waashk/dblp huggingface dataset:
three generative, lexicalised models for statistical parsing. in this paper we first propose a new statistical parsing model, which is a generative model of lexicalised context-free grammar. we then extend the model to include a probabilistic treatment of both subcategorisation and wh-movement. results on wall street journal text show that the parser performs at 88.1/87.5% constituent precision/recall, an average improvement of 2.3% over (collins 96).
head-driven parsing for word lattices. we present the first application of the head-driven statistical parsing model of collins (1999) as a simultaneous language model and parser for large-vocabulary speech recognition. the model is adapted to an online left to right chart-parser for word lattices, integrating acoustic, n-gram, and parser probabilities. the parser uses structural and lexical dependencies not considered by n-gram models, conditioning recognition on more linguistically-grounded relationships. experiments on the wall street journal treebank and lattice corpora show word error rates competitive with the standard n-gram language model while extracting additional structural information useful for speech understanding.
new ranking algorithms for parsing and tagging: kernels over discrete structures, and the voted perceptron. this paper introduces new learning algorithms for natural language processing based on the perceptron algorithm. we show how the algorithms can be efficiently applied to exponential sized representations of parse trees, such as the "all subtrees" (dop) representation described by (bod 1998), or a representation tracking all sub-fragments of a tagged sentence. we give experimental results showing significant improvements on two tasks: parsing wall street journal text, and named-entity extraction from web data.
clause restructuring for statistical machine translation. we describe a method for incorporating syntactic information in statistical machine translation systems. the first step of the method is to parse the source language string that is being translated. the second step is to apply a series of transformations to the parse tree, effectively reordering the surface string on the source language side of the translation system. the goal of this step is to recover an underlying word order that is closer to the target language word-order than the original string. the reordering approach is applied as a pre-processing step in both the training and decoding phases of a phrase-based statistical mt system. we describe experiments on translation from german to english, showing an improvement from 25.2% bleu score for a baseline system to 26.8% bleu score for the system with reordering, a statistically significant improvement.
incremental parsing with the perceptron algorithm. this paper describes an incremental parsing approach where parameters are estimated using a variant of the perceptron algorithm. a beam-search algorithm is used during both training and decoding phases of the method. the perceptron approach was implemented with the same feature set as that of an existing generative model (roark, 2001a), and experimental results show that it gives competitive performance to the generative model on parsing the penn treebank. we demonstrate that training a perceptron model to combine with the generative model during search provides a 2.1 percent f-measure improvement over the generative model alone, to 88.8 percent.
discriminative syntactic language modeling for speech recognition. we describe a method for discriminative training of a language model that makes use of syntactic features. we follow a reranking approach, where a baseline recogniser is used to produce 1000-best output for each acoustic input, and a second "reranking" model is then used to choose an utterance from these 1000-best lists. the reranking model makes use of syntactic features together with a parameter estimation method that is based on the perception algorithm. we describe experiments on the switchboard speech recognition task. the syntactic features provide an additional 0.3% reduction in test-set error rate beyond the model of (roark et al., 2004a; roark et al., 2004b) (significant at p < 0.001), which makes use of a discriminatively trained n-gram model, giving a total reduction of 1.2% over the baseline switchboard system.
measuring conformity to discourse routines in decision-making interactions. in an effort to develop measures of discourse level management strategies, this study examines a measure of the degree to which decision-making interactions consist of sequences of utterance functions that are linked in a decision-making routine. the measure is applied to 100 dyadic interactions elicited in both face-to-face and computer-mediated environments with systematic variation of task complexity and message-window size. every utterance in the interactions is coded according to a system that identifies decision-making functions and other routine functions of utterances. markov analyses of the coded utterances make it possible to measure the relative frequencies with which sequences of 2 and 3 utterances trace a path in a markov model of the decision routine. these proportions suggest that interactions in all conditions adhere to the model, although we find greater conformity in the computer-mediated environments, which is probably due to increased processing and attentional demands for greater efficiency. the results suggest that measures based on markov analyses of coded interactions can provide useful measures for comparing discourse level properties, for correlating discourse features with other textual features, and for analyses of discourse management strategies.
topic-focused multi-document summarization using an approximate oracle score. we consider the problem of producing a multi-document summary given a collection of documents. since most successful methods of multi-document summarization are still largely extractive, in this paper, we explore just how well an extractive method can perform. we introduce an "oracle" score, based on the probability distribution of unigrams in human summaries. we then demonstrate that with the oracle score, we can generate extracts which score, on average, better than the human summaries, when evaluated with rouge. in addition, we introduce an approximation to the oracle score which produces a system with the best known performance for the 2005 document understanding conference (duc) evaluation.
integrating symbolic and statistical representations: the lexicon pragmatics interface. we describe a formal framework for interpretation of words and compounds in a discourse context which integrates a symbolic lexicon/grammar, word-sense probabilities, and a pragmatic component. the approach is motivated by the need to handle productive word use. in this paper, we concentrate on compound nominals. we discuss the inadequacies of approaches which consider compund interpretation as either wholly lexico-grammatical or wholly pragmatic, and provide an alternative integrated account.
an algebra for semantic construction in constraint-based grammars. we develop a framework for formalizing semantic construction within grammars expressed in typed feature structure logics, including hpsg. the approach provides an alternative to the lambda calculus; it maintains much of the desirable flexibility of unification-based approaches to composition, while constraining the allowable operations in order to capture basic generalizations and improve maintainability.
a pylonic decision-tree language model- with optimal question selection. this paper discusses a decision-tree approach to the problem of assigning probabilities to words following a given text. in contrast with previous decision-tree language model attempts, an algorithm for selecting nearly optimal questions is considered. the model is to be tested on a standard task, <i>the wall street journal</i>, allowing a fair comparison with the well-known trigram model.
using parsed corpora for structural disambiguation in the trains domain. this paper describes a prototype disambiguation module, kankei, which was tested on two corpora of the trains project. in ambiguous verb phrases of form v ... np pp or v ... np adverb(s), the two corpora have very different pp and adverb attachment patterns; in the first, the correct attachment is to the vp 88.7% of the time, while in the second, the correct attachment is to the np 73.5% of the time. kankei uses various n-gram patterns of the phrase heads around these ambiguities, and assigns parse trees (with these ambiguities) a score based on a linear combination of the frequencies with which these patterns appear with np and vp attachments in the trains corpora. unlike previous statistical disambiguation systems, this technique thus combines evidence from bigrams, trigrams, and the 4-gram around an ambiguous attachment. in the current experiments, equal weights are used for simplicity but results are still good on the trains corpora (92.2% and 92.4% accuracy). despite the large statistical differences in attachment preferences in the two corpora, training on the first corpus and testing on the second gives an accuracy of 90.9%.
a syntactic framework for speech repairs and other disruptions. this paper presents a grammatical and processing framework for handling the repairs, hesitations, and other interruptions in natural human dialog. the proposed framework has proved adequate for a collection of human-human task-oriented dialogs, both in a full manual examination of the corpus, and in tests with a parser capable of parsing some of that corpus. this parser can also correct a pre-parser speech repair identifier resulting in a 4.8% increase in recall.
on determining the consistency of partial descriptions of trees. we examine the consistency problem for descriptions of trees based on remote dominance, and present a consistency-checking algorithm which is polynomial in the number of nodes in the description, despite disjunctions inherent in the theory of trees. the resulting algorithm allows for descriptions which go beyond sets of atomic formulas to allow certain types of disjunction and negation.
less is more: eliminating index terms from subordinate clauses. we perform a linguistic analysis of documents during indexing for information retrieval. by eliminating index terms that occur only in subordinate clauses, index size is reduced by approximately 30% without adversely affecting precision or recall. these results hold for two corpora: a sample of the world wide web and an electronic encyclopedia.
a machine learning approach to the automatic evaluation of machine translation. we present a machine learning approach to evaluating the well-formedness of output of a machine translation system, using classifiers that learn to distinguish human reference translations from machine translations. this approach can be used to evaluate an mt system, tracking improvements over time; to aid in the kind of failure analysis that can help guide system development; and to select among alternative output strings. the method presented is fully automated and independent of source language, target language and domain.
using wordnet to automatically deduce relations between words in noun-noun compounds. we present an algorithm for automatically disambiguating noun-noun compounds by deducing the correct semantic relation between their constituent words. this algorithm uses a corpus of 2,500 compounds annotated with wordnet senses and covering 139 different semantic relations (we make this corpus available online for researchers interested in the semantics of noun-noun compounds). the algorithm takes as input the wordnet senses for the nouns in a compound, finds all parent senses (hypernyms) of those senses, and searches the corpus for other compounds containing any pair of those senses. the relation with the highest proportional co-occurrence with any sense pair is returned as the correct relation for the compound. this algorithm was tested using a 'leave-one-out' procedure on the corpus of compounds. the algorithm identified the correct relations for compounds with high precision: in 92% of cases where a relation was found with a proportional co-occurrence of 1.0, it was the correct relation for the compound being disambiguated.
alignment of multiple languages for historical comparison. an essential step in comparative reconstruction is to align corresponding phonological segments in the words being compared. to do this, one must search among huge numbers of potential alignments to find those that give a good phonetic fit. this is a hard computational problem, and it becomes exponentially more difficult when more than two strings are being aligned. in this paper i extend the guided-search alignment algorithm of covington (computational linguistics, 1996) to handle more than two strings. the resulting algorithm has been implemented in prolog and gives reasonable results when tested on data from several languages.
reference to locations. we propose a semantics for locative expressions such as near jones or west of denver, an important subsystem for nlp applications. locative expressions denote regions of space, and serve as arguments to predicates, locating objects and events spatially. since simple locatives occupy argument positions, they do not participate in scope ambiguities---pace one common view, which sees locatives as logical operators. our proposal justifies common representational practice in computational linguistics, accounting for how locative expressions function anaphorically, and explaining a wide range of inference involving locatives. we further demonstrate how the argument analysis may accommodate multiple locative arguments in a single predicate. the analysis is implemented for use in a database query application.
a computational semantics for natural language. in the new head-driven phrase structure grammar (hpsg) language processing system that is currently under development at hewlett-packard laboratories, the montagovian semantics of the earlier gpsg system (see [gawron et al. 1982]) is replaced by a radically different approach with a number of distinct advantages. in place of the lambda calculus and standard first-order logic, our medium of conceptual representation is a new logical formalism called nflt (neo-fregean language of thought); compositional semantics is effected, not by schematic lambda expressions, but by lisp procedures that operate on nflt expressions to produce new expressions. nflt has a number of features that make it well-suited for natural language translations, including predicates of variable arity in which explicitly marked situational roles supercede order-coded argument positions, sortally restricted quantification, a compositional (but nonextensional) semantics that handles causal contexts, and a principled conceptual raising mechanism that we expect to lead to a computationally tractable account of propositional attitudes. the use of semantically compositional lisp procedures in place of lambda-schemas allows us to produce fully reduced translations on the fly, with no need for post-processing. this approach should simplify the task of using semantic information (such as sortal incompatibilities) to eliminate bad parse paths.
automatically extracting nominal mentions of events with a bootstrapped probabilistic classifier. most approaches to event extraction focus on mentions anchored in verbs. however, many mentions of events surface as noun phrases. detecting them can increase the recall of event extraction and provide the foundation for detecting relations between events. this paper describes a weakly-supervised method for detecting nominal event mentions that combines techniques from word sense disambiguation (wsd) and lexical acquisition to create a classifier that labels noun phrases as denoting events or non-events. the classifier uses boot-strapped probabilistic generative models of the contexts of events and non-events. the contexts are the lexically-anchored semantic dependency relations that the nps appear in. our method dramatically improves with bootstrapping, and comfortably outperforms lexical lookup methods which are based on very much larger hand-crafted resources.
unsupervised segmentation of words using prior distributions of morph length and frequency. we present a language-independent and unsupervised algorithm for the segmentation of words into morphs. the algorithm is based on a new generative probabilistic model, which makes use of relevant prior information on the length and frequency distributions of morphs in a language. our algorithm is shown to outperform two competing algorithms, when evaluated on data from a language with agglutinative morphology (finnish), and to perform well also on english data.
veins theory: a model of global discourse cohesion and coherence. in this paper, we propose a generalization of centering theory (ct) (grosz, joshi, weinstein (1995)) called veins theory (vt), which extends the applicability of centering rules from local to global discourse. a key facet of the theory involves the identification of &laquo;veins&raquo; over discourse structure trees such as those defined in rst, which delimit domains of referential accessibility for each unit in a discourse. once identified, reference chains can be extended across segment boundaries, thus enabling the application of ct over the entire discourse. we describe the processes by which veins are defined over discourse structure trees and how ct can be applied to global discourse by using these chains. we also define a discourse &laquo;smoothness&raquo; index which can be used to compare different discourse structures and interpretations, and show how vt can be used to abstract a span of text in the context of the whole discourse. finally, we validate our theory by analyzing examples from corpora of english, french, and romanian.
expectations in incremental discourse processing. the way in which discourse features express connections back to the previous discourse has been described in the literature in terms of adjoining at the right frontier of discourse structure. but this does not allow for discourse features that express expectations about what is to come in the subsequent discourse. after characterizing these expectations and their distribution in text, we show how an approach that makes use of substitution as well as adjoining on a suitably defined right frontier, can be used to both process expectations and constrain discouse processing in general.
sub-sentential alignment using substring co-occurrence counts. in this paper, we will present an efficient method to compute the co-occurrence counts of any pair of substring in a parallel corpus, and an algorithm that make use of these counts to create sub-sentential alignments on such a corpus. this algorithm has the advantage of being as general as possible regarding the segmentation of text.
constraint-based event recognition for information extraction. we present a program for segmenting texts according to the separate events they describe. a modular architecture is described that allows us to examine the contributions made by particular aspects of natural language to event structuring. this is applied in the context of terrorist news articles, and a technique is suggested for evaluating the resulting segmentations. we also examine the usefulness of various heuristics in forming these segmentations.
an integrated archictecture for shallow and deep processing. we present an architecture for the integration of shallow and deep nlp components which is aimed at flexible combination of different language technologies for a range of practical current and future applications. in particular, we describe the integration of a high-level hpsg parsing system with different high-performance shallow components, ranging from named entity recognition to chunk parsing and shallow clause recognition. the nlp components enrich a representation of natural language text with layers of new xml meta-information using a single shared data structure, called the text chart. we describe details of the integration methods, and show how information extraction and language checking applications for realworld german text benefit from a deep grammatical analysis.
automatic semantic tagging of unknown proper names. implemented methods for proper names recognition rely on large gazetteers of common proper nouns and a set of heuristic rules (e.g. mr. as an indicator of a person entity type). though the performance of current pn recognizers is very high (over 90%), it is important to note that this problem is by no means a "solved problem". existing systems perform extremely well on newswire corpora by virtue of the availability of large gazetteers and rule bases designed for specific tasks (e.g. recognition of organization and person entity types as specified in recent message understanding conferences muc).however, large gazetteers are not available for most languages and applications other than newswire texts and, in any case, proper nouns are an open class.in this paper we describe a context-based method to assign an entity type to unknown proper names (pns). like many others, our system relies on a gazetteer and a set of context-dependent heuristics to classify proper nouns. however, due to the unavailability of large gazetteers in italian, over 20% detected pns cannot be semantically tagged.the algorithm that we propose assigns an entity type to an unknown pn based on the analysis of syntactically and semantically similar contexts already seen in the application corpus.the performance of the algorithm is evaluated not only in terms of precision, following the tradition of muc conferences, but also in terms of information gain, an information theoretic measure that takes into account the complexity of the classification task.
language independent, minimally supervised induction of lexical probabilities. a central problem in part-of-speech tagging, especially for new languages for which limited annotated resources are available, is estimating the distribution of lexical probabilities for unknown words. this paper introduces a new paradigmatic similarity measure and presents a minimally supervised learning approach combining effective selection and weighting methods based on paradigmatic and contextual similarity measures populated from large quantities of inexpensive raw text data. this approach is highly language independent and requires no modification to the algorithm or implementation to shift between languages such as french and english.
dependency tree kernels for relation extraction. we extend previous work on tree kernels to estimate the similarity between the dependency trees of sentences. using this kernel within a support vector machine, we detect and classify relations between entities in the automatic content extraction (ace) corpus of news articles. we examine the utility of different features such as wordnet hypernyms, parts of speech, and entity types, and find that the dependency tree kernel achieves a 20% f1 improvement over a "bag-of-words" kernel.
supersense tagging of unknown nouns using semantic similarity. the limited coverage of lexical-semantic resources is a significant problem for nlp systems which can be alleviated by automatically classifying the unknown words. supersense tagging assigns unknown nouns one of 26 broad semantic categories used by lexicographers to organise their manual insertion into wordnet. ciaramita and johnson (2003) present a tagger which uses synonym set glosses as annotated training examples. we describe an unsupervised approach, based on vector-space similarity, which does not require annotated examples but significantly outperforms their tagger. we also demonstrate the use of an extremely large shallow-parsed corpus for calculating vector-space semantic similarity.