Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

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

Relation Classification with Limited Supervision

Large reams of unstructured data, for instance in form textual document collections containing entities and relations, exist in many domains. The process of deriving valuable domain insights and intelligence from such documents collections usually involves the extraction of information such as the relations between the entities in such collections. Relation classification is the task of detecting relations between entities. Supervised machine learning models, which have become the tool of choice for relation classification, require substantial quantities of annotated data for each relation in order to perform optimally. For many domains, such quantities of annotated data for relations may not be readily available, and manually curating such annotations may not be practical due to time and cost constraints. In this work, we develop both model-specific and model-agnostic approaches for relation classification with limited supervision. We start by proposing an approach for learning embeddings for contextual surface patterns, which are the set of surface patterns associated with entity pairs across a text corpus, to provide additional supervision signals for relation classification with limited supervision. We find that this approach improves classification performance on relations with limited supervision instances. However, this initial approach assumes the availability of at least one annotated instance per relation during training. In order to address this limitation, we propose an approach which formulates the task of relation classification as that of textual entailment. This reformulation allows us to use the textual descriptions of relations to classify their instances. It also allows us to utilize existing textual entailment datasets and models to classify relations with zero supervision instances. The two methods proposed previously rely on the use of specific model architectures for relation classification. Since a wide variety of models have been proposed for relation classification in the literature, a more general approach is thus desirable. We subsequently propose our first model-agnostic meta-learning algorithm for relation classification with limited supervision. This algorithm is applicable to any gradient-optimized relation classification model. We show that the proposed approach improves the predictive performance of two existing relation classification models when supervision for relations is limited. Next, because all the approaches we have proposed so far assume the availability of all supervision needed for classifying relations prior to model training, they are unable to handle the case when new supervision for relations becomes available after training. Such new supervision may need to be incorporated into the model to enable it classify new relations or to improve its performance on existing relations. Our last approach addresses this short-coming. We propose a model-agnostic algorithm which enables relation classification models to learn continually from new supervision as it becomes available, while doing so in a data-efficient manner and without forgetting knowledge of previous relations

Automatic recognition of the general-purpose communicative functions defined by the ISO 24617-2 standard for dialog act annotation

From the perspective of a dialog system, it is important to identify the intention behind the segments in a dialog, since it provides an important cue regarding the information that is present in the segments and how they should be interpreted. ISO 24617-2, the standard for dialog act annotation, defines a hierarchically organized set of general-purpose communicative functions which correspond to different intentions that are relevant in the context of a dialog. We explore the automatic recognition of these communicative functions in the DialogBank, which is a reference set of dialogs annotated according to this standard. To do so, we propose adaptations of existing approaches to flat dialog act recognition that allow them to deal with the hierarchical classification problem. More specifically, we propose the use of an end-to-end hierarchical network with cascading outputs and maximum a posteriori path estimation to predict the communicative function at each level of the hierarchy, preserve the dependencies between the functions in the path, and decide at which level to stop. Furthermore, since the amount of dialogs in the DialogBank is small, we rely on transfer learning processes to reduce overfitting and improve performance. The results of our experiments show that our approach outperforms both a flat one and hierarchical approaches based on multiple classifiers and that each of its components plays an important role towards the recognition of general-purpose communicative functions.info:eu-repo/semantics/publishedVersio

Data-efficient methods for information extraction

Strukturierte Wissensrepräsentationssysteme wie Wissensdatenbanken oder Wissensgraphen bieten Einblicke in Entitäten und Beziehungen zwischen diesen Entitäten in der realen Welt. Solche Wissensrepräsentationssysteme können in verschiedenen Anwendungen der natürlichen Sprachverarbeitung eingesetzt werden, z. B. bei der semantischen Suche, der Beantwortung von Fragen und der Textzusammenfassung. Es ist nicht praktikabel und ineffizient, diese Wissensrepräsentationssysteme manuell zu befüllen. In dieser Arbeit entwickeln wir Methoden, um automatisch benannte Entitäten und Beziehungen zwischen den Entitäten aus Klartext zu extrahieren. Unsere Methoden können daher verwendet werden, um entweder die bestehenden unvollständigen Wissensrepräsentationssysteme zu vervollständigen oder ein neues strukturiertes Wissensrepräsentationssystem von Grund auf zu erstellen. Im Gegensatz zu den gängigen überwachten Methoden zur Informationsextraktion konzentrieren sich unsere Methoden auf das Szenario mit wenigen Daten und erfordern keine große Menge an kommentierten Daten. Im ersten Teil der Arbeit haben wir uns auf das Problem der Erkennung von benannten Entitäten konzentriert. Wir haben an der gemeinsamen Aufgabe von Bacteria Biotope 2019 teilgenommen. Die gemeinsame Aufgabe besteht darin, biomedizinische Entitätserwähnungen zu erkennen und zu normalisieren. Unser linguistically informed Named-Entity-Recognition-System besteht aus einem Deep-Learning-basierten Modell, das sowohl verschachtelte als auch flache Entitäten extrahieren kann; unser Modell verwendet mehrere linguistische Merkmale und zusätzliche Trainingsziele, um effizientes Lernen in datenarmen Szenarien zu ermöglichen. Unser System zur Entitätsnormalisierung verwendet String-Match, Fuzzy-Suche und semantische Suche, um die extrahierten benannten Entitäten mit den biomedizinischen Datenbanken zu verknüpfen. Unser System zur Erkennung von benannten Entitäten und zur Entitätsnormalisierung erreichte die niedrigste Slot-Fehlerrate von 0,715 und belegte den ersten Platz in der gemeinsamen Aufgabe. Wir haben auch an zwei gemeinsamen Aufgaben teilgenommen: Adverse Drug Effect Span Detection (Englisch) und Profession Span Detection (Spanisch); beide Aufgaben sammeln Daten von der Social Media Plattform Twitter. Wir haben ein Named-Entity-Recognition-Modell entwickelt, das die Eingabedarstellung des Modells durch das Stapeln heterogener Einbettungen aus verschiedenen Domänen verbessern kann; unsere empirischen Ergebnisse zeigen komplementäres Lernen aus diesen heterogenen Einbettungen. Unser Beitrag belegte den 3. Platz in den beiden gemeinsamen Aufgaben. Im zweiten Teil der Arbeit untersuchten wir Strategien zur Erweiterung synthetischer Daten, um ressourcenarme Informationsextraktion in spezialisierten Domänen zu ermöglichen. Insbesondere haben wir backtranslation an die Aufgabe der Erkennung von benannten Entitäten auf Token-Ebene und der Extraktion von Beziehungen auf Satzebene angepasst. Wir zeigen, dass die Rückübersetzung sprachlich vielfältige und grammatikalisch kohärente synthetische Sätze erzeugen kann und als wettbewerbsfähige Erweiterungsstrategie für die Aufgaben der Erkennung von benannten Entitäten und der Extraktion von Beziehungen dient. Bei den meisten realen Aufgaben zur Extraktion von Beziehungen stehen keine kommentierten Daten zur Verfügung, jedoch ist häufig ein großer unkommentierter Textkorpus vorhanden. Bootstrapping-Methoden zur Beziehungsextraktion können mit diesem großen Korpus arbeiten, da sie nur eine Handvoll Startinstanzen benötigen. Bootstrapping-Methoden neigen jedoch dazu, im Laufe der Zeit Rauschen zu akkumulieren (bekannt als semantische Drift), und dieses Phänomen hat einen drastischen negativen Einfluss auf die endgültige Genauigkeit der Extraktionen. Wir entwickeln zwei Methoden zur Einschränkung des Bootstrapping-Prozesses, um die semantische Drift bei der Extraktion von Beziehungen zu minimieren. Unsere Methoden nutzen die Graphentheorie und vortrainierte Sprachmodelle, um verrauschte Extraktionsmuster explizit zu identifizieren und zu entfernen. Wir berichten über die experimentellen Ergebnisse auf dem TACRED-Datensatz für vier Relationen. Im letzten Teil der Arbeit demonstrieren wir die Anwendung der Domänenanpassung auf die anspruchsvolle Aufgabe der mehrsprachigen Akronymextraktion. Unsere Experimente zeigen, dass die Domänenanpassung die Akronymextraktion in wissenschaftlichen und juristischen Bereichen in sechs Sprachen verbessern kann, darunter auch Sprachen mit geringen Ressourcen wie Persisch und Vietnamesisch.The structured knowledge representation systems such as knowledge base or knowledge graph can provide insights regarding entities and relationship(s) among these entities in the real-world, such knowledge representation systems can be employed in various natural language processing applications such as semantic search, question answering and text summarization. It is infeasible and inefficient to manually populate these knowledge representation systems. In this work, we develop methods to automatically extract named entities and relationships among the entities from plain text and hence our methods can be used to either complete the existing incomplete knowledge representation systems to create a new structured knowledge representation system from scratch. Unlike mainstream supervised methods for information extraction, our methods focus on the low-data scenario and do not require a large amount of annotated data. In the first part of the thesis, we focused on the problem of named entity recognition. We participated in the shared task of Bacteria Biotope 2019, the shared task consists of recognizing and normalizing the biomedical entity mentions. Our linguistically informed named entity recognition system consists of a deep learning based model which can extract both nested and flat entities; our model employed several linguistic features and auxiliary training objectives to enable efficient learning in data-scarce scenarios. Our entity normalization system employed string match, fuzzy search and semantic search to link the extracted named entities to the biomedical databases. Our named entity recognition and entity normalization system achieved the lowest slot error rate of 0.715 and ranked first in the shared task. We also participated in two shared tasks of Adverse Drug Effect Span detection (English) and Profession Span Detection (Spanish); both of these tasks collect data from the social media platform Twitter. We developed a named entity recognition model which can improve the input representation of the model by stacking heterogeneous embeddings from a diverse domain(s); our empirical results demonstrate complementary learning from these heterogeneous embeddings. Our submission ranked 3rd in both of the shared tasks. In the second part of the thesis, we explored synthetic data augmentation strategies to address low-resource information extraction in specialized domains. Specifically, we adapted backtranslation to the token-level task of named entity recognition and sentence-level task of relation extraction. We demonstrate that backtranslation can generate linguistically diverse and grammatically coherent synthetic sentences and serve as a competitive augmentation strategy for the task of named entity recognition and relation extraction. In most of the real-world relation extraction tasks, the annotated data is not available, however, quite often a large unannotated text corpus is available. Bootstrapping methods for relation extraction can operate on this large corpus as they only require a handful of seed instances. However, bootstrapping methods tend to accumulate noise over time (known as semantic drift) and this phenomenon has a drastic negative impact on the final precision of the extractions. We develop two methods to constrain the bootstrapping process to minimise semantic drift for relation extraction; our methods leverage graph theory and pre-trained language models to explicitly identify and remove noisy extraction patterns. We report the experimental results on the TACRED dataset for four relations. In the last part of the thesis, we demonstrate the application of domain adaptation to the challenging task of multi-lingual acronym extraction. Our experiments demonstrate that domain adaptation can improve acronym extraction within scientific and legal domains in 6 languages including low-resource languages such as Persian and Vietnamese

Advances in statistical script learning

When humans encode information into natural language, they do so with the clear assumption that the reader will be able to seamlessly make inferences based on world knowledge. For example, given the sentence ``Mrs. Dalloway said she would buy the flowers herself,'' one can make a number of probable inferences based on event co-occurrences: she bought flowers, she went to a store, she took the flowers home, and so on. Observing this, it is clear that many different useful natural language end-tasks could benefit from models of events as they typically co-occur (so-called script models). Robust question-answering systems must be able to infer highly-probable implicit events from what is explicitly stated in a text, as must robust information-extraction systems that map from unstructured text to formal assertions about relations expressed in the text. Coreference resolution systems, semantic role labeling, and even syntactic parsing systems could, in principle, benefit from event co-occurrence models. To this end, we present a number of contributions related to statistical event co-occurrence models. First, we investigate a method of incorporating multiple entities into events in a count-based co-occurrence model. We find that modeling multiple entities interacting across events allows for improved empirical performance on the task of modeling sequences of events in documents. Second, we give a method of applying Recurrent Neural Network sequence models to the task of predicting held-out predicate-argument structures from documents. This model allows us to easily incorporate entity noun information, and can allow for more complex, higher-arity events than a count-based co-occurrence model. We find the neural model improves performance considerably over the count-based co-occurrence model. Third, we investigate the performance of a sequence-to-sequence encoder-decoder neural model on the task of predicting held-out predicate-argument events from text. This model does not explicitly model any external syntactic information, and does not require a parser. We find the text-level model to be competitive in predictive performance with an event level model directly mediated by an external syntactic analysis. Finally, motivated by this result, we investigate incorporating features derived from these models into a baseline noun coreference resolution system. We find that, while our additional features do not appreciably improve top-level performance, we can nonetheless provide empirical improvement on a number of restricted classes of difficult coreference decisions.Computer Science

Automated Assessment of the Aftermath of Typhoons Using Social Media Texts

Disasters are one of the major threats to economics and human societies, causing substantial losses of human lives, properties and infrastructures. It has been our persistent endeavors to understand, prevent and reduce such disasters, and the popularization of social media is offering new opportunities to enhance disaster management in a crowd-sourcing approach. However, social media data is also characterized by its undue brevity, intense noise, and informality of language. The existing literature has not completely addressed these disadvantages, otherwise vast manual efforts are devoted to tackling these problems. The major focus of this research is on constructing a holistic framework to exploit social media data in typhoon damage assessment. The scope of this research covers data collection, relevance classification, location extraction and damage assessment while assorted approaches are utilized to overcome the disadvantages of social media data. Moreover, a semi-supervised or unsupervised approach is prioritized in forming the framework to minimize manual intervention. In data collection, query expansion strategy is adopted to optimize the search recall of typhoon-relevant information retrieval. Multiple filtering strategies are developed to screen the keywords and maintain the relevance to search topics in the keyword updates. A classifier based on a convolutional neural network is presented for relevance classification, with hashtags and word clusters as extra input channels to augment the information. In location extraction, a model is constructed by integrating Bidirectional Long Short-Time Memory and Conditional Random Fields. Feature noise correction layers and label smoothing are leveraged to handle the noisy training data. Finally, a multi-instance multi-label classifier identifies the damage relations in four categories, and the damage categories of a message are integrated with the damage descriptions score to obtain damage severity score for the message. A case study is conducted to verify the effectiveness of the framework. The outcomes indicate that the approaches and models developed in this study significantly improve in the classification of social media texts especially under the framework of semi-supervised or unsupervised learning. Moreover, the results of damage assessment from social media data are remarkably consistent with the official statistics, which demonstrates the practicality of the proposed damage scoring scheme