Towards Harmful Erotic Content Detection through Coreference-Driven Contextual Analysis

Adult content detection still poses a great challenge for automation. Existing classifiers primarily focus on distinguishing between erotic and non-erotic texts. However, they often need more nuance in assessing the potential harm. Unfortunately, the content of this nature falls beyond the reach of generative models due to its potentially harmful nature. Ethical restrictions prohibit large language models (LLMs) from analyzing and classifying harmful erotics, let alone generating them to create synthetic datasets for other neural models. In such instances where data is scarce and challenging, a thorough analysis of the structure of such texts rather than a large model may offer a viable solution. Especially given that harmful erotic narratives, despite appearing similar to harmless ones, usually reveal their harmful nature first through contextual information hidden in the non-sexual parts of the narrative. This paper introduces a hybrid neural and rule-based context-aware system that leverages coreference resolution to identify harmful contextual cues in erotic content. Collaborating with professional moderators, we compiled a dataset and developed a classifier capable of distinguishing harmful from non-harmful erotic content. Our hybrid model, tested on Polish text, demonstrates a promising accuracy of 84% and a recall of 80%. Models based on RoBERTa and Longformer without explicit usage of coreference chains achieved significantly weaker results, underscoring the importance of coreference resolution in detecting such nuanced content as harmful erotics. This approach also offers the potential for enhanced visual explainability, supporting moderators in evaluating predictions and taking necessary actions to address harmful content.Comment: Accepted for 6th Workshop on Computational Models of Reference, Anaphora and Coreference at EMNLP 2023 Conferenc

Findings of the Shared Task on Multilingual Coreference Resolution

This paper presents an overview of the shared task on multilingual coreference resolution associated with the CRAC 2022 workshop. Shared task participants were supposed to develop trainable systems capable of identifying mentions and clustering them according to identity coreference. The public edition of CorefUD 1.0, which contains 13 datasets for 10 languages, was used as the source of training and evaluation data. The CoNLL score used in previous coreference-oriented shared tasks was used as the main evaluation metric. There were 8 coreference prediction systems submitted by 5 participating teams; in addition, there was a competitive Transformer-based baseline system provided by the organizers at the beginning of the shared task. The winner system outperformed the baseline by 12 percentage points (in terms of the CoNLL scores averaged across all datasets for individual languages)

A preliminary study in zero anaphora coreference resolution for Polish

A preliminary study in zero anaphora coreference resolution for PolishZero anaphora is an element of the coreference resolution task that has not yet been directly addressed in Polish and, in most studies, it has been left as the most challenging aspect for further investigation. This article presents an initial study of this problem. The preparation of a machine learning approach, alongside engineering features based on linguistic study of the KPWr corpus, is discussed. This study utilizes existing tools for Polish coreference resolution as sources of partial coreferential clusters containing pronoun, noun and named entity mentions. They are also used as baseline zero coreference resolution systems for comparison with our system. The evaluation process is focused not only on clustering correctness, without taking into account types of mentions, using standard CoNLL-2012 measures, but also on the informativeness of the resulting relations. According to the annotation approach used for coreference to the KPWr corpus, only named entities are treated as mentions that are informative enough to constitute a link to real world objects. Consequently, we provide an evaluation of informativeness based on found links between zero anaphoras and named entities. For the same reason, we restrict coreference resolution in this study to mention clusters built around named entities. Wstępne studium rozwiązywania problemu koreferencji anafory zerowej w języku polskimKoreferencja zerowa, w języku polskim, jest jednym z zagadnień rozpoznawania koreferencji. Dotychczas nie była ona bezpośrednim przedmiotem badań, gdyż ze względu na jej złożoność była pomijana i odsuwana na dalsze etapy badań. Artykuł prezentuje wstępne studium problemu, jakim jest rozpoznawanie koreferencji zerowej. Przedstawiamy podejście wykorzystujące techniki uczenia maszynowego oraz proces tworzenia cech w oparciu o analizę lingwistyczną korpusu KPWr. W przedstawionej pracy wykorzystujemy istniejące narzędzia do rozpoznawania koreferencji dla pozostałych rodzajów wzmianek (tj. nazwy własne, frazy rzeczownikowe oraz zaimki) jako źródło częściowych zbiorów wzmianek odnoszących się do tego samego obiektu, a także jako punkt odniesienia dla uzyskanych przez nas wyników. Ocena skupia się nie tylko na poprawności uzyskanych zbiorów wzmianek, bez względu na ich typ, co odzwierciedlają wyniki podane dla standardowych metryk CoNLL-2012, ale także na wartości informacji, która zostaje uzyskana w wyniku rozpoznania koreferencji. W nawiązaniu do założeń anotacji korpusu KPWr, jedynie nazwy własne traktowane są jako wzmianki, które zawierają w sobie wystarczająco szczegółową informację, aby można było powiązać je z obiektami rzeczywistymi. W konsekwencji dostarczamy także ocenę opartą na wartości informacji dla podmiotów domyślnych połączonych relacją koreferencji z nazwami własnymi. Z tą samą motywacją rozpatrujemy jedynie zbiory wzmianek koreferencyjnych zbudowane wokół nazw własnych

Investigating Multilingual Coreference Resolution by Universal Annotations

Multilingual coreference resolution (MCR) has been a long-standing and challenging task. With the newly proposed multilingual coreference dataset, CorefUD (Nedoluzhko et al., 2022), we conduct an investigation into the task by using its harmonized universal morphosyntactic and coreference annotations. First, we study coreference by examining the ground truth data at different linguistic levels, namely mention, entity and document levels, and across different genres, to gain insights into the characteristics of coreference across multiple languages. Second, we perform an error analysis of the most challenging cases that the SotA system fails to resolve in the CRAC 2022 shared task using the universal annotations. Last, based on this analysis, we extract features from universal morphosyntactic annotations and integrate these features into a baseline system to assess their potential benefits for the MCR task. Our results show that our best configuration of features improves the baseline by 0.9% F1 score.Comment: Accepted at Findings of EMNLP202

Review of coreference resolution in English and Persian

Coreference resolution (CR) is one of the most challenging areas of natural language processing. This task seeks to identify all textual references to the same real-world entity. Research in this field is divided into coreference resolution and anaphora resolution. Due to its application in textual comprehension and its utility in other tasks such as information extraction systems, document summarization, and machine translation, this field has attracted considerable interest. Consequently, it has a significant effect on the quality of these systems. This article reviews the existing corpora and evaluation metrics in this field. Then, an overview of the coreference algorithms, from rule-based methods to the latest deep learning techniques, is provided. Finally, coreference resolution and pronoun resolution systems in Persian are investigated.Comment: 44 pages, 11 figures, 5 table

Creating a Semantic Graph from Wikipedia

With the continued need to organize and automate the use of data, solutions are needed to transform unstructred text into structred information. By treating dependency grammar functions as programming language functions, this process produces \property maps which connect entities (people, places, events) with snippets of information. These maps are used to construct a semantic graph. By inputting Wikipedia, a large graph of information is produced representing a section of history. The resulting graph allows a user to quickly browse a topic and view the interconnections between entities across history

Towards Multilingual Coreference Resolution

The current work investigates the problems that occur when coreference resolution is considered as a multilingual task. We assess the issues that arise when a framework using the mention-pair coreference resolution model and memory-based learning for the resolution process are used. Along the way, we revise three essential subtasks of coreference resolution: mention detection, mention head detection and feature selection. For each of these aspects we propose various multilingual solutions including both heuristic, rule-based and machine learning methods. We carry out a detailed analysis that includes eight different languages (Arabic, Catalan, Chinese, Dutch, English, German, Italian and Spanish) for which datasets were provided by the only two multilingual shared tasks on coreference resolution held so far: SemEval-2 and CoNLL-2012. Our investigation shows that, although complex, the coreference resolution task can be targeted in a multilingual and even language independent way. We proposed machine learning methods for each of the subtasks that are affected by the transition, evaluated and compared them to the performance of rule-based and heuristic approaches. Our results confirmed that machine learning provides the needed flexibility for the multilingual task and that the minimal requirement for a language independent system is a part-of-speech annotation layer provided for each of the approached languages. We also showed that the performance of the system can be improved by introducing other layers of linguistic annotations, such as syntactic parses (in the form of either constituency or dependency parses), named entity information, predicate argument structure, etc. Additionally, we discuss the problems occurring in the proposed approaches and suggest possibilities for their improvement

Korreferentzia-ebazpena euskarazko testuetan.

203 p.Gaur egun, korreferentzia-ebazpen automatikoa gakotzat har dezakegu testuak ulertuahal izateko; ondorioz, behar-beharrezkoa da diskurtsoaren ulerkuntza sakona eskatzenduten Lengoaia Naturalaren Prozesamenduko (NLP) hainbat atazatan.Testu bateko bi espresio testualek objektu berbera adierazi edo erreferentziatzendutenean, bi espresio horien artean korreferentzia-erlazio bat dagoela esan ohi da. Testubatean ager daitezkeen espresio testual horien arteko korreferentzia-erlazioak ebazteahelburu duen atazari korreferentzia-ebazpena deritzo.Tesi-lan hau, hizkuntzalaritza konputazionalaren arloan kokatzen da eta euskarazidatzitako testuen korreferentzia-ebazpen automatikoa du helburu, zehazkiago esanda,euskarazko korreferentzia-ebazpen automatikoa gauzatzeko dagoen baliabide eta tresnenhutsunea betetzea du helburu.Tesi-lan honetan, lehenik euskarazko testuetan ager daitezkeen espresio testualakautomatikoki identifikatzeko garatu dugun erregelatan oinarritutako tresna azaltzen da.Ondoren, Stanfordeko unibertsitatean ingeleserako diseinatu den erregelatanoinarritutako korreferentzia-ebazpenerako sistema euskararen ezaugarrietara nolaegokitu den eta ezagutza-base semantikoak erabiliz nola hobetu dugun aurkezten da.Bukatzeko, ikasketa automatikoan oinarritzen den BART korreferentzia-ebazpenerakosistema euskarara egokitzeko eta hobetzeko egindako lana azaltzen da