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Christopher D. Manning [51]Christopher Manning [42]ChristopherD Manning [1]
  1. Probabilistic Models of Language Processing and Acquisition.Nick Chater & Christopher D. Manning - 2006 - Trends in Cognitive Sciences 10 (7):335-344.
  2.  66
    Probabilistic Models of Language Processing and Acquisition.Nick Chater & Christopher D. Manning - 2006 - Trends in Cognitive Sciences 10 (7):335–344.
    Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online (...)
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  3.  28
    Accurate Unlexicalized Parsing.Dan Klein & Christopher D. Manning - unknown
    We demonstrate that an unlexicalized PCFG can parse much more accurately than previously shown, by making use of simple, linguistically motivated state splits, which break down false independence assumptions latent in a vanilla treebank grammar. Indeed, its performance of 86.36% (LP/LR F1) is better than that of early lexicalized PCFG models, and surprisingly close to the current state-of-theart. This result has potential uses beyond establishing a strong lower bound on the maximum possible accuracy of unlexicalized models: an unlexicalized PCFG is (...)
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  4. Accurate Unlexicalized Parsing.Christopher Manning - manuscript
    assumptions latent in a vanilla treebank grammar. Indeed, its performance of 86.36% (LP/LR F1) is..
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  5.  61
    An Introduction to Information Retrieval.Christopher D. Manning - unknown
    1 Boolean retrieval 1 2 The term vocabulary and postings lists 19 3 Dictionaries and tolerant retrieval 49 4 Index construction 67 5 Index compression 85 6 Scoring, term weighting and the vector space model 109 7 Computing scores in a complete search system 135 8 Evaluation in information retrieval 151 9 Relevance feedback and query expansion 177 10 XML retrieval 195 11 Probabilistic information retrieval 219 12 Language models for information retrieval 237 13 Text classification and Naive Bayes 253 (...)
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  6.  36
    Learning to Recognize Features of Valid Textual Entailments.Christopher Manning - unknown
    separated from evaluating entailment. Current approaches to semantic inference in question answer-.
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  7.  62
    Studying the History of Ideas Using Topic Models.David Hall & Christopher D. Manning - unknown
    How can the development of ideas in a scientific field be studied over time? We apply unsupervised topic modeling to the ACL Anthology to analyze historical trends in the field of Computational Linguistics from 1978 to 2006. We induce topic clusters using Latent Dirichlet Allocation, and examine the strength of each topic over time. Our methods find trends in the field including the rise of probabilistic methods starting in 1988, a steady increase in applications, and a sharp decline of research (...)
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  8. Labeled LDA: A Supervised Topic Model for Credit Attribution in Multi-Labeled Corpora.David Hall & Christopher D. Manning - unknown
    A significant portion of the world’s text is tagged by readers on social bookmarking websites. Credit attribution is an inherent problem in these corpora because most pages have multiple tags, but the tags do not always apply with equal specificity across the whole document. Solving the credit attribution problem requires associating each word in a document with the most appropriate tags and vice versa. This paper introduces Labeled LDA, a topic model that constrains Latent Dirichlet Allocation by defining a one-to-one (...)
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  9.  49
    Generating Typed Dependency Parses From Phrase Structure Parses.Christopher Manning - unknown
    This paper describes a system for extracting typed dependency parses of English sentences from phrase structure parses. In order to capture inherent relations occurring in corpus texts that can be critical in real-world applications, many NP relations are included in the set of grammatical relations used. We provide a comparison of our system with Minipar and the Link parser. The typed dependency extraction facility described here is integrated in the Stanford Parser, available for download.
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  10.  45
    Probabilistic Syntax.Christopher Manning - manuscript
    “Everyone knows that language is variable.” This is the bald sentence with which Sapir (1921:147) begins his chapter on language as an historical product. He goes on to emphasize how two speakers’ usage is bound to differ “in choice of words, in sentence structure, in the relative frequency with which particular forms or combinations of words are used”. I should add that much sociolinguistic and historical linguistic research has shown that the same speaker’s usage is also variable (Labov 1966, Kroch (...)
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  11.  12
    Natural Language Grammar Induction Using a Constituent-Context Model.Dan Klein & Christopher D. Manning - unknown
    This paper presents a novel approach to the unsupervised learning of syntactic analyses of natural language text. Most previous work has focused on maximizing likelihood according to generative PCFG models. In contrast, we employ a simpler probabilistic model over trees based directly on constituent identity and linear context, and use an EM-like iterative procedure to induce structure. This method produces much higher quality analyses, giving the best published results on the ATIS dataset.
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  12.  32
    Natural Logic for Textual Inference.Christopher D. Manning - unknown
    This paper presents the first use of a computational model of natural logic—a system of logical inference which operates over natural language—for textual inference. Most current approaches to the PAS- CAL RTE textual inference task achieve robustness by sacrificing semantic precision; while broadly effective, they are easily confounded by ubiquitous inferences involving monotonicity. At the other extreme, systems which rely on first-order logic and theorem proving are precise, but excessively brittle. This work aims at a middle way. Our system finds (...)
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  13.  52
    Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network.Christopher Manning - manuscript
    first-order HMM, the current tag t0 is predicted based on the previous tag t−1 (and the current word).1 The back- We present a new part-of-speech tagger that ward interaction between t0 and the next tag t+1 shows demonstrates the following ideas: (i) explicit up implicitly later, when t+1 is generated in turn. While unidirectional models are therefore able to capture both use of both preceding and following tag con-.
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  14.  18
    Ergativity: Argument Structure and Grammatical Relations.Christopher D. Manning - unknown
    I wish to present a codi cation of syntactic approaches to dealing with ergative languages and argue for the correctness of one particular approach, which I will call the Inverse Grammatical Relations hypothesis.1 I presume familiarity with the term `ergativity', but, brie y, many languages have ergative case marking, such as Burushaski in (1), in contrast to the accusative case marking of Latin in (2). More generally, if we follow Dixon (1979) and use A to mark the agent-like argument of (...)
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  15.  17
    Incorporating Non-Local Information Into Information Extraction Systems by Gibbs Sampling.Christopher Manning - unknown
    Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sam- pling, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, (...)
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  16.  15
    Learning to Distinguish Valid Textual Entailments.Christopher D. Manning & Daniel Cer - unknown
    This paper proposes a new architecture for textual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to the text, using a locally decomposable matching score. While this formulation is adequate for representing local (word-level) phenomena such as synonymy, it is incapable of representing global interactions, such as that between verb negation (...)
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  17.  54
    Learning Alignments and Leveraging Natural Logic.Nathanael Chambers, Daniel Cer, Trond Grenager, David Hall, Chloe Kiddon, Bill MacCartney, Marie-Catherine de Marneffe, Daniel Ramage, Eric Yeh & Christopher D. Manning - unknown
    We describe an approach to textual inference that improves alignments at both the typed dependency level and at a deeper semantic level. We present a machine learning approach to alignment scoring, a stochastic search procedure, and a new tool that finds deeper semantic alignments, allowing rapid development of semantic features over the aligned graphs. Further, we describe a complementary semantic component based on natural logic, which shows an added gain of 3.13% accuracy on the RTE3 test set.
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  18.  23
    A Generative Constituent-Context Model for Improved Grammar Induction.Dan Klein & Christopher D. Manning - unknown
    We present a generative distributional model for the unsupervised induction of natural language syntax which explicitly models constituent yields and contexts. Parameter search with EM produces higher quality analyses than previously exhibited by unsupervised systems, giving the best published unsupervised parsing results on the ATIS corpus. Experiments on Penn treebank sentences of comparable length show an even higher F1 of 71% on nontrivial brackets. We compare distributionally induced and actual part-of-speech tags as input data, and examine extensions to the basic (...)
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  19.  15
    Parsing with Treebank Grammars: Empirical Bounds, Theoretical Models, and the Structure of the Penn Treebank.Dan Klein & Christopher D. Manning - unknown
    This paper presents empirical studies and closely corresponding theoretical models of the performance of a chart parser exhaustively parsing the Penn Treebank with the Treebank’s own CFG grammar. We show how performance is dramatically affected by rule representation and tree transformations, but little by top-down vs. bottom-up strategies. We discuss grammatical saturation, including analysis of the strongly connected components of the phrasal nonterminals in the Treebank, and model how, as sentence length increases, the effective grammar rule size increases as regions (...)
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  20.  13
    Fast Exact Inference with a Factored Model for Natural Language Parsing.Dan Klein & Christopher D. Manning - unknown
    We present a novel generative model for natural language tree structures in which semantic (lexical dependency) and syntactic (PCFG) structures are scored with separate models. This factorization provides conceptual simplicity, straightforward opportunities for separately improving the component models, and a level of performance comparable to similar, non-factored models. Most importantly, unlike other modern parsing models, the factored model admits an extremely effective A* parsing algorithm, which enables efficient, exact inference.
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  21.  6
    Robust Textual Inference Via Graph Matching.Christopher D. Manning - unknown
    We present a system for deciding whether a given sentence can be inferred from text. Each sentence is represented as a directed graph (extracted from a dependency parser) in which the nodes represent words or phrases, and the links represent syntactic and semantic relationships. We develop a learned graph matching model to approximate entailment by the amount of the sentence’s semantic content which is contained in the text. We present results on the Recognizing Textual Entailment dataset (Dagan et al., 2005), (...)
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  22.  37
    Finding Contradictions in Text.Christopher Manning - manuscript
    Marie-Catherine de Marneffe, Anna N. Rafferty and Christopher D. Manning Linguistics Department Computer Science Department Stanford University Stanford University Stanford, CA 94305 Stanford, CA 94305 {rafferty,manning}@stanford.edu mcdm@stanford.edu..
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  23.  36
    Learning Alignments and Leveraging Natural Logic.Christopher Manning - manuscript
    Nathanael Chambers, Daniel Cer, Trond Grenager, David Hall, Chloe Kiddon Bill MacCartney, Marie-Catherine de Marneffe, Daniel Ramage Eric Yeh, Christopher D. Manning Computer Science Department Stanford University Stanford, CA 94305.
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  24.  45
    Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger.Christopher Manning - manuscript
    Kristina Toutanova Christopher D. Manning Dept of Computer Science Depts of Computer Science and Linguistics Gates Bldg 4A, 353 Serra Mall Gates Bldg 4A, 353 Serra Mall Stanford, CA 94305–9040, USA Stanford, CA 94305–9040, USA kristina@cs.stanford.edu manning@cs.stanford.edu..
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  25.  49
    Efficient, Feature-Based, Conditional Random Field Parsing.Christopher D. Manning - unknown
    Discriminative feature-based methods are widely used in natural language processing, but sentence parsing is still dominated by generative methods. While prior feature-based dynamic programming parsers have restricted training and evaluation to artificially short sentences, we present the first general, featurerich discriminative parser, based on a conditional random field model, which has been successfully scaled to the full WSJ parsing data. Our efficiency is primarily due to the use of stochastic optimization techniques, as well as parallelization and chart prefiltering. On WSJ15, (...)
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  26.  33
    Modeling Semantic Containment and Exclusion in Natural Language Inference.Christopher D. Manning - unknown
    We propose an approach to natural language inference based on a model of natural logic, which identifies valid inferences by their lexical and syntactic features, without full semantic interpretation. We greatly extend past work in natural logic, which has focused solely on semantic containment and monotonicity, to incorporate both semantic exclusion and implicativity. Our system decomposes an inference problem into a sequence of atomic edits linking premise to hypothesis; predicts a lexical entailment relation for each edit using a statistical classifier; (...)
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  27.  97
    A Phrase-Based Alignment Model for Natural Language Inference.Christopher D. Manning - unknown
    The alignment problem—establishing links between corresponding phrases in two related sentences—is as important in natural language inference (NLI) as it is in machine translation (MT). But the tools and techniques of MT alignment do not readily transfer to NLI, where one cannot assume semantic equivalence, and for which large volumes of bitext are lacking. We present a new NLI aligner, the MANLI system, designed to address these challenges. It uses a phrase-based alignment representation, exploits external lexical resources, and capitalizes on (...)
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  28.  51
    Optimizing Chinese Word Segmentation for Machine Translation Performance.Christopher Manning - unknown
    Pi-Chuan Chang, Michel Galley, and Christopher D. Manning Computer Science Department, Stanford University Stanford, CA 94305 pichuan,galley,manning@cs.stanford.edu..
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  29.  47
    Fast Exact Inference with a Factored Model for Natural Language Parsing.Christopher Manning - manuscript
    We present a novel generative model for natural language tree structures in which semantic (lexical dependency) and syntactic (PCFG) structures are scored with separate models. This factorization provides conceptual simplicity, straightforward opportunities for separately improving the component models, and a level of performance comparable to similar, non-factored models. Most importantly, unlike other modern parsing models, the factored model admits an extremely effective A* parsing algorithm, which enables efficient, exact inference.
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  30.  12
    The Lexical Integrity of Japanese Causatives.Christopher D. Manning & Ivan A. Sag - unknown
    Grammatical theory has long wrestled with the fact that causative constructions exhibit properties of both single words and complex phrases. However, as Paul Kiparsky has observed, the distribution of such properties of causatives is not arbitrary: ‘construal’ phenomena such as honorification, anaphor and pronominal binding, and quantifier ‘floating’ typically behave as they would if causatives were syntactically complex, embedding constructions; whereas case marking, agreement and word order phenomena all point to the analysis of causatives as single lexical items.1 Although an (...)
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  31.  10
    Conditional Structure Versus Conditional Estimation in NLP Models.Dan Klein & Christopher D. Manning - unknown
    This paper separates conditional parameter estima- tion, which consistently raises test set accuracy on statistical NLP tasks, from conditional model struc- tures, such as the conditional Markov model used for maximum-entropy tagging, which tend to lower accuracy. Error analysis on part-of-speech tagging shows that the actual tagging errors made by the conditionally structured model derive not only from label bias, but also from other ways in which the independence assumptions of the conditional model structure are unsuited to linguistic sequences. The (...)
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  32.  10
    Automatic Acquisition of a Large Subcategorization Dictionary From Corpora.Christopher D. Manning - unknown
    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.
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  33.  61
    A Conditional Random Field Word Segmenter.Christopher Manning - unknown
    We present a Chinese word segmentation system submitted to the closed track of Sighan bakeoff 2005. Our segmenter was built using a conditional random field sequence model that provides a framework to use a large number of linguistic features such as character identity, morphological and character reduplication features. Because our morphological features were extracted from the training corpora automatically, our system was not biased toward any particular variety of Mandarin. Thus, our system does not overfit the variety of Mandarin most (...)
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  34.  52
    Part-of-Speech Tagging From 97% to 100%: Is It Time for Some Linguistics?Christopher D. Manning - unknown
    I examine what would be necessary to move part-of-speech tagging performance from its current level of about 97.3% token accuracy (56% sentence accuracy) to close to 100% accuracy. I suggest that it must still be possible to greatly increase tagging performance and examine some useful improvements that have recently been made to the Stanford Part-of-Speech Tagger. However, an error analysis of some of the remaining errors suggests that there is limited further mileage to be had either from better machine learning (...)
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  35.  33
    The Infinite Tree.Christopher Manning - manuscript
    number of hidden categories is not fixed, but when the number of hidden states is unknown (Beal et al., 2002; Teh et al., 2006). can grow with the amount of training data.
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  36.  42
    A Simple and Effective Hierarchical Phrase Reordering Model.Christopher D. Manning - unknown
    adjacent phrases, but they typically lack the ability to perform the kind of long-distance reorderings possible with syntax-based systems. In this paper, we present a novel hierarchical phrase reordering model aimed at improving non-local reorderings, which seamlessly integrates with a standard phrase-based system with little loss of computational efficiency. We show that this model can successfully handle the key examples often used to motivate syntax-based systems, such as the rotation of a prepositional phrase around a noun phrase. We contrast our (...)
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  37.  39
    Clustering the Tagged Web.Christopher D. Manning - unknown
    Automatically clustering web pages into semantic groups promises improved search and browsing on the web. In this paper, we demonstrate how user-generated tags from largescale social bookmarking websites such as del.icio.us can be used as a complementary data source to page text and anchor text for improving automatic clustering of web pages. This paper explores the use of tags in 1) K-means clustering in an extended vector space model that includes tags as well as page text and 2) a novel (...)
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  38. Nested Named Entity Recognition.Christopher D. Manning - unknown
    Many named entities contain other named entities inside them. Despite this fact, the field of named entity recognition has almost entirely ignored nested named entity recognition, but due to technological, rather than ideological reasons. In this paper, we present a new technique for recognizing nested named entities, by using a discriminative constituency parser. To train the model, we transform each sentence into a tree, with constituents for each named entity (and no other syntactic structure). We present results on both newspaper (...)
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  39.  17
    Distributional Phrase Structure Induction.Dan Klein & Christopher D. Manning - unknown
    Unsupervised grammar induction systems commonly judge potential constituents on the basis of their effects on the likelihood of the data. Linguistic justifications of constituency, on the other hand, rely on notions such as substitutability and varying external contexts. We describe two systems for distributional grammar induction which operate on such principles, using part-of-speech tags as the contextual features. The advantages and disadvantages of these systems are examined, including precision/recall trade-offs, error analysis, and extensibility.
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  40.  29
    An Ç ´Ò¿ Μ Agenda-Based Chart Parser for Arbitrary Probabilistic Context-Free Grammars.Dan Klein & Christopher D. Manning - unknown
    While Ç ´Ò¿ µ methods for parsing probabilistic context-free grammars (PCFGs) are well known, a tabular parsing framework for arbitrary PCFGs which allows for botton-up, topdown, and other parsing strategies, has not yet been provided. This paper presents such an algorithm, and shows its correctness and advantages over prior work. The paper finishes by bringing out the connections between the algorithm and work on hypergraphs, which permits us to extend the presented Viterbi (best parse) algorithm to an inside (total probability) (...)
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  41.  41
    Dissociations Between Argument Structure and Grammatical Relations.Christopher Manning - manuscript
    In Pollard and Sag (1987) and Pollard and Sag (1994:Ch. 1–8), the subcategorized arguments of a head are stored on a single ordered list, the subcat list. However, Borsley (1989) argues that there are various defi- ciencies in this approach, and suggests that the unified list should be split into separate lists for subjects, complements, and specifiers. This proposal has been widely adopted in what is colloquially known as HPSG3 (Pollard and Sag (1994:Ch. 9) and other recent work in HPSG). (...)
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  42.  20
    Aligning Semantic Graphs for Textual Inference and Machine Reading.Marie-Catherine de Marneffe, Trond Grenager, Bill MacCartney, Daniel Cer, Daniel Ramage, Chloe Kiddon & Christopher D. Manning - unknown
    This paper presents our work on textual inference and situates it within the context of the larger goals of machine reading. The textual inference task is to determine if the meaning of one text can be inferred from the meaning of another and from background knowledge. Our system generates semantic graphs as a representation of the meaning of a text. This paper presents new results for aligning pairs of semantic graphs, and proposes the application of natural logic to derive inference (...)
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  43.  26
    The Stanford Typed Dependencies Representation.Christopher D. Manning - unknown
    This paper examines the Stanford typed dependencies representation, which was designed to provide a straightforward description of grammatical relations for any user who could benefit from automatic text understanding. For such purposes, we argue that dependency schemes must follow a simple design and provide semantically contentful information, as well as offer an automatic procedure to extract the relations. We consider the underlying design principles of the Stanford scheme from this perspective, and compare it to the GR and PARC representations. Finally, (...)
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  44.  34
    Parsing and Hypergraphs.Christopher Manning - manuscript
    While symbolic parsers can be viewed as deduction systems, this view is less natural for probabilistic parsers. We present a view of parsing as directed hypergraph analysis which naturally covers both symbolic and probabilistic parsing. We illustrate the approach by showing how a dynamic extension of Dijkstra’s algorithm can be used to construct a probabilistic chart parser with an Ç´Ò¿µ time bound for arbitrary PCFGs, while preserving as much of the flexibility of symbolic chart parsers as allowed by the inherent (...)
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  45.  34
    Max-Margin Parsing.Christopher Manning - manuscript
    Ben Taskar Dan Klein Michael Collins Computer Science Dept. Computer Science Dept. CS and AI Lab Stanford University Stanford University.
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  46.  15
    Parsing and Hypergraphs.Dan Klein & Christopher D. Manning - unknown
    While symbolic parsers can be viewed as deduction systems, this view is less natural for probabilistic parsers. We present a view of parsing as directed hypergraph analysis which naturally covers both symbolic and probabilistic parsing. We illustrate the approach by showing how a dynamic extension of Dijkstra’s algorithm can be used to construct a probabilistic chart parser with an Ç´Ò¿µ time bound for arbitrary PCFGs, while preserving as much of the flexibility of symbolic chart parsers as allowed by the inherent (...)
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  47.  9
    Soft Constraints Mirror Hard Constraints: Voice and Person in English and Lummi.Christopher D. Manning - unknown
    The same categorical phenomena which are attributed to hard grammatical constraints in some languages continue to show up as statistical preferences in other languages, motivating a grammatical model that can account for soft constraints. The effects of a hierarchy of person (1st, 2nd 3rd) on grammar are categorical in some languages, most famously in languages withError: Illegal entry in bfrange block in ToUnicode CMap inverse systems, but also in languages with person restrictions on passivization. In Lummi, for example, the person (...)
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  48.  26
    An Extended Model of Natural Logic.Christopher D. Manning & Bill MacCartney - unknown
    We propose a model of natural language inference which identifies valid inferences by their lexical and syntactic features, without full semantic interpretation. We extend past work in natural logic, which has focused on semantic containment and monotonicity, by incorporating both semantic exclusion and implicativity. Our model decomposes an inference problem into a sequence of atomic edits linking premise to hypothesis; predicts a lexical semantic relation for each edit; propagates these relations upward through a semantic composition tree according to properties of (...)
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  49.  24
    An ¢¡¤£¦¥¨§ Agenda-Based Chart Parser for Arbitrary Probabilistic Context-Free Grammars.Christopher Manning - manuscript
    fundamental rule” in an order-independent manner, such that the same basic algorithm supports top-down and Most PCFG parsing work has used the bottom-up bottom-up parsing, and the parser deals correctly with CKY algorithm (Kasami, 1965; Younger, 1967) with the difficult cases of left-recursive rules, empty elements, Chomsky Normal Form Grammars (Baker, 1979; Jeand unary rules, in a natural way.
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  50.  6
    Feature Selection for a Rich HPSG Grammar Using Decision Trees.Christopher D. Manning & Kristina Toutanova - unknown
    This paper examines feature selection for log linear models over rich constraint-based grammar (HPSG) representations by building decision trees over features in corresponding probabilistic context free grammars (PCFGs). We show that single decision trees do not make optimal use of the available information; constructed ensembles of decision trees based on different feature subspaces show signifi- cant performance gains (14% parse selection error reduction). We compare the performance of the learned PCFG grammars and log linear models over the same features.
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