TiFi: Taxonomy Induction for Fictional Domains [Extended version]

Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin

CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information

Open Information Extraction (OpenIE) methods extract (noun phrase, relation phrase, noun phrase) triples from text, resulting in the construction of large Open Knowledge Bases (Open KBs). The noun phrases (NPs) and relation phrases in such Open KBs are not canonicalized, leading to the storage of redundant and ambiguous facts. Recent research has posed canonicalization of Open KBs as clustering over manuallydefined feature spaces. Manual feature engineering is expensive and often sub-optimal. In order to overcome this challenge, we propose Canonicalization using Embeddings and Side Information (CESI) - a novel approach which performs canonicalization over learned embeddings of Open KBs. CESI extends recent advances in KB embedding by incorporating relevant NP and relation phrase side information in a principled manner. Through extensive experiments on multiple real-world datasets, we demonstrate CESI's effectiveness.Comment: Accepted at WWW 201

Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings

We consider the task of inferring is-a relationships from large text corpora. For this purpose, we propose a new method combining hyperbolic embeddings and Hearst patterns. This approach allows us to set appropriate constraints for inferring concept hierarchies from distributional contexts while also being able to predict missing is-a relationships and to correct wrong extractions. Moreover -- and in contrast with other methods -- the hierarchical nature of hyperbolic space allows us to learn highly efficient representations and to improve the taxonomic consistency of the inferred hierarchies. Experimentally, we show that our approach achieves state-of-the-art performance on several commonly-used benchmarks

From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

Knowledge extraction from fictional texts

Knowledge extraction from text is a key task in natural language processing, which involves many sub-tasks, such as taxonomy induction, named entity recognition and typing, relation extraction, knowledge canonicalization and so on. By constructing structured knowledge from natural language text, knowledge extraction becomes a key asset for search engines, question answering and other downstream applications. However, current knowledge extraction methods mostly focus on prominent real-world entities with Wikipedia and mainstream news articles as sources. The constructed knowledge bases, therefore, lack information about long-tail domains, with fiction and fantasy as archetypes. Fiction and fantasy are core parts of our human culture, spanning from literature to movies, TV series, comics and video games. With thousands of fictional universes which have been created, knowledge from fictional domains are subject of search-engine queries - by fans as well as cultural analysts. Unlike the real-world domain, knowledge extraction on such specific domains like fiction and fantasy has to tackle several key challenges: - Training data: Sources for fictional domains mostly come from books and fan-built content, which is sparse and noisy, and contains difficult structures of texts, such as dialogues and quotes. Training data for key tasks such as taxonomy induction, named entity typing or relation extraction are also not available. - Domain characteristics and diversity: Fictional universes can be highly sophisticated, containing entities, social structures and sometimes languages that are completely different from the real world. State-of-the-art methods for knowledge extraction make assumptions on entity-class, subclass and entity-entity relations that are often invalid for fictional domains. With different genres of fictional domains, another requirement is to transfer models across domains. - Long fictional texts: While state-of-the-art models have limitations on the input sequence length, it is essential to develop methods that are able to deal with very long texts (e.g. entire books), to capture multiple contexts and leverage widely spread cues. This dissertation addresses the above challenges, by developing new methodologies that advance the state of the art on knowledge extraction in fictional domains. - The first contribution is a method, called TiFi, for constructing type systems (taxonomy induction) for fictional domains. By tapping noisy fan-built content from online communities such as Wikia, TiFi induces taxonomies through three main steps: category cleaning, edge cleaning and top-level construction. Exploiting a variety of features from the original input, TiFi is able to construct taxonomies for a diverse range of fictional domains with high precision. - The second contribution is a comprehensive approach, called ENTYFI, for named entity recognition and typing in long fictional texts. Built on 205 automatically induced high-quality type systems for popular fictional domains, ENTYFI exploits the overlap and reuse of these fictional domains on unseen texts. By combining different typing modules with a consolidation stage, ENTYFI is able to do fine-grained entity typing in long fictional texts with high precision and recall. - The third contribution is an end-to-end system, called KnowFi, for extracting relations between entities in very long texts such as entire books. KnowFi leverages background knowledge from 142 popular fictional domains to identify interesting relations and to collect distant training samples. KnowFi devises a similarity-based ranking technique to reduce false positives in training samples and to select potential text passages that contain seed pairs of entities. By training a hierarchical neural network for all relations, KnowFi is able to infer relations between entity pairs across long fictional texts, and achieves gains over the best prior methods for relation extraction.Wissensextraktion ist ein Schlüsselaufgabe bei der Verarbeitung natürlicher Sprache, und umfasst viele Unteraufgaben, wie Taxonomiekonstruktion, Entitätserkennung und Typisierung, Relationsextraktion, Wissenskanonikalisierung, etc. Durch den Aufbau von strukturiertem Wissen (z.B. Wissensdatenbanken) aus Texten wird die Wissensextraktion zu einem Schlüsselfaktor für Suchmaschinen, Question Answering und andere Anwendungen. Aktuelle Methoden zur Wissensextraktion konzentrieren sich jedoch hauptsächlich auf den Bereich der realen Welt, wobei Wikipedia und Mainstream- Nachrichtenartikel die Hauptquellen sind. Fiktion und Fantasy sind Kernbestandteile unserer menschlichen Kultur, die sich von Literatur bis zu Filmen, Fernsehserien, Comics und Videospielen erstreckt. Für Tausende von fiktiven Universen wird Wissen aus Suchmaschinen abgefragt – von Fans ebenso wie von Kulturwissenschaftler. Im Gegensatz zur realen Welt muss die Wissensextraktion in solchen spezifischen Domänen wie Belletristik und Fantasy mehrere zentrale Herausforderungen bewältigen: • Trainingsdaten. Quellen für fiktive Domänen stammen hauptsächlich aus Büchern und von Fans erstellten Inhalten, die spärlich und fehlerbehaftet sind und schwierige Textstrukturen wie Dialoge und Zitate enthalten. Trainingsdaten für Schlüsselaufgaben wie Taxonomie-Induktion, Named Entity Typing oder Relation Extraction sind ebenfalls nicht verfügbar. • Domain-Eigenschaften und Diversität. Fiktive Universen können sehr anspruchsvoll sein und Entitäten, soziale Strukturen und manchmal auch Sprachen enthalten, die sich von der realen Welt völlig unterscheiden. Moderne Methoden zur Wissensextraktion machen Annahmen über Entity-Class-, Entity-Subclass- und Entity- Entity-Relationen, die für fiktive Domänen oft ungültig sind. Bei verschiedenen Genres fiktiver Domänen müssen Modelle auch über fiktive Domänen hinweg transferierbar sein. • Lange fiktive Texte. Während moderne Modelle Einschränkungen hinsichtlich der Länge der Eingabesequenz haben, ist es wichtig, Methoden zu entwickeln, die in der Lage sind, mit sehr langen Texten (z.B. ganzen Büchern) umzugehen, und mehrere Kontexte und verteilte Hinweise zu erfassen. Diese Dissertation befasst sich mit den oben genannten Herausforderungen, und entwickelt Methoden, die den Stand der Kunst zur Wissensextraktion in fiktionalen Domänen voranbringen. • Der erste Beitrag ist eine Methode, genannt TiFi, zur Konstruktion von Typsystemen (Taxonomie induktion) für fiktive Domänen. Aus von Fans erstellten Inhalten in Online-Communities wie Wikia induziert TiFi Taxonomien in drei wesentlichen Schritten: Kategoriereinigung, Kantenreinigung und Top-Level- Konstruktion. TiFi nutzt eine Vielzahl von Informationen aus den ursprünglichen Quellen und ist in der Lage, Taxonomien für eine Vielzahl von fiktiven Domänen mit hoher Präzision zu erstellen. • Der zweite Beitrag ist ein umfassender Ansatz, genannt ENTYFI, zur Erkennung von Entitäten, und deren Typen, in langen fiktiven Texten. Aufbauend auf 205 automatisch induzierten hochwertigen Typsystemen für populäre fiktive Domänen nutzt ENTYFI die Überlappung und Wiederverwendung dieser fiktiven Domänen zur Bearbeitung neuer Texte. Durch die Zusammenstellung verschiedener Typisierungsmodule mit einer Konsolidierungsphase ist ENTYFI in der Lage, in langen fiktionalen Texten eine feinkörnige Entitätstypisierung mit hoher Präzision und Abdeckung durchzuführen. • Der dritte Beitrag ist ein End-to-End-System, genannt KnowFi, um Relationen zwischen Entitäten aus sehr langen Texten wie ganzen Büchern zu extrahieren. KnowFi nutzt Hintergrundwissen aus 142 beliebten fiktiven Domänen, um interessante Beziehungen zu identifizieren und Trainingsdaten zu sammeln. KnowFi umfasst eine ähnlichkeitsbasierte Ranking-Technik, um falsch positive Einträge in Trainingsdaten zu reduzieren und potenzielle Textpassagen auszuwählen, die Paare von Kandidats-Entitäten enthalten. Durch das Trainieren eines hierarchischen neuronalen Netzwerkes für alle Relationen ist KnowFi in der Lage, Relationen zwischen Entitätspaaren aus langen fiktiven Texten abzuleiten, und übertrifft die besten früheren Methoden zur Relationsextraktion