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The resulting structure of a sentence will be given as a hierarchical arrangement of constituents.

Although this algorithm does not use any a priori knowledge about the language, it is able to detect heads, modifiers and a phrase type’s different compound composition possibilities.

Why is it that if you know I’ll give a silly talk, it follows that you know I’ll give a talk, whereas if you doubt I’ll give a good talk, it doesn’t follow that you doubt I’ll give a talk?

This pair of examples shows that the word “doubt” exhibits a special but prevalent kind of behavior known as downward entailingness — the licensing of reasoning from supersets to subsets, so to speak, but not vice versa.

We develop two novel learning algorithms capable of predicting complex structures which only rely on a binary feedback signal based on the context of an external world.

In addition we reformulate the semantic parsing problem to reduce the dependency of the model on syntactic patterns, thus allowing our parser to scale better using less supervision.

If we are able to detect these separators we can follow a very simple procedure to identify the constituents of a sentence by taking the classes of words between separators.

The name of the game in such settings is to find and leverage auxiliary sources of information.The first project I’ll describe is to identify words that are downward entailing, a task that promises to enhance the performance of systems that engage in textual inference, and one that is quite challenging since it is difficult to characterize these items as a class and no corpus with downward-entailingness annotations exists.We are able to surmount these challenges by utilizing some insights from the linguistics literature regarding the relationship between downward entailing operators and what are known as negative polarity items — words such as “ever” or the idiom “have a clue” that tend to occur only in negative contexts.For evaluation purposes, the algorithm is applied to manually annotated part-of-speech tags (POS tags) as well as to word classes induced by an unsupervised part-of-speech tagger.We show that Viterbi (or "hard") EM is well-suited to unsupervised grammar induction.

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