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Measuring Semantic Relatedness Using Salient Encyclopedic ConceptsHassan, Samer 08 1900 (has links)
While pragmatics, through its integration of situational awareness and real world relevant knowledge, offers a high level of analysis that is suitable for real interpretation of natural dialogue, semantics, on the other end, represents a lower yet more tractable and affordable linguistic level of analysis using current technologies. Generally, the understanding of semantic meaning in literature has revolved around the famous quote ``You shall know a word by the company it keeps''. In this thesis we investigate the role of context constituents in decoding the semantic meaning of the engulfing context; specifically we probe the role of salient concepts, defined as content-bearing expressions which afford encyclopedic definitions, as a suitable source of semantic clues to an unambiguous interpretation of context. Furthermore, we integrate this world knowledge in building a new and robust unsupervised semantic model and apply it to entail semantic relatedness between textual pairs, whether they are words, sentences or paragraphs. Moreover, we explore the abstraction of semantics across languages and utilize our findings into building a novel multi-lingual semantic relatedness model exploiting information acquired from various languages. We demonstrate the effectiveness and the superiority of our mono-lingual and multi-lingual models through a comprehensive set of evaluations on specialized synthetic datasets for semantic relatedness as well as real world applications such as paraphrase detection and short answer grading. Our work represents a novel approach to integrate world-knowledge into current semantic models and a means to cross the language boundary for a better and more robust semantic relatedness representation, thus opening the door for an improved abstraction of meaning that carries the potential of ultimately imparting understanding of natural language to machines.
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Using Explicit Semantic Analysis to Link in Multi-Lingual Document Collections / Using Explicit Semantic Analysis to Link in Multi-Lingual Document CollectionsŽilka, Lukáš January 2012 (has links)
Udržování prolinkování dokumentů v ryhle rostoucích kolekcích je problematické. To je dále zvětšeno vícejazyčností těchto kolekcí. Navrhujeme použít Explicitní Sémantickou Analýzu k identifikaci relevantních dokumentů a linků napříč jazyky, bez použití strojového překladu. Navrhli jsme a implementovali několik přistupů v prototypu linkovacího systému. Evaluace byla provedena na Čínské, České, Anglické a Španělské Wikipedii. Diskutujeme evaluační metodologii pro linkovací systémy, a hodnotíme souhlasnost mezi odkazy v různých jazykoých verzích Wikipedie. Hodnotíme vlastnosti Explicitní Sémantické Analýzy důležité pro její praktické použití.
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