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Semantic similarity measures play important roles in information retrieval and Natural Language Processing. Previous work in semantic web-related applications such as community mining, relation extraction, automatic meta data extraction have used various semantic similarity measures. Despite the usefulness of semantic similarity measures in these(More)
—Measuring the semantic similarity between words is an important component in various tasks on the web such as relation extraction, community mining, document clustering, and automatic meta data extraction. Despite the usefulness of semantic similarity measures in these applications, accurately measuring semantic similarity between two words (or entities)(More)
Several recent discourse parsers have employed fully-supervised machine learning approaches. These methods require human an-notators to beforehand create an extensive training corpus, which is a time-consuming and costly process. On the other hand, un-labeled data is abundant and cheap to collect. In this paper, we propose a novel semi-supervised method for(More)
Measuring the similarity between semantic relations that hold among entities is an important and necessary step in various Web related tasks such as relation extraction, information retrieval and analogy detection. For example, consider the case in which a person knows a pair of entities (e.g. Google, YouTube), between which a particular relation holds(More)
Ordering information is a difficult but important task for applications generating natural-language text. We present a bottom-up approach to arranging sentences extracted for multi-document sum-marization. To capture the association and order of two textual segments (eg, sentences), we define four criteria, chronology , topical-closeness, precedence, and(More)
Measuring semantic similarity between words is vital for various applications in natural language processing, such as language modeling, information retrieval, and document clustering. We propose a method that utilizes the information available on the Web to measure semantic similarity between a pair of words or entities. We integrate page counts for each(More)
Identifying discourse relations in a text is essential for various tasks in Natural Language Processing, such as automatic text summa-rization, question-answering, and dialogue generation. The first step of this process is segmenting a text into elementary units. In this paper, we present a novel model of discourse segmentation based on sequential data(More)
Semantic similarity is a central concept that extends across numerous fields such as artificial intelligence, natural language processing, cognitive science and psychology. Accurate measurement of semantic similarity between words is essential for various tasks such as, document clustering , information retrieval, and synonym extraction. We propose a novel(More)
Latent relational search (LRS) is a novel approach for mapping knowledge across two domains. Given a source domain knowledge concerning the Moon, " The Moon is a satellite of the Earth " , one can form a question {(Moon, Earth), (Ganymede, ?)} to query an LRS engine for new knowledge in the target domain concerning the Ganymede. An LRS engine relies on some(More)
—Ranking the set of search results according to their relevance to a user query is an important task in an Information Retrieval (IR) systems such as a Web Search Engine. Learning the optimal ranking function for this task is a challenging problem because one must consider complex non-linear interactions between numerous factors such as the novelty,(More)