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

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

All Purpose Textual Data Information Extraction, Visualization and Querying

abstract: Since the advent of the internet and even more after social media platforms, the explosive growth of textual data and its availability has made analysis a tedious task. Information extraction systems are available but are generally too specific and often only extract certain kinds of information they deem necessary and extraction worthy. Using data visualization theory and fast, interactive querying methods, leaving out information might not really be necessary. This thesis explores textual data visualization techniques, intuitive querying, and a novel approach to all-purpose textual information extraction to encode large text corpus to improve human understanding of the information present in textual data. This thesis presents a modified traversal algorithm on dependency parse output of text to extract all subject predicate object pairs from text while ensuring that no information is missed out. To support full scale, all-purpose information extraction from large text corpuses, a data preprocessing pipeline is recommended to be used before the extraction is run. The output format is designed specifically to fit on a node-edge-node model and form the building blocks of a network which makes understanding of the text and querying of information from corpus quick and intuitive. It attempts to reduce reading time and enhancing understanding of the text using interactive graph and timeline.Dissertation/ThesisMasters Thesis Software Engineering 201

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail

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

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

Online learning of latent linguistic structure with approximate search

Automatic analysis of natural language data is a frequently occurring application of machine learning systems. These analyses often revolve around some linguistic structure, for instance a syntactic analysis of a sentence by means of a tree. Machine learning models that carry out structured prediction, as opposed to simpler machine learning tasks such as classification or regression, have therefore received considerable attention in the language processing literature. As an additional twist, the sought linguistic structures are sometimes not directly modeled themselves. Rather, prediction takes place in a different space where the same linguistic structure can be represented in more than one way. However, in a standard supervised learning setting, these prediction structures are not available in the training data, but only the linguistic structure. Since multiple prediction structures may correspond to the same linguistic structure, it is thus unclear which prediction structure to use for learning. One option is to treat the prediction structure as latent and let the machine learning algorithm guide this selection. In this dissertation we present an abstract framework for structured prediction. This framework supports latent structures and is agnostic of the particular language processing task. It defines a set of hyperparameters and task-specific functions which a user must implement in order to apply it to a new task. The advantage of this modularization is that it permits comparisons and reuse across tasks in a common framework. The framework we devise is based on the structured perceptron for learning. The perceptron is an online learning algorithm which considers one training instance at a time, makes a prediction, and carries out an update if the prediction was wrong. We couple the structured perceptron with beam search, which is a general purpose search algorithm. Beam search is, however, only approximate, meaning that there is no guarantee that it will find the optimal structure in a large search space. Therefore special attention is required to handle search errors during training. This has led to the development of special update methods such as early and max-violation updates. The contributions of this dissertation sit at the intersection of machine learning and natural language processing. With regard to language processing, we consider three tasks: Coreference resolution, dependency parsing, and joint sentence segmentation and dependency parsing. For coreference resolution, we start from an existing latent tree model and extend it to accommodate non-local features drawn from a greater structural context. This requires us to sacrifice exact for approximate search, but we show that, assuming sufficiently advanced update methods are used for the structured perceptron, then the richer scope of features yields a stronger coreference model. We take a transition-based approach to dependency parsing, where dependency trees are constructed incrementally by transition system. Latent structures for transition-based parsing have previously not received enough attention, partly because the characterization of the prediction space is non-trivial. We provide a thorough analysis of this space with regard to the ArcStandard with Swap transition system. This characterization enables us to evaluate the role of latent structures in transition-based dependency parsing. Empirically we find that the utility of latent structures depend on the choice of approximate search -- for greedy search they improve performance, whereas with beam search they are on par, or sometimes slightly ahead of, previous approaches. We then go on to extend this transition system to do joint sentence segmentation and dependency parsing. We develop a transition system capable of handling this task and evaluate it on noisy, non-edited texts. With a set of carefully selected baselines and data sets we employ this system to measure the effectiveness of syntactic information for sentence segmentation. We show that, in the absence of obvious orthographic clues such as punctuation and capitalization, syntactic information can be used to improve sentence segmentation. With regard to machine learning, our contributions of course include the framework itself. The task-specific evaluations, however, allow us to probe the learning machinery along certain boundary points and draw more general conclusions. A recurring observation is that some of the standard update methods for the structured perceptron with approximate search -- e.g., early and max-violation updates -- are inadequate when the predicted structure reaches a certain size. We show that the primary problem with these updates is that they may discard training data and that this effect increases as the structure size increases. This problem can be handled by using more advanced update methods that commit to using all the available training data. Here, we propose a new update method, DLaSO, which consistently outperforms all other update methods we compare to. Moreover, while this problem potentially could be handled by an increased beam size, we also show that this cannot fully compensate for the structure size and that the more advanced methods indeed are required.Bei der automatisierten Analyse natürlicher Sprache werden in der Regel maschinelle Lernverfahren eingesetzt, um verschiedenste linguistische Information wie beispielsweise syntaktische Strukturen vorherzusagen. Structured Prediction (dt. etwa Strukturvorhersage), also der Zweig des maschinellen Lernens, der sich mit der Vorhersage komplexer Strukturen wie formalen Bäumen oder Graphen beschäftigt, hat deshalb erhebliche Beachtung in der Forschung zur automatischen Sprachverarbeitung gefunden. In manchen Fällen ist es vorteilhaft, die gesuchte linguistische Struktur nicht direkt zu modellieren und stattdessen interne Repräsentationen zu lernen, aus denen dann die gewünschte linguistische Information abgeleitet werden kann. Da die internen Repräsentationen allerdings selten direkt in Trainingsdaten verfügbar sind, sondern erst aus der linguistischen Annotation inferiert werden müssen, kann es vorkommen, dass dabei mehrere äquivalente Strukturen in Frage kommen. Anstatt nun vor dem Lernen eine Struktur beliebig auszuwählen, kann man diese Entscheidung dem Lernverfahren selbst überlassen, welches dann selbständig die für das Modell am besten passende auszuwählen lernt. Unter diesen Umständen bezeichnet man die interne, nicht a priori bekannte Repräsentation für eine gesuchte Zielstruktur als latent. Diese Dissertation stellt ein Structured Prediction Framework vor, mit dem man den Vorteil latenter Repräsentationen nutzen kann und welches gleichzeitig von konkreten Anwendungsfällen abstrahiert. Diese Modularisierung ermöglicht die Wiederverwendbarkeit und den Vergleich über mehrere Aufgaben und Aufgabenklassen hinweg. Um das Framework auf ein reales Problem anzuwenden, müssen nur einige Hyperparameter definiert und einige problemspezifische Funktionen implementiert werden. Das vorgestellte Framework basiert auf dem Structured Perceptron. Der Perceptron-Algorithmus ist ein inkrementelles Lernverfahren (eng. online learning), bei dem während des Trainings einzelne Trainingsinstanzen nacheinander betrachtet werden. In jedem Schritt wird mit dem aktuellen Modell eine Vorhersage gemacht. Stimmt die Vorhersage nicht mit dem vorgegebenen Ergebnis überein, wird das Modell durch ein entsprechendes Update angepasst und mit der nächsten Trainingsinstanz fortgefahren. Der Structured Perceptron wird im vorgestellten Framework mit Beam Search kombiniert. Beam Search ist ein approximatives Suchverfahren, welches auch in sehr großen Suchräumen effizientes Suchen erlaubt. Es kann aus diesem Grund aber keine Garantie dafür bieten, dass das gefundene Ergebnis auch das optimale ist. Das Training eines Perceptrons mit Beam Search erfordert deshalb besondere Update-Methoden, z.B. Early- oder Max-Violation-Updates, um mögliche Vorhersagefehler, die auf den Suchalgorithmus zurückgehen, auszugleichen. Diese Dissertation ist an der Schnittstelle zwischen maschinellem Lernen und maschineller Sprachverarbeitung angesiedelt. Im Bereich Sprachverarbeitung beschäftigt sie sich mit drei Aufgaben: Koreferenzresolution, Dependenzparsing und Dependenzparsing mit gleichzeitiger Satzsegmentierung. Das vorgestellte Modell zur Koreferenzresolution ist eine Erweiterung eines existierenden Modells, welches Koreferenz mit Hilfe latenter Baumstrukturen repräsentiert. Dieses Modell wird um Features erweitert, mit denen nicht-lokale Abhängigkeiten innerhalb eines größeren strukturellen Kontexts modelliert werden. Die Modellierung nicht-lokaler Abhängigkeiten macht durch die kombinatorische Explosion der Features die Verwendung eines approximativen Suchverfahrens notwendig. Es zeigt sich aber, dass das so entstandene Koreferenzmodell trotz der approximativen Suche dem Modell ohne nicht-lokale Features überlegen ist, sofern hinreichend gute Update-Verfahren beim Lernen verwendet werden. Für das Dependenzparsing verwenden wir ein transitionsbasiertes Verfahren, bei dem Dependenzbäume inkrementell durch Transitionen zwischen definierten Zuständen konstruiert werden. Im ersten Schritt erarbeiten wir eine umfassende Analyse des latenten Strukturraums eines bekannten Transitionssystems, nämlich ArcStandard mit Swap. Diese Analyse erlaubt es uns, die Rolle der latenten Strukturen in einem transitionsbasierten Dependenzparser zu evaluieren. Wir zeigen dann empirisch, dass die Nützlichkeit latenter Strukturen von der Wahl des Suchverfahrens abhängt -- in Kombination mit Greedy-Search verbessern sich die Ergebnisse, in Kombination mit Beam-Search bleiben sie gleich oder verbessern sich leicht gegenüber vergleichbaren Modellen. Für die dritte Aufgabe wird der Parser noch einmal erweitert: wir entwickeln das Transitionssystem so weiter, dass es neben syntaktischer Struktur auch Satzgrenzen vorhersagt und testen das System auf verrauschten und unredigierten Textdaten. Mit Hilfe sorgfältig ausgewählter Baselinemodelle und Testdaten messen wir den Einfluss syntaktischer Information auf die Vorhersagequalität von Satzgrenzen und zeigen, dass sich in Abwesenheit orthographischer Information wie Interpunktion und Groß- und Kleinschreibung das Ergebnis durch syntaktische Information verbessert. Zu den wissenschaftlichen Beiträgen der Arbeit gehört einerseits das Framework selbst. Unsere problemspezifischen Experimente ermöglichen es uns darüber hinaus, die Lernverfahren zu untersuchen und allgemeinere Schlußfolgerungen zu ziehen. So finden wir z.B. in mehreren Experimenten, dass die etablierten Update-Methoden, also Early- oder Max-Violation-Update, nicht mehr gut funktionieren, sobald die vorhergesagte Struktur eine gewisse Größe überschreitet. Es zeigt sich, dass das Hauptproblem dieser Methoden das Auslassen von Trainingsdaten ist, und dass sie desto mehr Daten auslassen, je größer die vorhergesagte Struktur wird. Dieses Problem kann durch bessere Update-Methoden vermieden werden, bei denen stets alle Trainingsdaten verwendet werden. Wir stellen eine neue Methode vor, DLaSO, und zeigen, dass diese Methode konsequent bessere Ergebnisse liefert als alle Vergleichsmethoden. Überdies zeigen wir, dass eine erhöhte Beamgröße beim Suchen das Problem der ausgelassenen Trainingsdaten nicht kompensieren kann und daher keine Alternative zu besseren Update-Methoden darstellt

Representation and Processing of Composition, Variation and Approximation in Language Resources and Tools

In my habilitation dissertation, meant to validate my capacity of and maturity for directingresearch activities, I present a panorama of several topics in computational linguistics, linguisticsand computer science.Over the past decade, I was notably concerned with the phenomena of compositionalityand variability of linguistic objects. I illustrate the advantages of a compositional approachto the language in the domain of emotion detection and I explain how some linguistic objects,most prominently multi-word expressions, defy the compositionality principles. I demonstratethat the complex properties of MWEs, notably variability, are partially regular and partiallyidiosyncratic. This fact places the MWEs on the frontiers between different levels of linguisticprocessing, such as lexicon and syntax.I show the highly heterogeneous nature of MWEs by citing their two existing taxonomies.After an extensive state-of-the art study of MWE description and processing, I summarizeMultiflex, a formalism and a tool for lexical high-quality morphosyntactic description of MWUs.It uses a graph-based approach in which the inflection of a MWU is expressed in function ofthe morphology of its components, and of morphosyntactic transformation patterns. Due tounification the inflection paradigms are represented compactly. Orthographic, inflectional andsyntactic variants are treated within the same framework. The proposal is multilingual: it hasbeen tested on six European languages of three different origins (Germanic, Romance and Slavic),I believe that many others can also be successfully covered. Multiflex proves interoperable. Itadapts to different morphological language models, token boundary definitions, and underlyingmodules for the morphology of single words. It has been applied to the creation and enrichmentof linguistic resources, as well as to morphosyntactic analysis and generation. It can be integratedinto other NLP applications requiring the conflation of different surface realizations of the sameconcept.Another chapter of my activity concerns named entities, most of which are particular types ofMWEs. Their rich semantic load turned them into a hot topic in the NLP community, which isdocumented in my state-of-the art survey. I present the main assumptions, processes and resultsissued from large annotation tasks at two levels (for named entities and for coreference), parts ofthe National Corpus of Polish construction. I have also contributed to the development of bothrule-based and probabilistic named entity recognition tools, and to an automated enrichment ofProlexbase, a large multilingual database of proper names, from open sources.With respect to multi-word expressions, named entities and coreference mentions, I pay aspecial attention to nested structures. This problem sheds new light on the treatment of complexlinguistic units in NLP. When these units start being modeled as trees (or, more generally, asacyclic graphs) rather than as flat sequences of tokens, long-distance dependencies, discontinu-ities, overlapping and other frequent linguistic properties become easier to represent. This callsfor more complex processing methods which control larger contexts than what usually happensin sequential processing. Thus, both named entity recognition and coreference resolution comesvery close to parsing, and named entities or mentions with their nested structures are analogous3to multi-word expressions with embedded complements.My parallel activity concerns finite-state methods for natural language and XML processing.My main contribution in this field, co-authored with 2 colleagues, is the first full-fledged methodfor tree-to-language correction, and more precisely for correcting XML documents with respectto a DTD. We have also produced interesting results in incremental finite-state algorithmics,particularly relevant to data evolution contexts such as dynamic vocabularies or user updates.Multilingualism is the leitmotif of my research. I have applied my methods to several naturallanguages, most importantly to Polish, Serbian, English and French. I have been among theinitiators of a highly multilingual European scientific network dedicated to parsing and multi-word expressions. I have used multilingual linguistic data in experimental studies. I believethat it is particularly worthwhile to design NLP solutions taking declension-rich (e.g. Slavic)languages into account, since this leads to more universal solutions, at least as far as nominalconstructions (MWUs, NEs, mentions) are concerned. For instance, when Multiflex had beendeveloped with Polish in mind it could be applied as such to French, English, Serbian and Greek.Also, a French-Serbian collaboration led to substantial modifications in morphological modelingin Prolexbase in its early development stages. This allowed for its later application to Polishwith very few adaptations of the existing model. Other researchers also stress the advantages ofNLP studies on highly inflected languages since their morphology encodes much more syntacticinformation than is the case e.g. in English.In this dissertation I am also supposed to demonstrate my ability of playing an active rolein shaping the scientific landscape, on a local, national and international scale. I describemy: (i) various scientific collaborations and supervision activities, (ii) roles in over 10 regional,national and international projects, (iii) responsibilities in collective bodies such as program andorganizing committees of conferences and workshops, PhD juries, and the National UniversityCouncil (CNU), (iv) activity as an evaluator and a reviewer of European collaborative projects.The issues addressed in this dissertation open interesting scientific perspectives, in whicha special impact is put on links among various domains and communities. These perspectivesinclude: (i) integrating fine-grained language data into the linked open data, (ii) deep parsingof multi-word expressions, (iii) modeling multi-word expression identification in a treebank as atree-to-language correction problem, and (iv) a taxonomy and an experimental benchmark fortree-to-language correction approaches