Learning semantic classes

Xem 1-9 trên 9 kết quả Learning semantic classes
  • Word Sense Disambiguation suffers from a long-standing problem of knowledge acquisition bottleneck. Although state of the art supervised systems report good accuracies for selected words, they have not been shown to be promising in terms of scalability. In this paper, we present an approach for learning coarser and more general set of concepts from a sense tagged corpus, in order to alleviate the knowledge acquisition bottleneck.

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  • This research explores the idea of inducing domain-specific semantic class taggers using only a domain-specific text collection and seed words. The learning process begins by inducing a classifier that only has access to contextual features, forcing it to generalize beyond the seeds. The contextual classifier then labels new instances, to expand and diversify the training set. Next, a cross-category bootstrapping process simultaneously trains a suite of classifiers for multiple semantic classes. ...

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  • We present a novel approach to weakly supervised semantic class learning from the web, using a single powerful hyponym pattern combined with graph structures, which capture two properties associated with pattern-based extractions: popularity and productivity. Intuitively, a candidate is popular if it was discovered many times by other instances in the hyponym pattern. A candidate is productive if it frequently leads to the discovery of other instances.

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  • We present a technique for automatic induction of slot annotations for subcategorization frames, based on induction of hidden classes in the EM framework of statistical estimation. The models are empirically evalutated by a general decision test. Induction of slot labeling for subcategorization frames is accomplished by a further application of EM, and applied experimentally on frame observations derived from parsing large corpora. We outline an interpretation of the learned representations as theoretical-linguistic decompositional lexical entries. ...

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  • We describe an unsupervised system for learning narrative schemas, coherent sequences or sets of events (arrested(POLICE , SUSPECT), convicted( JUDGE , SUSPECT )) whose arguments are filled with participant semantic roles defined over words (J UDGE = {judge, jury, court}, P OLICE = {police, agent, authorities}). Unlike most previous work in event structure or semantic role learning, our system does not use supervised techniques, hand-built knowledge, or predefined classes of events or roles.

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  • We apply machine learning techniques to classify automatically a set of verbs into lexical semantic classes, based on distributional approximations of diatheses, extracted from a very large annotated corpus. Distributions of four grammatical features are sufficient to reduce error rate by 50% over chance. We conclude that corpus data is a usable repository of verb class information, and that corpus-driven extraction of grammatical features is a promising methodology for automatic lexical acquisition. ...

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  • I have noticed over the years that students have great deal of difficulty dealing with composite and abstract data types. Therefore we are going spend an extra lab review material we have already learned. Vector data type, a variation of array, will be introduced as well. Vector data type (Vector Class) can be used when you need an array that grows dynamically. However, C++ does not allow us to declare size of an array dynamically like some other languages. It is important for you to declare the maximum size you will need. Suppose you are writing a program to keep...

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  • The EM clustering algorithm (Hofmann and Puzicha, 1998) used here is an unsupervised machine learning algorithm that has been applied in many NLP tasks, such as inducing a semantically labeled lexicon and determining lexical choice in machine translation (Rooth et al., 1998), automatic acquisition of verb semantic classes (Schulte im Walde, 2000) and automatic semantic labeling (Gildea and Jurafsky, 2002).

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  • Understanding Statements A statement is a command that performs an action. Statements are found inside methods. You'll learn more about methods in Chapter 3, “Writing Methods and Applying Scope,” but for now, think of a method as a named sequence of statements inside a class. Main, which was introduced in the previous chapter, is an example of a method. Statements in C# must follow a well-defined set of rules. These rules are collectively known as syntax. (In contrast, the specification of what statements do is collectively known as semantics.

    pdf5p linhcuuhoa 10-09-2010 52 3   Download



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