 8.1 points. Our best performing models reach 51.91-46.39 F1 across both domains.
With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic has been declared one of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. Addressing the issue requires solving a number of challenging problems such as identifying messages containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few. To address this gap, we release a large dataset of 16K manually annotated tweets for fine-grained disinformation analysis that (i) focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society, and (iii) covers Arabic, Bulgarian, Dutch, and English. Finally, we show strong evaluation results using pretrained Transformers, thus confirming the practical utility of the dataset in monolingual vs. multilingual, and single task vs. multitask settings.
Lexical normalization is the task of transforming an utterance into its standardized form. This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation. Such variation is typical for social media on which information is shared in a multitude of ways, including diverse languages and code-switching. Since the seminal work of Han and Baldwin (2011) a decade ago, lexical normalization has attracted attention in English and multiple other languages. However, there exists a lack of a common benchmark for comparison of systems across languages with a homogeneous data and evaluation setup. The MultiLexNorm shared task sets out to fill this gap. We provide the largest publicly available multilingual lexical normalization benchmark including 13 language variants. We propose a homogenized evaluation setup with both intrinsic and extrinsic evaluation. As extrinsic evaluation, we use dependency parsing and part-of-speech tagging with adapted evaluation metrics (a-LAS, a-UAS, and a-POS) to account for alignment discrepancies. The shared task hosted at W-NUT 2021 attracted 9 participants and 18 submissions. The results show that neural normalization systems outperform the previous state-of-the-art system by a large margin. Downstream parsing and part-of-speech tagging performance is positively affected but to varying degrees, with improvements of up to 1.72 a-LAS, 0.85 a-UAS, and 1.54 a-POS for the winning system.
This paper describes the TOKOFOU system, an ensemble model for misinformation detection tasks based on six different transformer-based pre-trained encoders, implemented in the context of the COVID-19 Infodemic Shared Task for English. We fine tune each model on each of the task’s questions and aggregate their prediction scores using a majority voting approach. TOKOFOU obtains an overall F1 score of 89.7%, ranking first.
We introduce 9 guiding principles to integrate Participatory Design (PD) methods in the development of Natural Language Processing (NLP) systems. The adoption of PD methods by NLP will help to alleviate issues concerning the development of more democratic, fairer, less-biased technologies to process natural language data. This short paper is the outcome of an ongoing dialogue between designers and NLP experts and adopts a non-standard format following previous work by Traum (2000); Bender (2013); Abzianidze and Bos (2019). Every section is a guiding principle. While principles 1–3 illustrate assumptions and methods that inform community-based PD practices, we used two fictional design scenarios (Encinas and Blythe, 2018), which build on top of situations familiar to the authors, to elicit the identification of the other 6. Principles 4–6 describes the impact of PD methods on the design of NLP systems, targeting two critical aspects: data collection & annotation, and the deployment & evaluation. Finally, principles 7–9 guide a new reflexivity of the NLP research with respect to its context, actors and participants, and aims. We hope this guide will offer inspiration and a road-map to develop a new generation of PD-inspired NLP.
We introduce HateBERT, a re-trained BERT model for abusive language detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit comments in English from communities banned for being offensive, abusive, or hateful that we have curated and made available to the public. We present the results of a detailed comparison between a general pre-trained language model and the retrained version on three English datasets for offensive, abusive language and hate speech detection tasks. In all datasets, HateBERT outperforms the corresponding general BERT model. We also discuss a battery of experiments comparing the portability of the fine-tuned models across the datasets, suggesting that portability is affected by compatibility of the annotated phenomena.
As socially unacceptable language become pervasive in social media platforms, the need for automatic content moderation become more pressing. This contribution introduces the Dutch Abusive Language Corpus (DALC v1.0), a new dataset with tweets manually an- notated for abusive language. The resource ad- dress a gap in language resources for Dutch and adopts a multi-layer annotation scheme modeling the explicitness and the target of the abusive messages. Baselines experiments on all annotation layers have been conducted, achieving a macro F1 score of 0.748 for binary classification of the explicitness layer and .489 for target classification.
The COVID-19 pandemic has witnessed the implementations of exceptional measures by governments across the world to counteract its impact. This work presents the initial results of an on-going project, EXCEPTIUS, aiming to automatically identify, classify and com- pare exceptional measures against COVID-19 across 32 countries in Europe. To this goal, we created a corpus of legal documents with sentence-level annotations of eight different classes of exceptional measures that are im- plemented across these countries. We evalu- ated multiple multi-label classifiers on a manu- ally annotated corpus at sentence level. The XLM-RoBERTa model achieves highest per- formance on this multilingual multi-label clas- sification task, with a macro-average F1 score of 59.8%.
We introduce an approach to multilingual Offensive Language Detection based on the mBERT transformer model. We download extra training data from Twitter in English, Danish, and Turkish, and use it to re-train the model. We then fine-tuned the model on the provided training data and, in some configurations, implement transfer learning approach exploiting the typological relatedness between English and Danish. Our systems obtained good results across the three languages (.9036 for EN, .7619 for DA, and .7789 for TR).
Datasets to train models for abusive language detection are at the same time necessary and still scarce. One the reasons for their limited availability is the cost of their creation. It is not only that manual annotation is expensive, it is also the case that the phenomenon is sparse, causing human annotators having to go through a large number of irrelevant examples in order to obtain some significant data. Strategies used until now to increase density of abusive language and obtain more meaningful data overall, include data filtering on the basis of pre-selected keywords and hate-rich sources of data. We suggest a recipe that at the same time can provide meaningful data with possibly higher density of abusive language and also reduce top-down biases imposed by corpus creators in the selection of the data to annotate. More specifically, we exploit the controversy channel on Reddit to obtain keywords that are used to filter a Twitter dataset. While the method needs further validation and refinement, our preliminary experiments show a higher density of abusive tweets in the filtered vs unfiltered dataset, and a more meaningful topic distribution after filtering.
The paper focuses on a large collection of Dutch tweets from the Netherlands to get an insight into the perception and reactions of users during the early months of the COVID-19 pandemic. We focused on five major user communities of users: government and health organizations, news media, politicians, the general public and conspiracy theory supporters, investigating differences among them in topic dominance and the expressions of emotions. Through topic modeling we monitor the evolution of the conversation about COVID-19 among these communities. Our results indicate that the national focus on COVID-19 shifted from the virus itself to its impact on the economy between February and April. Surprisingly, the overall emotional public response appears to be substantially positive and expressing trust, although differences can be observed in specific group of users.
Abusive language detection is an unsolved and challenging problem for the NLP community. Recent literature suggests various approaches to distinguish between different language phenomena (e.g., hate speech vs. cyberbullying vs. offensive language) and factors (degree of explicitness and target) that may help to classify different abusive language phenomena. There are data sets that annotate the target of abusive messages (i.e.OLID/OffensEval (Zampieri et al., 2019a)). However, there is a lack of data sets that take into account the degree of explicitness. In this paper, we propose annotation guidelines to distinguish between explicit and implicit abuse in English and apply them to OLID/OffensEval. The outcome is a newly created resource, AbuseEval v1.0, which aims to address some of the existing issues in the annotation of offensive and abusive language (e.g., explicitness of the message, presence of a target, need of context, and interaction across different phenomena).
Lexical normalization is the task of translating non-standard social media data to a standard form. Previous work has shown that this is beneficial for many downstream tasks in multiple languages. However, for Italian, there is no benchmark available for lexical normalization, despite the presence of many benchmarks for other tasks involving social media data. In this paper, we discuss the creation of a lexical normalization dataset for Italian. After two rounds of annotation, a Cohen’s kappa score of 78.64 is obtained. During this process, we also analyze the inter-annotator agreement for this task, which is only rarely done on datasets for lexical normalization,and when it is reported, the analysis usually remains shallow. Furthermore, we utilize this dataset to train a lexical normalization model and show that it can be used to improve dependency parsing of social media data. All annotated data and the code to reproduce the results are available at: http://bitbucket.org/robvanderg/normit.
This paper describes a crowdsourcing experiment on the annotation of plot-like structures in English news articles. CrowdThruth methodology and metrics have been applied to select valid annotations from the crowd. We further run an in-depth analysis of the annotated data by comparing them with available expert data. Our results show a valuable use of crowdsourcing annotations for such complex semantic tasks, and suggest a new annotation approach which combine crowd and experts.
In this paper we describe the ongoing work on the Circumstantial Event Ontology (CEO), a newly developed ontology for calamity events that models semantic circumstantial relations between event classes. The circumstantial relations are designed manually, based on the shared properties of each event class. We discuss and contrast two types of event circumstantial relations: semantic circumstantial relations and episodic circumstantial relations. Further, we show the metamodel and the current contents of the ontology and outline the evaluation of the CEO.
This paper reports on the Event StoryLine Corpus (ESC) v1.0, a new benchmark dataset for the temporal and causal relation detection. By developing this dataset, we also introduce a new task, the StoryLine Extraction from news data, which aims at extracting and classifying events relevant for stories, from across news documents spread in time and clustered around a single seminal event or topic. In addition to describing the dataset, we also report on three baselines systems whose results show the complexity of the task and suggest directions for the development of more robust systems.
This paper presents a new resource, called Content Types Dataset, to promote the analysis of texts as a composition of units with specific semantic and functional roles. By developing this dataset, we also introduce a new NLP task for the automatic classification of Content Types. The annotation scheme and the dataset are described together with two sets of classification experiments.
In this paper we present PIERINO (PIattaforma per l’Estrazione e il Recupero di INformazione Online), a system that was implemented in collaboration with the Italian Ministry of Education, University and Research to analyse the citizens’ comments given in #labuonascuola survey. The platform includes various levels of automatic analysis such as key-concept extraction and word co-occurrences. Each analysis is displayed through an intuitive view using different types of visualizations, for example radar charts and sunburst. PIERINO was effectively used to support shaping the last Italian school reform, proving the potential of NLP in the context of policy making.
This paper presents a framework and methodology for the annotation of perspectives in text. In the last decade, different aspects of linguistic encoding of perspectives have been targeted as separated phenomena through different annotation initiatives. We propose an annotation scheme that integrates these different phenomena. We use a multilayered annotation approach, splitting the annotation of different aspects of perspectives into small subsequent subtasks in order to reduce the complexity of the task and to better monitor interactions between layers. Currently, we have included four layers of perspective annotation: events, attribution, factuality and opinion. The annotations are integrated in a formal model called GRaSP, which provides the means to represent instances (e.g. events, entities) and propositions in the (real or assumed) world in relation to their mentions in text. Then, the relation between the source and target of a perspective is characterized by means of perspective annotations. This enables us to place alternative perspectives on the same entity, event or proposition next to each other.
This paper describes two sets of crowdsourcing experiments on temporal information annotation conducted on two languages, i.e., English and Italian. The first experiment, launched on the CrowdFlower platform, was aimed at classifying temporal relations given target entities. The second one, relying on the CrowdTruth metric, consisted in two subtasks: one devoted to the recognition of events and temporal expressions and one to the detection and classification of temporal relations. The outcomes of the experiments suggest a valuable use of crowdsourcing annotations also for a complex task like Temporal Processing.
The increasing streams of information pose challenges to both humans and machines. On the one hand, humans need to identify relevant information and consume only the information that lies at their interests. On the other hand, machines need to understand the information that is published in online data streams and generate concise and meaningful overviews. We consider events as prime factors to query for information and generate meaningful context. The focus of this paper is to acquire empirical insights for identifying salience features in tweets and news about a target event, i.e., the event of “whaling”. We first derive a methodology to identify such features by building up a knowledge space of the event enriched with relevant phrases, sentiments and ranked by their novelty. We applied this methodology on tweets and we have performed preliminary work towards adapting it to news articles. Our results show that crowdsourcing text relevance, sentiments and novelty (1) can be a main step in identifying salient information, and (2) provides a deeper and more precise understanding of the data at hand compared to state-of-the-art approaches.
This paper reports on research activities on automatic methods for the enrichment of the Senso Comune platform. At this stage of development, we will report on two tasks, namely word sense alignment with MultiWordNet and automatic acquisition of Verb Shallow Frames from sense annotated data in the MultiSemCor corpus. The results obtained are satisfying. We achieved a final F-measure of 0.64 for noun sense alignment and a F-measure of 0.47 for verb sense alignment, and an accuracy of 68\% on the acquisition of Verb Shallow Frames.
Lexica of predicate-argument structures constitute a useful tool for several tasks in NLP. This paper describes a web-service system for automatic acquisition of verb subcategorization frames (SCFs) from parsed data in Italian. The system acquires SCFs in an unsupervised manner. We created two gold standards for the evaluation of the system, the first by mixing together information from two lexica (one manually created and the second automatically acquired) and manual exploration of corpus data and the other annotating data extracted from a specialized corpus (environmental domain). Data filtering is accomplished by means of the maximum likelihood estimate (MLE). The evaluation phase has allowed us to identify the best empirical MLE threshold for the creation of a lexicon (P=0.653, R=0.557, F1=0.601). In addition to this, we assigned to the extracted entries of the lexicon a confidence score based on the relative frequency and evaluated the extractor on domain specific data. The confidence score will allow the final user to easily select the entries of the lexicon in terms of their reliability: one of the most interesting feature of this work is the possibility the final users have to customize the results of the SCF extractor, obtaining different SCF lexica in terms of size and accuracy.
Sentiment Analysis (SA) and Opinion Mining (OM) have become a popular task in recent years in NLP with the development of language resources, corpora and annotation schemes. The possibility to discriminate between objective and subjective expressions contributes to the identification of a document's semantic orientation and to the detection of the opinions and sentiments expressed by the authors or attributed to other participants in the document. Subjectivity word sense disambiguation helps in this task, automatically determining which word senses in a corpus are being used subjectively and which are being used objectively. This paper reports on a methodology to assign in a semi-automatic way connotative values to eventive nouns usually labelled as neutral through syntagmatic patterns that express cause-effect relations between emotion cause events and emotion words. We have applied our method to nouns and we have been able reduce the number of OBJ polarity values associated to event noun.
In recent years we have resgitered a renewed interest in event detection and temporal processing of text/discourse. TimeML (Pustejovsky et al., 2003a) has shed new lights on the notion of event and developed a new methodology for its annotation. On a parallel, works on anaphora resolution have developed a reliable methodology for the annotation and pointed out the core role of this phenomenon for the improvement of NLP systems. This paper tries to put together these two lines of research by describing a case study for the creation of an annotation scheme on event anaphora. We claim that this work could have consequences for the annotation of eventualities as proposed in TimeML and on the use of the tag and on the study of anaphora and its annotation. The annotation scheme and its guidelines have been developed on the basis of a coarse grained bottom up approach. In order to do this, we have performed a small sampling annotation which has highlighted shortcomings and open issues which need to be resolved.
This paper describes the creation of a bilingual corpus of inter-linked events for Italian and English. Linkage is accomplished through the Inter-Lingual Index (ILI) that links ItalWordNet with WordNet. The availability of this resource, on the one hand, enables contrastive analysis of the linguistic phenomena surrounding events in both languages, and on the other hand, can be used to perform multilingual temporal analysis of texts. In addition to describing the methodology for construction of the inter-linked corpus and the analysis of the data collected, we demonstrate that the ILI could potentially be used to bootstrap the creation of comparable corpora by exporting layers of annotation for words that have the same sense.
In this paper we address the issue of developing an interoperable infrastructure for language resources and technologies. In our approach, called UFRA, we extend the Federate Database Architecture System adding typical functionalities caming from UIMA. In this way, we capitalize the advantages of a federated architecture, such as autonomy, heterogeneity and distribution of components, monitored by a central authority responsible for checking both the integration of components and user rights on performing different tasks. We use the UIMA approach to manage and define one common front-end, enabling users and clients to query, retrieve and use language resources and technologies. The purpose of this paper is to show how UIMA leads from a Federated Database Architecture to a Federated Resource Architecture, adding to a registry of available components both static resources such as lexicons and corpora and dynamic ones such as tools and general purpose language technologies. At the end of the paper, we present a case-study that adopts this framework to integrate the SIMPLE lexicon and TIMEML annotation guidelines to tag natural language texts.
The aim of this work is the presentation and preliminary evaluation of an XML annotation scheme for marking bridging anaphors of the form definite article + N in Italian. The scheme is based on a corpus-study. The data we collected from the evaluation experiment seem to support the reliability of the scheme, although some problems still remain open.