Wed, 20 Aug 2008 21:17:33 BST CiteULike: matwendt's statistical-nlp http://www.citeulike.org/user/matwendt/tag/statistical-nlp CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/matwendt/article/1856373 <i>(1998), pp. 1110-1116.</i><br /><br />Generating semantic lexicons semiautomatically could be a great time saver, relative to creating them by hand. In this paper, we present an algorithm for extracting potential entries for a category from an on-line corpus, based upon a small set of exemplars. Our algorithm finds more correct terms and fewer incorrect ones than previous work in this area. Additionally, the entries that are generated potentially provide broader coverage of the category than vould occur to an individual coding them ... Noun-Phrase Co-Occurence Statistics for Semi-Automatic Semantic Lexicon Construction Brian Roark Eugene Charniak (1998), pp. 1110-1116. 2007-11-02T14:16:54-00:00 1998 1110 1116 lexicon-acquisition statistical-nlp http://www.citeulike.org/user/matwendt/article/1222866 <i></i><br /><br />We seek a knowledge-free method for inducing multiword units from text corpora for use as machine-readable dictionary headwords. We provide two major evaluations of nine existing collocation-finders and illustrate the continuing need for improvement. We use Latent Semantic Analysis to make modest gains in performance, but we show the significant challenges encountered in trying this approach. 1 Is Knowledge-Free Induction of Multiword Unit Dictionary Headwords a Solved Problem? Patrick Schone Daniel Jurafsky 2007-04-12T20:28:28-00:00 corpus lexicon-acquisition statistical-nlp http://www.citeulike.org/user/matwendt/article/105906 <i>(18 June 1999)</i><br /><br />"Statistical natural-language processing is, in my estimation, one of the most fast-moving and exciting areas of computer science these days. Anyone who wants to learn this field would be well advised to get this book. For that matter, the same goes for anyone who is already in the field. I know that it is going to be one of the most well-thumbed books on my bookshelf." -- Eugene Charniak, Department of Computer Science, Brown University <P>Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications. <P>More on this book Foundations of Statistical Natural Language Processing Christopher Manning Hinrich Schütze (18 June 1999) 2005-02-27T13:16:32-00:00 1999 The MIT Press introduction overview statistical-nlp http://www.citeulike.org/user/matwendt/article/1343015 <i>(1993)</i><br /><br />Representations for semantic information about words are necessary for many applications of neural networks in natural language processing. This paper describes an efficient, corpus-based method for inducing distributed semantic representations for a large number of words (50,000) from lexical coccurrence statistics by means of a large-scale linear regression. The representations are successfully applied to word sense disambiguation using a nearest neighbor method. Word space Hinrich Schütze (1993) 2007-05-30T14:01:49-00:00 1993 Morgan Kaufmann Publishers co-occurrence disambiguation statistical-nlp http://www.citeulike.org/user/matwendt/article/1342754 <i></i><br /><br />This paper describes a three-stage methodology for investigating the semantics and pragmatics of sentence and clause connective phrases. The first step in the methodology is to assemble a large corpus of connectives. The second step is to organise this corpus into a hierarchical taxonomy of synonyms and hyponyms, using a pre-theoretical substitution test. The final step is to impose a theoretical interpretation on the taxonomy. The taxonomy lends itself to an analysis of... A Data-Driven Method for Classifying Connective Phrases Alistair Knott Chris Mellish 2007-05-30T12:38:14-00:00 coherence corpus discourse nlp rst statistical-nlp http://www.citeulike.org/user/matwendt/article/1342717 <i></i><br /><br />Introduction: generation and understanding Natural Language Generation (NLG) research aims at systems which produce coherent natural language text from an underlying representation of knowledge. Systems must produce language|single sentences or more complex discourses|which (i) faithfully represents the relevant knowledge, and also (ii) does this in a naturalsounding way. These have been termed the delity and uency goals, respectively (Ward 1993). The uency goal leads to important dierences... Stochastic Text Generation Jon Oberlander Chris Brew 2007-05-30T12:17:03-00:00 fluency nlg pragmatics statistical-nlp http://www.citeulike.org/user/matwendt/article/833490 <i>(1998), pp. 704-710.</i><br /><br />We describe novel aspects of a new natural language generator called Nitrogen. This generator has a highly flexible input representation that allows a spectrum of input from syntactic to semantic depth, and shifts the burden of many linguistic decisions to the statistical post-processor. The generation algorithm is compositional, making it efficient, yet it also handles non-compositional aspects of language. Nitrogen's design makes it robust and scalable, operating with lexicons and knowledge... Generation that Exploits Corpus-Based Statistical Knowledge Irene Langkilde Kevin Knight (1998), pp. 704-710. 2006-09-07T08:32:38-00:00 1998 704 710 corpus lexical-choice nlg statistical-nlp http://www.citeulike.org/user/matwendt/article/282193 <i>(1992), pp. 539-545.</i> Automatic acquisition of hyponyms from large text corpora Marti Hearst doi:10.3115/992133.992154 (1992), pp. 539-545. 2005-08-15T10:52:53-00:00 1992 539 545 Association for Computational Linguistics corpus hyponym statistical-nlp text-mining http://www.citeulike.org/user/matwendt/article/1321514 <i></i><br /><br />Morphological analysis is an important subtask in text-to-speech conversion, hyphenation, and other language engineering tasks. The traditional approach to performing morphological analysis is to combine a morpheme lexicon, sets of (linguistic) rules, and heuristics to find a most probable analysis. In contrast, we present an inductive learning approach in which morphological analysis is reformulated as a segmentation task. We report on a number of experiments in which five inductive learning... An Inductive-Learning Approach to Morphological Analysis B Antal Walter D Weijters 2007-05-23T13:42:36-00:00 lexicon-acquisition morphology statistical-nlp http://www.citeulike.org/user/matwendt/article/1321502 <i>(1999)</i> Unsupervised Learning of Derivational Morphology from Inflectional Lexicons Eric Gaussier (1999) 2007-05-23T13:36:56-00:00 1999 lexicon-acquisition morphology statistical-nlp http://www.citeulike.org/user/matwendt/article/1321490 <i>(1999)</i><br /><br />This document is a summary, for ourselves and those who are curious, of the current state of our Phonological Learner. The Learner is the centerpiece of our current research project; it is a computer program whose purposes is to learn morphophonemic systems from input data, and to serve as a tool for modeling phonological and morphological knowledge in humans. 2. Rationale Linguists of many persuasions take a realist view of linguistic theory as it relates to learning: language learners, in... An automated learner for phonology and morphology A Albright B Hayes (1999) 2007-05-23T13:29:10-00:00 1999 morphology phonology statistical-nlp http://www.citeulike.org/user/matwendt/article/1321399 <i></i><br /><br />This paper describes a system for the unsupervised learning of morphological suffixes and stems from word lists. The system is composed of a generative probability model and a novel search algorithm. Unsupervised Learning of Morphology Using a Novel Directed Search Algorithm The 2007-05-23T13:06:12-00:00 lexicon-acquisition morphology statistical-nlp http://www.citeulike.org/user/matwendt/article/1321396 <i>(2001), pp. 482-490.</i><br /><br />This paper describes a system for unsupervised learning of morphological affixes from texts or word lists. The system is composed of a generative probability model and a search algorithm. Experi- ments on the Wall Street Journal and the Hansard Corpus (French and English) demonstrate the effectiveness of this approach. A Bayesian Model for Morpheme and Paradigm Identification Matthew Snover Michael Brent (2001), pp. 482-490. 2007-05-23T13:04:01-00:00 2001 482 490 lexicon-acquisition morphology statistical-nlp http://www.citeulike.org/user/matwendt/article/1321385 <i>Computational Linguistics, Vol. 27 (2001), pp. 153-198.</i> Unsupervised Learning of the Morphology of a Natural Language John Goldsmith Computational Linguistics, Vol. 27 (2001), pp. 153-198. 2007-05-23T12:56:55-00:00 2001 Computational Linguistics 27 153 198 lexicon-acquisition morphology statistical-nlp http://www.citeulike.org/user/matwendt/article/1321376 <i>(July 1997), pp. 604-609.</i><br /><br />The Naive Mix is a new supervised learning algorithm that is based on a sequential method for selecting probabilistic models. The usual objective of model selection is to find a single model that adequately characterizes the data in a training sample. However, during model selection a sequence of models is generated that consists of the best--fitting model at each level of model complexity. The Naive Mix utilizes this sequence of models to define a probabilistic model which is then used as a... A New Supervised Learning Algorithm for Word Sense Disambiguati on T Pedersen R Bruce (July 1997), pp. 604-609. 2007-05-23T12:52:27-00:00 1997 604 609 statistical-nlp word-sense-disambiguation http://www.citeulike.org/user/matwendt/article/1321372 <i>(July 1998), pp. 800-805.</i><br /><br />We present a corpus--based approach to word--sense disambiguation that only requires information that can be automatically extracted from untagged text. We use unsupervised techniques to estimate the parameters of a model describing the conditional distribution of the sense group given the known contextual features. Both the EM algorithm and Gibbs Sampling are evaluated to determine which is most appropriate for our data. We compare their disambiguation accuracy in an experiment with thirteen... Knowledge Lean Word Sense Disambiguation T Pedersen R Bruce (July 1998), pp. 800-805. 2007-05-23T12:50:23-00:00 1998 800 805 statistical-nlp word-sense-disambiguation http://www.citeulike.org/user/matwendt/article/1321357 <i></i><br /><br />Recently, there has been a proliferation of research into the acquisition of morphological grammars---that is, grammars and lexicons required for computer-based morphological analysis and synthesis. The approaches to acquiring such grammars range from tools which structure data provided by native speakers and linguists, to unsupervised machine learning. Despite this flurry of research into morphology learning, a means of comparing results among different approaches is largely lacking. This... Resources for Morphology Learning and Evaluation Mike Maxwell 2007-05-23T12:43:58-00:00 assessment lexicon-acquisition morphology statistical-nlp http://www.citeulike.org/user/matwendt/article/1321340 <i></i><br /><br />In this paper I present an algorithm for the unsupervised learning of morphology using stochastic finite state transducers, in particular Pair Hidden Markov Models. The task is viewed as an alignment problem between two sets of words. A supervised model of morphology acquisition is converted to an unsupervised model by treating the alignment as a further hidden variable. The use of the Expectation-Maximisation algorithm for this task is studied, which leads to calculations involving the... Partially Supervised Learning of Morphology with Stochastic Transducers Alexander Clark 2007-05-23T12:34:47-00:00 finite-state lexicon-acquisition morphology statistical-nlp http://www.citeulike.org/user/matwendt/article/1321308 <i>(2000), pp. 67-72.</i><br /><br />Morphology induction is a subproblem of important tasks like automatic learning of machine-readable dictionaries and grammar induction. Previous morphology induction approaches have relied solely on statistics of hypothesized stems and affixes to choose which axes to consider legitimate. Relying on stem-and-affix statistics rather than semantic knowledge leads to a number of problems, such as the inappropriate use of valid axes ("ally" stemming to "all"). We introduce a semantic-based algorithm ... Knowledge-Free Induction of Morphology Using Latent Semantic Analysis Patrick Schone Daniel Jurafsky (2000), pp. 67-72. 2007-05-23T12:15:17-00:00 2000 67 72 Association for Computational Linguistics classification lexicon-acquisition morphology statistical-nlp http://www.citeulike.org/user/matwendt/article/1321306 <i></i><br /><br />We introduce a language-independent strategy for inducing part of speech tags from corpora. Unlike other techniques that use language-specific lexicons, rulesets, and so forth to tag, our algorithm bootstraps only from cluster properties and language universals. Language-independent Induction of Part of Speech Class Labels Using Only Language Universals Patrick Schone Daniel Jurafsky 2007-05-23T12:14:17-00:00 classification lexicon-acquisition pos-tagging statistical-nlp http://www.citeulike.org/user/matwendt/article/1321250 <i>(11 July 2001)</i><br /><br />We describe a new formalism for word morphology. Our model views word generation as a random walk on a lattice of units where each unit is a set of (short) strings. The model naturally incorporates segmentation of words into morphemes. We capture the statistics of unit generation using a probabilistic suffix tree (PST) which is a variant of variable length Markov models. We present an efficient algorithm that learns a PST over the units whose output is a compact stochastic representation of ... A Markovian Lattice Model for the Acquisition of Morphological Structure Leonid Kontorovich Dana Ron Yoram Singer (11 July 2001) 2007-05-23T12:00:07-00:00 2001 lexicon-acquisition morphology statistical-nlp stemming http://www.citeulike.org/user/matwendt/article/1289617 <i></i><br /><br />ion. In the mean while, we refer the reader to [Jelinek & Mercer 1980] for Deleted Interpolation, to [Good 1953] and [Katz 1987] for Good-Turing reestimation, and to [Samuelsson 1996] for Successive Abstraction. 58 CHAPTER 2. APPLIED STATISTICS Chapter 3 Basic Corpus Linguistics 3.1 Empirical Evaluation Chinchor et al. write in Computational Linguistics 19(3): "One of the common problems with evaluations is that the statistical significance of the results is unknown", [Chinchor et al... The Linguist's Guide to Statistics - Don't Panic Brigitte Krenn Christer Samuelsson 2007-05-11T11:14:37-00:00 linguistics statistical-nlp statistics http://www.citeulike.org/user/matwendt/article/1287849 <i></i><br /><br />This paper describes unsupervised learning algorithm for disambiguating verbal word senses using term weight learning. Word Sense Disambiguation in Untagged Text based on Term Weight Learning Fumiyo And 2007-05-10T10:05:54-00:00 disambiguation sense statistical-nlp http://www.citeulike.org/user/matwendt/article/1287839 <i>(1997)</i><br /><br />Information Retrieval (IR) is an important application area of Natural Language Processing (NLP) where one encounters the genuine challenge of processing large quantities of unrestricted natural language text. While much effort has been made to apply NLP techniques to IR, very few NLP techniques have been evaluated on a document collection larger than several megabytes. Many NLP techniques are simply not efficient enough, and not robust enough, to handle a large amount of text. This paper... Fast Statistical Parsing of Noun Phrases for Document Indexing C Zhai (1997) 2007-05-10T10:01:18-00:00 1997 chunking shallow-parsing statistical-nlp http://www.citeulike.org/user/matwendt/article/1248098 <i>(1993), pp. 235-242.</i><br /><br />This paper presents a new method for producing a dictionary of subcategorization frames from unlabelled text corpora. It is shown that statistical filtering of the results of a finite state parser running on the output of a stochastic tagger produces high quality results, despite the error rates of the tagger and the parser. Further, it is argued that this method can be used to learn all subcategorization frames, whereas previous methods are not extensible to a general solution to the problem. ... Automatic Acquisition of a Large Subcategorization Dictionary from Corpora Christopher Manning (1993), pp. 235-242. 2007-04-24T16:18:00-00:00 1993 235 242 statistical-nlp subcategorization http://www.citeulike.org/user/matwendt/article/705498 <i>Computational Linguistics, Vol. 18, No. 4. (1992), pp. 467-479.</i><br /><br />We address the problem of predicting a word from previous words in a sample of text. In particular, we discuss n-gram models based on classes of words. We also discuss several statistical algorithms for assigning words to classes based on the frequency of their cooccurrence with other words. We find that we are able to extract classes that have the flavor of either syntactically based groupings or semantically based groupings, depending on the nature of the underlying statistics. 1 ... Class-Based n-gram Models of Natural Language Peter Brown Vincent Della Pietra Peter Desouza Jennifer Lai Robert Mercer Computational Linguistics, Vol. 18, No. 4. (1992), pp. 467-479. 2006-06-21T10:34:48-00:00 1992 Computational Linguistics 18 4 467 479 classification co-occurrence n-gram statistical-nlp