SEQUITUR is an algorithm that infers a hierarchical structure from a sequence of discrete symbols by replacing repeated phrases with a grammatical rule that generates the phrase, and continuing this process recursively. The result is a hierarchical representation of the original sequence, which offers insights into its lexical structure. The algorithm is driven by two constraints that reduce the size of the grammar, and produce structure as a by-product. SEQUITUR breaks new ground by operating incrementally. Moreover, the method’s simple structure permits a proof that it operates in space and time that is linear in the size of the input. Our implementation can process 50,000 symbols per second and has been applied to an extensive range of real world sequences. 1.

We present a generative model for the unsupervised learning of dependency structures. We also describe the multiplicative combination of this dependency model with a model of linear constituency. The product model outperforms both components on their respective evaluation metrics, giving the best published figures for unsupervised dependency parsing and unsupervised constituency parsing. We also demonstrate that the combined model works and is robust cross-linguistically, being able to exploit either attachment or distributional regularities that are salient in the data. 1

To simplify the task of obtaining information from the vast number of information sources that are available on the World Wide Web (WWW), we are building tools to build information mediators for extracting and integrating data from multiple Web sources. In a mediator based approach, wrappers are built around individual information sources, that provide translation between the mediator query language and the individual source. We present an approach for semi-automatically generating wrappers for structured internet sources. The key idea is to exploit formatting information in Web pages from the source to hypothesize the underlying structure of a page. From this structure the system generates a wrapper that facilitates querying of a source and possibly integrating it with other sources. We demonstrate the ease with which we are able to build wrappers for a number of Web sources using our implemented wrapper generation toolkit. 1. Introduction We are building information agents or media...

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m we can carve o# next. `Partial parsing' is a cover term for a range of di#erent techniques for recovering some but not all of the information contained in a traditional syntactic analysis. Partial parsing techniques, like tagging techniques, aim for reliability and robustness in the face of the vagaries of natural text, by sacrificing completeness of analysis and accepting a low but non-zero error rate. 1 Tagging The earliest taggers [35, 51] had large sets of hand-constructed rules for assigning tags on the basis of words' character patterns and on the basis of the tags assigned to preceding or following words, but they had only small lexica, primarily for exceptions to the rules. TAGGIT [35] was used to generate an initial tagging of the Brown corpus, which was then hand-edited. (Thus it provided the data that has since been used to train other taggers [20].) The tagger described by Garside [56, 34], CLAWS, was a probabilistic version of TAGGIT, and the DeRose tagger improved on

In order to acquire a lexicon, young children must segment speech into words, even though most words are unfamiliar to them. This is a non-trivial task because speech lacks any acoustic analog of the blank spaces between printed words. Two sources of information that might be useful for this task are distributional regularity and phonotactic constraints. Informally, distributional regularity refers to the intuition that sound sequences that occur frequently and in a variety of contexts are better candidates for the lexicon than those that occur rarely or in few contexts. We express that intuition formally by a class of functions called DR functions. We then put forth three hypotheses: First, that children segment using DR functions. Second, that they exploit phonotactic constraints on the possible pronunciations of words in their language. Specifically, they exploit both the requirement that every word must have a vowel and the constraints that languages impose constraints on word-ini...

We present a generative distributional model for the unsupervised induction of natural language syntax which explicitly models constituent yields and contexts.

Learning-based anomaly detection systems build models of the expected behavior of applications by analyzing events that are generated during their normal operation. Once these models have been established, subsequent events are analyzed to identify deviations, given the assumption that anomalies usually represent evidence of an attack.

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