Linking named entities to Wikipedia

Natural language is fraught with problems of ambiguity, including name reference. A name in text can refer to multiple entities just as an entity can be known by different names. This thesis examines how a mention in text can be linked to an external knowledge base (KB), in our case, Wikipedia. The named entity linking (NEL) task requires systems to identify the KB entry, or Wikipedia article, that a mention refers to; or, if the KB does not contain the correct entry, return NIL. Entity linking systems can be complex and we present a framework for analysing their different components, which we use to analyse three seminal systems which are evaluated on a common dataset and we show the importance of precise search for linking. The Text Analysis Conference (TAC) is a major venue for NEL research. We report on our submissions to the entity linking shared task in 2010, 2011 and 2012. The information required to disambiguate entities is often found in the text, close to the mention. We explore apposition, a common way for authors to provide information about entities. We model syntactic and semantic restrictions with a joint model that achieves state-of-the-art apposition extraction performance. We generalise from apposition to examine local descriptions specified close to the mention. We add local description to our state-of-the-art linker by using patterns to extract the descriptions and matching against this restricted context. Not only does this make for a more precise match, we are also able to model failure to match. Local descriptions help disambiguate entities, further improving our state-of-the-art linker. The work in this thesis seeks to link textual entity mentions to knowledge bases. Linking is important for any task where external world knowledge is used and resolving ambiguity is fundamental to advancing research into these problems

Deep learning methods for knowledge base population

Knowledge bases store structured information about entities or concepts of the world and can be used in various applications, such as information retrieval or question answering. A major drawback of existing knowledge bases is their incompleteness. In this thesis, we explore deep learning methods for automatically populating them from text, addressing the following tasks: slot filling, uncertainty detection and type-aware relation extraction. Slot filling aims at extracting information about entities from a large text corpus. The Text Analysis Conference yearly provides new evaluation data in the context of an international shared task. We develop a modular system to address this challenge. It was one of the top-ranked systems in the shared task evaluations in 2015. For its slot filler classification module, we propose contextCNN, a convolutional neural network based on context splitting. It improves the performance of the slot filling system by 5.0% micro and 2.9% macro F1. To train our binary and multiclass classification models, we create a dataset using distant supervision and reduce the number of noisy labels with a self-training strategy. For model optimization and evaluation, we automatically extract a labeled benchmark for slot filler classification from the manual shared task assessments from 2012-2014. We show that results on this benchmark are correlated with slot filling pipeline results with a Pearson's correlation coefficient of 0.89 (0.82) on data from 2013 (2014). The combination of patterns, support vector machines and contextCNN achieves the best results on the benchmark with a micro (macro) F1 of 51% (53%) on test. Finally, we analyze the results of the slot filling pipeline and the impact of its components. For knowledge base population, it is essential to assess the factuality of the statements extracted from text. From the sentence "Obama was rumored to be born in Kenya", a system should not conclude that Kenya is the place of birth of Obama. Therefore, we address uncertainty detection in the second part of this thesis. We investigate attention-based models and make a first attempt to systematize the attention design space. Moreover, we propose novel attention variants: External attention, which incorporates an external knowledge source, k-max average attention, which only considers the vectors with the k maximum attention weights, and sequence-preserving attention, which allows to maintain order information. Our convolutional neural network with external k-max average attention sets the new state of the art on a Wikipedia benchmark dataset with an F1 score of 68%. To the best of our knowledge, we are the first to integrate an uncertainty detection component into a slot filling pipeline. It improves precision by 1.4% and micro F1 by 0.4%. In the last part of the thesis, we investigate type-aware relation extraction with neural networks. We compare different models for joint entity and relation classification: pipeline models, jointly trained models and globally normalized models based on structured prediction. First, we show that using entity class prediction scores instead of binary decisions helps relation classification. Second, joint training clearly outperforms pipeline models on a large-scale distantly supervised dataset with fine-grained entity classes. It improves the area under the precision-recall curve from 0.53 to 0.66. Third, we propose a model with a structured prediction output layer, which globally normalizes the score of a triple consisting of the classes of two entities and the relation between them. It improves relation extraction results by 4.4% F1 on a manually labeled benchmark dataset. Our analysis shows that the model learns correct correlations between entity and relation classes. Finally, we are the first to use neural networks for joint entity and relation classification in a slot filling pipeline. The jointly trained model achieves the best micro F1 score with a score of 22% while the neural structured prediction model performs best in terms of macro F1 with a score of 25%

Towards Population of Knowledge Bases from Conversational Sources

With an increasing amount of data created daily, it is challenging for users to organize and discover information from massive collections of digital content (e.g., text and speech). The population of knowledge bases requires linking information from unstructured sources (e.g., news articles and web pages) to structured external knowledge bases (e.g., Wikipedia), which has the potential to advance information archiving and access, and to support knowledge discovery and reasoning. Because of the complexity of this task, knowledge base population is composed of multiple sub-tasks, including the entity linking task, defined as linking the mention of entities (e.g., persons, organizations, and locations) found in documents to their referents in external knowledge bases and the event task, defined as extracting related information for events that should be entered in the knowledge base. Most prior work on tasks related to knowledge base population has focused on dissemination-oriented sources written in the third person (e.g., new articles) that benefit from two characteristics: the content is written in formal language and is to some degree self-contextualized, and the entities mentioned (e.g., persons) are likely to be widely known to the public so that rich information can be found from existing general knowledge bases (e.g., Wikipedia and DBpedia). The work proposed in this thesis focuses on tasks related to knowledge base population for conversational sources written in the first person (e.g., emails and phone recordings), which offers new challenges. One challenge is that most conversations (e.g., 68% of the person names and 53% of the organization names in Enron emails) refer to entities that are known to the conversational participants but not widely known. Thus, existing entity linking techniques relying on general knowledge bases are not appropriate. Another challenge is that some of the shared context between participants in first-person conversations may be implicit and thus challenging to model, increasing the difficulty, even for human annotators, of identifying the true referents. This thesis focuses on several tasks relating to the population of knowledge bases for conversational content: the population of collection-specific knowledge bases for organization entities and meetings from email collections; the entity linking task that resolves the mention of three types of entities (person, organization, and location) found in both conversational text (emails) and speech (phone recordings) sources to multiple knowledge bases, including a general knowledge base built from Wikipedia and collection-specific knowledge bases; the meeting linking task that links meeting-related email messages to the referenced meeting entries in the collection-specific meeting knowledge base; and speaker identification techniques to improve the entity linking task for phone recordings without known speakers. Following the model-based evaluation paradigm, three collections (namely, Enron emails, Avocado emails, and Enron phone recordings) are used as the representations of conversational sources, new test collections are created for each task, and experiments are conducted for each task to evaluate the efficacy of the proposed methods and to provide a comparison to existing state-of-the-art systems. This work has implications in the research fields of e-discovery, scientific collaboration, speaker identification, speech retrieval, and privacy protection

Methods for improving entity linking and exploiting social media messages across crises

Entity Linking (EL) is the task of automatically identifying entity mentions in texts and resolving them to a corresponding entity in a reference knowledge base (KB). There is a large number of tools available for different types of documents and domains, however the literature in entity linking has shown the quality of a tool varies across different corpus and depends on specific characteristics of the corpus it is applied to. Moreover the lack of precision on particularly ambiguous mentions often spoils the usefulness of automated disambiguation results in real world applications. In the first part of this thesis I explore an approximation of the difficulty to link entity mentions and frame it as a supervised classification task. Classifying difficult to disambiguate entity mentions can facilitate identifying critical cases as part of a semi-automated system, while detecting latent corpus characteristics that affect the entity linking performance. Moreover, despiteless the large number of entity linking tools that have been proposed throughout the past years, some tools work better on short mentions while others perform better when there is more contextual information. To this end, I proposed a solution by exploiting results from distinct entity linking tools on the same corpus by leveraging their individual strengths on a per-mention basis. The proposed solution demonstrated to be effective and outperformed the individual entity systems employed in a series of experiments. An important component in the majority of the entity linking tools is the probability that a mentions links to one entity in a reference knowledge base, and the computation of this probability is usually done over a static snapshot of a reference KB. However, an entity’s popularity is temporally sensitive and may change due to short term events. Moreover, these changes might be then reflected in a KB and EL tools can produce different results for a given mention at different times. I investigated the prior probability change over time and the overall disambiguation performance using different KB from different time periods. The second part of this thesis is mainly concerned with short texts. Social media has become an integral part of the modern society. Twitter, for instance, is one of the most popular social media platforms around the world that enables people to share their opinions and post short messages about any subject on a daily basis. At first I presented one approach to identifying informative messages during catastrophic events using deep learning techniques. By automatically detecting informative messages posted by users during major events, it can enable professionals involved in crisis management to better estimate damages with only relevant information posted on social media channels, as well as to act immediately. Moreover I have also performed an analysis study on Twitter messages posted during the Covid-19 pandemic. Initially I collected 4 million tweets posted in Portuguese since the begining of the pandemic and provided an analysis of the debate aroud the pandemic. I used topic modeling, sentiment analysis and hashtags recomendation techniques to provide isights around the online discussion of the Covid-19 pandemic

Thinking outside the graph: scholarly knowledge graph construction leveraging natural language processing

Despite improved digital access to scholarly knowledge in recent decades, scholarly communication remains exclusively document-based. The document-oriented workflows in science publication have reached the limits of adequacy as highlighted by recent discussions on the increasing proliferation of scientific literature, the deficiency of peer-review and the reproducibility crisis. In this form, scientific knowledge remains locked in representations that are inadequate for machine processing. As long as scholarly communication remains in this form, we cannot take advantage of all the advancements taking place in machine learning and natural language processing techniques. Such techniques would facilitate the transformation from pure text based into (semi-)structured semantic descriptions that are interlinked in a collection of big federated graphs. We are in dire need for a new age of semantically enabled infrastructure adept at storing, manipulating, and querying scholarly knowledge. Equally important is a suite of machine assistance tools designed to populate, curate, and explore the resulting scholarly knowledge graph. In this thesis, we address the issue of constructing a scholarly knowledge graph using natural language processing techniques. First, we tackle the issue of developing a scholarly knowledge graph for structured scholarly communication, that can be populated and constructed automatically. We co-design and co-implement the Open Research Knowledge Graph (ORKG), an infrastructure capable of modeling, storing, and automatically curating scholarly communications. Then, we propose a method to automatically extract information into knowledge graphs. With Plumber, we create a framework to dynamically compose open information extraction pipelines based on the input text. Such pipelines are composed from community-created information extraction components in an effort to consolidate individual research contributions under one umbrella. We further present MORTY as a more targeted approach that leverages automatic text summarization to create from the scholarly article's text structured summaries containing all required information. In contrast to the pipeline approach, MORTY only extracts the information it is instructed to, making it a more valuable tool for various curation and contribution use cases. Moreover, we study the problem of knowledge graph completion. exBERT is able to perform knowledge graph completion tasks such as relation and entity prediction tasks on scholarly knowledge graphs by means of textual triple classification. Lastly, we use the structured descriptions collected from manual and automated sources alike with a question answering approach that builds on the machine-actionable descriptions in the ORKG. We propose JarvisQA, a question answering interface operating on tabular views of scholarly knowledge graphs i.e., ORKG comparisons. JarvisQA is able to answer a variety of natural language questions, and retrieve complex answers on pre-selected sub-graphs. These contributions are key in the broader agenda of studying the feasibility of natural language processing methods on scholarly knowledge graphs, and lays the foundation of which methods can be used on which cases. Our work indicates what are the challenges and issues with automatically constructing scholarly knowledge graphs, and opens up future research directions

Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition. Volume 2 : Traitement Automatique des Langues Naturelles

@ 6ème conférence conjointe: JEP-TALN-RECITAL 2020no abstrac

Conflict transformation through international organizations

Entstehende Konflikte mit Ursprung in oder unter Beteilung einer Region, welche durch eine internationale Organisation zusammengehalten wird, tragen einen impliziten Handlungsappell an letztere in sich. Beabsichtigte sowohl als auch unbeabsichtigte Auswirkungen von Hand-lungen als auch Unterlassungen internationaler Organisationen, welche auf diese Weise aus-gelöst wurden, wirken sich umgestaltend auf den gegenständlichen Konflikt aus. Diese Dis-sertation analysiert eingehend die Konflikttransformationsgeschichte der Organisation Amerikanischer Staaten (OAS) auf der einen Seite, sowie jener der Assoziation Südost-asiatischer Staaten (ASEAN) auf der anderen. Während der Zugang durch die OAS ein über-wiegend formaler, legalistischer und faktischer war, verfolgte die ASEAN eine vielmehr in-direkte, jedoch stärker holistisch geprägten Kurs. Die OAS entwickelte unterschiedliche Werkzeuge, um den meist zwischen zwei ihrer Mitglieder entstehenden Konflikten zu begegnen, diese aufzuklären und so auf eine De-eskalation hinzu-wirken. Demgegenüber hat die ASEAN nie den Anspruch auf Verantwortlichkeit über die Schlichtung von überwiegend bilateral ausgerichteten Konflikten erhoben; stattdessen zielte sie darauf ab die Länder inner- sowie außerhalb der Region, welche dazu dispositioniert schienen, Konflikte zu verursachen, verstärkt zu integrieren. Im Laufe der Jahrzehnte änderten beide Organisationen die von ihnen verfolgte Strategie und Sicherheitspolitik. Für die OAS gewann die Frage, welchen Status ihre Mitglieder Demokratie und Menschenrechte zugestanden insofern an Bedeutung, als dieser Umstand zunehmend als Faktor mit Konfliktrelevanz eingeschätzt wurde. Dies führte schließlich dazu, dass die OAS verschiedene Entitäten und gesetzliche Grundlagen zum Zwecke der Beobachtung und des Schutzes von Demokratie und Menschenrechten auf dem amerikanischen Kontinenten schuf. Im Gegensatz dazu erkannte die ASEAN, dass sie regionale sowie globale Konfrontationen am besten dadurch vermeidet, indem sie Länder mit erheblichen Interessen in Südostasien ver-stärkt einbindet. Dazu verfolgte die Assoziation einen zweifache Zugang: einerseits rief sie zahlreiche Plattformen ins Leben, mit dem Ziel das unter den daran teilnehmenden Staaten von inner- und außerhalb der Region, entstehende Vertrauen zu erhöhen. In Hinblick auf Süd-ostasien selbst, verfolgte die ASEAN eifrig eine beschleunigte Umsetzung der von ihren Gründungsvätern ersonnen „Ein Südostasien“-Vision, indem sie darum bestrebt war alle Länder der Region als Mitglieder zu gewinnen, und dies trotz weltweit geäußerter Kritik an dieser Politik. Und obgleich beide Organisationen eine vorhersehbare Neigung zeigten, Konfliktsituationen einzudämmen, so entwickelten sie unterschiedliche Stile, mit denen sie herannahenden Dis-puten begegneten. Wie ausgeführt, sind die hierbei zur Anwendung gelangenden Methoden und Modi bedingt durch die, den Organisationen zugrundeliegenden normativen und ideellen Strukturen. Gemeinsame Ideen und gemeinsames Wissen als auch Identitätsüberlappungen sind entscheidende Kriterien in Hinblick auf Fragen wie, was eine Bedrohung grundsätzlich überhaupt ausmacht, oder wie Sicherheit zu definieren ist. Das zugrundeliegende Verständnis über die Welt und über sich selbst beeinflussen direkt wie Internationale Organisationen auf eine gegebene Konfliktsituation reagieren. Auf die sich entfaltenden Konflikttransformations-erfolge angewandte konstruktivistische Konzepte erlauben, wie gezeigt wurde, wertvolle Ein-sichten, welche über jene die auf die klassischen Erklärungsansätze von Machtbalance und Interessenpolitik zurückführbar sind, hinausgehen.Emerging conflicts emanating from or involving a region bound together through an overarch-ing International Organization in most cases bring with them an implicit call on the latter to act in one way or the other. The intended and unintended impact of International Organiza-tions’ actions and omissions thus precipitated transform the conflict in question as a conse-quence. This dissertation provides a detailed analysis of the conflict transformation history of the Organization of American States (OAS) on the one, and of the Association of Southeast Asian Nations (ASEAN) on the other hand. Whereas the OAS’s approach proved to be pre-dominantly formal, legalistic, and factual, ASEAN followed a more indirect, albeit overall more holistic course. While the OAS was mostly confronted with disputes between two of its members and devel-oped various tools it tasked with bringing clarification and thus hoped-for de-escalation to the various conflicts, ASEAN abstained from claiming responsibility for handling mainly bilaterally conceivable conflicts but instead sought to integrate into its realm regional as well as extra-regional countries that were likely to cause serious controversies. Over the decades both IOs changed the strategy and general security policy they pursued. In the case of the OAS, the status human rights and democracy enjoyed among its members were increasingly seen as factors playing a significant role in the development of many con-flicts. Hence, various bodies and legal documents were established with the aim to monitor and safeguard democratic governance and the protection of human rights in the western hemisphere. In contrast the ASEAN realized that regional and global confrontations are best averted by engaging the countries with considerable interests in the southeast Asian area. Thus ASEAN pursued a twofold approach: it undertook to establish a number of cooperation platforms with the goal to create increasing confidence among its participants located in- and outside of the Association; internally, ASEAN fervently endeavoured to speed up the realization of its found-ing-father’s original vision of One Southeast Asia, bringing all countries under its fold; this despite considerable worldwide criticism to such moves. While both regional entities showed a predictable inclination to ameliorate conflictive situa-tions, each of them evolved a distinct style of tackling approaching disputes. As elaborated the mode and methods developed were – at least partly – conditioned on the Organizations’ un-derlying normative and ideational structures. Shared ideas and knowledge as well as overlaps in identities play a critical role if it comes to establish what constitutes a threat in the first place or how security is to be defined. Such understandings about the world and about oneself then feed directly into the way IOs react to a given conflict situation. As was demonstrated, constructivist thought applied to the unfolded conflict transformational performance delivers valuable insights which go well beyond those derive

Grammatical gender and linguistic complexity : Volume II: World-wide comparative studies

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