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Recently, much research focuses on event s-toryline generation, which aims to produce a concise, global and temporal event summary from a collection of articles. Generally, each event contains multiple sub-events and the sto-ryline should be composed by the component summaries of all the sub-events. However, different sub-events have different part-whole(More)
With the popularity of Web 2.0, comments left by readers on web documents have drawn much attention. In this paper, we study the problem of comments-oriented document summarization, which aims to summarize a web document by considering not only its content but also the comments. Generally, most of the comments usually convey one or a few aspects of the(More)
At the basis of using Arnold to replace pixel, this algorithm draw into Logistic map to generate a chaotic sequence, which is used as the Henon map parameters and the number of iterations, then through the chain iteration of Henon map and XOR to encrypt the grey value. After that, the image pixel value is uniform distributed. The test practice and(More)
We propose a brand new " Liberal " Event Extraction paradigm to extract events and discover event schemas from any input corpus simultaneously. We incorporate symbolic (e.g., Abstract Meaning Representation) and distributional semantics to detect and represent event structures and adopt a joint typing framework to simultaneously extract event types and(More)
Event detection remains a challenge due to the difficulty at encoding the word semantics in various contexts. Previous approaches heavily depend on language-specific knowledge and pre-existing natural language processing (NLP) tools. However, compared to English, not all languages have such resources and tools available. A more promising approach is to(More)
Distant supervision has been widely used in current systems of fine-grained entity typing to automatically assign categories (entity types) to entity mentions. However, the types so obtained from knowledge bases are often incorrect for the entity mention's local context. This paper proposes a novel embedding method to separately model " clean " and " noisy(More)
Most existing learning to rank based summarization methods only used content relevance of sentences with respect to queries to rank or estimate sentences, while neglecting sentence relationships. In our work, we propose a novel model, RelationListwise, by integrating relation information among all the estimated sentences into listMLE-Top K, a basic listwise(More)
Recent research has shown great progress on fine-grained entity typing. Most existing methods require pre-defining a set of types and training a multi-class classifier from a large labeled data set based on multi-level linguistic features. They are thus limited to certain domains, genres and languages. In this paper, we propose a novel unsupervised entity(More)