31 research outputs found

    Ekstraksi Relasi Antar Entitas di Bahasa Indonesia Menggunakan Neural Network

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    Dengan perkembangan zaman yang begitu pesat, berdampak pada perkembangan data pula. Salah satu bentuk data yang paling banyak saat ini berupa data tekstual seperti artikel sederhana maupun dokumen lain yang terdapat di internet. Agar data tekstual tersebut dapat dimengerti dan dimanfaatkan dengan baik oleh manusia, maka perlu di proses dan disederhanakan agar menjadi informasi yang ringkas dan jelas. Oleh karena itu, semakin berkembang pula penelitian dalam bidang Information Extraction (IE) dan salah satu contoh penelitian di IE adalah Relation Extraction (RE). Penelitian RE sudah banyak dilakukan terutama pada Bahasa Inggris dimana resourcenya sudah termasuk banyak. Metode yang digunakan pun bermacam-macam seperti kernel, tree kernel, support vector machine, long short-term memory, convulution recurrent neural network, dan lain sebagainya. Pada penelitian kali ini adalah penelitian RE pada Bahasa Indonesia dengan menggunakan metode convulution recurrent neural network yang sudah dipergunakan untuk RE Bahasa Inggris. Dataset yang digunakan pada penelitian ini adalah dataset Bahasa Indonesia yang berasal dari file xml wikipedia. File xml wikipedia ini kemudian diproses sehingga menghasilkan dataset seperti yang digunakan pada CRNN dalam Bahasa inggris yaitu dalam format SemEval-2 Task 8. Uji coba dilakukan dengan berbagai macam perbandingan data training dan testing yaitu 80:20, 70:30, dan 60:40. Selain itu, parameter pooling untuk CRNN yang digunakan ada dua macam yaitu ‘att’ dan ‘max’. Dari uji coba yang dilakukan, hasil yang didapatkan adalah bervariasi mulai dari mendekati maupun lebih baik bila dibandingkan dengan CRNN dengan menggunakan dataset Bahasa inggris sehingga dapat disimpulkan bahwa dengan CRNN ini bisa digunakan untuk proses RE pada Bahasa Indonesia apabila dataset yang digunakan sesuai dengan penelitian sebelumnya

    An ontology for human-like interaction systems

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    This report proposes and describes the development of a Ph.D. Thesis aimed at building an ontological knowledge model supporting Human-Like Interaction systems. The main function of such knowledge model in a human-like interaction system is to unify the representation of each concept, relating it to the appropriate terms, as well as to other concepts with which it shares semantic relations. When developing human-like interactive systems, the inclusion of an ontological module can be valuable for both supporting interaction between participants and enabling accurate cooperation of the diverse components of such an interaction system. On one hand, during human communication, the relation between cognition and messages relies in formalization of concepts, linked to terms (or words) in a language that will enable its utterance (at the expressive layer). Moreover, each participant has a unique conceptualization (ontology), different from other individual’s. Through interaction, is the intersection of both part’s conceptualization what enables communication. Therefore, for human-like interaction is crucial to have a strong conceptualization, backed by a vast net of terms linked to its concepts, and the ability of mapping it with any interlocutor’s ontology to support denotation. On the other hand, the diverse knowledge models comprising a human-like interaction system (situation model, user model, dialogue model, etc.) and its interface components (natural language processor, voice recognizer, gesture processor, etc.) will be continuously exchanging information during their operation. It is also required for them to share a solid base of references to concepts, providing consistency, completeness and quality to their processing. Besides, humans usually handle a certain range of similar concepts they can use when building messages. The subject of similarity has been and continues to be widely studied in the fields and literature of computer science, psychology and sociolinguistics. Good similarity measures are necessary for several techniques from these fields such as information retrieval, clustering, data-mining, sense disambiguation, ontology translation and automatic schema matching. Furthermore, the ontological component should also be able to perform certain inferential processes, such as the calculation of semantic similarity between concepts. The principal benefit gained from this procedure is the ability to substitute one concept for another based on a calculation of the similarity of the two, given specific circumstances. From the human’s perspective, the procedure enables referring to a given concept in cases where the interlocutor either does not know the term(s) initially applied to refer that concept, or does not know the concept itself. In the first case, the use of synonyms can do, while in the second one it will be necessary to refer the concept from some other similar (semantically-related) concepts...Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaSecretario: Inés María Galván León.- Secretario: José María Cavero Barca.- Vocal: Yolanda García Rui

    Integrating Distributional, Compositional, and Relational Approaches to Neural Word Representations

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    When the field of natural language processing (NLP) entered the era of deep neural networks, the task of representing basic units of language, an inherently sparse and symbolic medium, using low-dimensional dense real-valued vectors, or embeddings, became crucial. The dominant technique to perform this task has for years been to segment input text sequences into space-delimited words, for which embeddings are trained over a large corpus by means of leveraging distributional information: a word is reducible to the set of contexts it appears in. This approach is powerful but imperfect; words not seen during the embedding learning phase, known as out-of-vocabulary words (OOVs), emerge in any plausible application where embeddings are used. One approach applied in order to combat this and other shortcomings is the incorporation of compositional information obtained from the surface form of words, enabling the representation of morphological regularities and increasing robustness to typographical errors. Another approach leverages word-sense information and relations curated in large semantic graph resources, offering a supervised signal for embedding space structure and improving representations for domain-specific rare words. In this dissertation, I offer several analyses and remedies for the OOV problem based on the utilization of character-level compositional information in multiple languages and the structure of semantic knowledge in English. In addition, I provide two novel datasets for the continued exploration of vocabulary expansion in English: one with a taxonomic emphasis on novel word formation, and the other generated by a real-world data-driven use case in the entity graph domain. Finally, recognizing the recent shift in NLP towards contextualized representations of subword tokens, I describe the form in which the OOV problem still appears in these methods, and apply an integrative compositional model to address it.Ph.D

    Commonsense knowledge acquisition and applications

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    Computers are increasingly expected to make smart decisions based on what humans consider commonsense. This would require computers to understand their environment, including properties of objects in the environment (e.g., a wheel is round), relations between objects (e.g., two wheels are part of a bike, or a bike is slower than a car) and interactions of objects (e.g., a driver drives a car on the road). The goal of this dissertation is to investigate automated methods for acquisition of large-scale, semantically organized commonsense knowledge. Prior state-of-the-art methods to acquire commonsense are either not automated or based on shallow representations. Thus, they cannot produce large-scale, semantically organized commonsense knowledge. To achieve the goal, we divide the problem space into three research directions, constituting our core contributions: 1. Properties of objects: acquisition of properties like hasSize, hasShape, etc. We develop WebChild, a semi-supervised method to compile semantically organized properties. 2. Relationships between objects: acquisition of relations like largerThan, partOf, memberOf, etc. We develop CMPKB, a linear-programming based method to compile comparative relations, and, we develop PWKB, a method based on statistical and logical inference to compile part-whole relations. 3. Interactions between objects: acquisition of activities like drive a car, park a car, etc., with attributes such as temporal or spatial attributes. We develop Knowlywood, a method based on semantic parsing and probabilistic graphical models to compile activity knowledge. Together, these methods result in the construction of a large, clean and semantically organized Commonsense Knowledge Base that we call WebChild KB.Von Computern wird immer mehr erwartet, dass sie kluge Entscheidungen treffen können, basierend auf Allgemeinwissen. Dies setzt voraus, dass Computer ihre Umgebung, einschließlich der Eigenschaften von Objekten (z. B. das Rad ist rund), Beziehungen zwischen Objekten (z. B. ein Fahrrad hat zwei Räder, ein Fahrrad ist langsamer als ein Auto) und Interaktionen von Objekten (z. B. ein Fahrer fährt ein Auto auf der Straße), verstehen können. Das Ziel dieser Dissertation ist es, automatische Methoden für die Erfassung von großmaßstäblichem, semantisch organisiertem Allgemeinwissen zu schaffen. Dies ist schwierig aufgrund folgender Eigenschaften des Allgemeinwissens. Es ist: (i) implizit und spärlich, da Menschen nicht explizit das Offensichtliche ausdrücken, (ii) multimodal, da es über textuelle und visuelle Inhalte verteilt ist, (iii) beeinträchtigt vom Einfluss des Berichtenden, da ungewöhnliche Fakten disproportional häufig berichtet werden, (iv) Kontextabhängig, und hat aus diesem Grund eine eingeschränkte statistische Konfidenz. Vorherige Methoden, auf diesem Gebiet sind entweder nicht automatisiert oder basieren auf flachen Repräsentationen. Daher können sie kein großmaßstäbliches, semantisch organisiertes Allgemeinwissen erzeugen. Um unser Ziel zu erreichen, teilen wir den Problemraum in drei Forschungsrichtungen, welche den Hauptbeitrag dieser Dissertation formen: 1. Eigenschaften von Objekten: Erfassung von Eigenschaften wie hasSize, hasShape, usw. Wir entwickeln WebChild, eine halbüberwachte Methode zum Erfassen semantisch organisierter Eigenschaften. 2. Beziehungen zwischen Objekten: Erfassung von Beziehungen wie largerThan, partOf, memberOf, usw. Wir entwickeln CMPKB, eine Methode basierend auf linearer Programmierung um vergleichbare Beziehungen zu erfassen. Weiterhin entwickeln wir PWKB, eine Methode basierend auf statistischer und logischer Inferenz welche zugehörigkeits Beziehungen erfasst. 3. Interaktionen zwischen Objekten: Erfassung von Aktivitäten, wie drive a car, park a car, usw. mit temporalen und räumlichen Attributen. Wir entwickeln Knowlywood, eine Methode basierend auf semantischem Parsen und probabilistischen grafischen Modellen um Aktivitätswissen zu erfassen. Als Resultat dieser Methoden erstellen wir eine große, saubere und semantisch organisierte Allgemeinwissensbasis, welche wir WebChild KB nennen

    Commonsense knowledge acquisition and applications

    Get PDF
    Computers are increasingly expected to make smart decisions based on what humans consider commonsense. This would require computers to understand their environment, including properties of objects in the environment (e.g., a wheel is round), relations between objects (e.g., two wheels are part of a bike, or a bike is slower than a car) and interactions of objects (e.g., a driver drives a car on the road). The goal of this dissertation is to investigate automated methods for acquisition of large-scale, semantically organized commonsense knowledge. Prior state-of-the-art methods to acquire commonsense are either not automated or based on shallow representations. Thus, they cannot produce large-scale, semantically organized commonsense knowledge. To achieve the goal, we divide the problem space into three research directions, constituting our core contributions: 1. Properties of objects: acquisition of properties like hasSize, hasShape, etc. We develop WebChild, a semi-supervised method to compile semantically organized properties. 2. Relationships between objects: acquisition of relations like largerThan, partOf, memberOf, etc. We develop CMPKB, a linear-programming based method to compile comparative relations, and, we develop PWKB, a method based on statistical and logical inference to compile part-whole relations. 3. Interactions between objects: acquisition of activities like drive a car, park a car, etc., with attributes such as temporal or spatial attributes. We develop Knowlywood, a method based on semantic parsing and probabilistic graphical models to compile activity knowledge. Together, these methods result in the construction of a large, clean and semantically organized Commonsense Knowledge Base that we call WebChild KB.Von Computern wird immer mehr erwartet, dass sie kluge Entscheidungen treffen können, basierend auf Allgemeinwissen. Dies setzt voraus, dass Computer ihre Umgebung, einschließlich der Eigenschaften von Objekten (z. B. das Rad ist rund), Beziehungen zwischen Objekten (z. B. ein Fahrrad hat zwei Räder, ein Fahrrad ist langsamer als ein Auto) und Interaktionen von Objekten (z. B. ein Fahrer fährt ein Auto auf der Straße), verstehen können. Das Ziel dieser Dissertation ist es, automatische Methoden für die Erfassung von großmaßstäblichem, semantisch organisiertem Allgemeinwissen zu schaffen. Dies ist schwierig aufgrund folgender Eigenschaften des Allgemeinwissens. Es ist: (i) implizit und spärlich, da Menschen nicht explizit das Offensichtliche ausdrücken, (ii) multimodal, da es über textuelle und visuelle Inhalte verteilt ist, (iii) beeinträchtigt vom Einfluss des Berichtenden, da ungewöhnliche Fakten disproportional häufig berichtet werden, (iv) Kontextabhängig, und hat aus diesem Grund eine eingeschränkte statistische Konfidenz. Vorherige Methoden, auf diesem Gebiet sind entweder nicht automatisiert oder basieren auf flachen Repräsentationen. Daher können sie kein großmaßstäbliches, semantisch organisiertes Allgemeinwissen erzeugen. Um unser Ziel zu erreichen, teilen wir den Problemraum in drei Forschungsrichtungen, welche den Hauptbeitrag dieser Dissertation formen: 1. Eigenschaften von Objekten: Erfassung von Eigenschaften wie hasSize, hasShape, usw. Wir entwickeln WebChild, eine halbüberwachte Methode zum Erfassen semantisch organisierter Eigenschaften. 2. Beziehungen zwischen Objekten: Erfassung von Beziehungen wie largerThan, partOf, memberOf, usw. Wir entwickeln CMPKB, eine Methode basierend auf linearer Programmierung um vergleichbare Beziehungen zu erfassen. Weiterhin entwickeln wir PWKB, eine Methode basierend auf statistischer und logischer Inferenz welche zugehörigkeits Beziehungen erfasst. 3. Interaktionen zwischen Objekten: Erfassung von Aktivitäten, wie drive a car, park a car, usw. mit temporalen und räumlichen Attributen. Wir entwickeln Knowlywood, eine Methode basierend auf semantischem Parsen und probabilistischen grafischen Modellen um Aktivitätswissen zu erfassen. Als Resultat dieser Methoden erstellen wir eine große, saubere und semantisch organisierte Allgemeinwissensbasis, welche wir WebChild KB nennen

    Investigating the universality of a semantic web-upper ontology in the context of the African languages

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    Ontologies are foundational to, and upper ontologies provide semantic integration across, the Semantic Web. Multilingualism has been shown to be a key challenge to the development of the Semantic Web, and is a particular challenge to the universality requirement of upper ontologies. Universality implies a qualitative mapping from lexical ontologies, like WordNet, to an upper ontology, such as SUMO. Are a given natural language family's core concepts currently included in an existing, accepted upper ontology? Does SUMO preserve an ontological non-bias with respect to the multilingual challenge, particularly in the context of the African languages? The approach to developing WordNets mapped to shared core concepts in the non-Indo-European language families has highlighted these challenges and this is examined in a unique new context: the Southern African languages. This is achieved through a new mapping from African language core concepts to SUMO. It is shown that SUMO has no signi ficant natural language ontology bias.ComputingM. Sc. (Computer Science
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