waashk/dblp huggingface dataset:
graph transformations in data-driven dependency parsing. transforming syntactic representations in order to improve parsing accuracy has been exploited successfully in statistical parsing systems using constituency-based representations. in this paper, we show that similar transformations can give substantial improvements also in data-driven dependency parsing. experiments on the prague dependency treebank show that systematic transformations of coordinate structures and verb groups result in a 10% error reduction for a deterministic data-driven dependency parser. combining these transformations with previously proposed techniques for recovering non-projective dependencies leads to state-of-the-art accuracy for the given data set.
phoneme-to-text transcription system with an infinite vocabulary. the noisy channel model approach is successfully applied to various natural language processing tasks. currently the main research focus of this approach is adaptation methods, how to capture characteristics of words and expressions in a target domain given example sentences in that domain. as a solution we describe a method enlarging the vocabulary of a language model to an almost infinite size and capturing their context information. especially the new method is suitable for languages in which words are not delimited by whitespace. we applied our method to a phoneme-to-text transcription task in japanese and reduced about 10% of the errors in the results of an existing method.
coreference for nlp applications. this paper presents several techniques for performing automatic coreference annotation and performance results for each of them. to demonstrate that they can be applied to real-world data, we have built a simple question-answering system which uses the techniques. a system using coreference is compared to a baseline system with the result that the addition of the coreference annotation improves performance.
syntactic features and word similarity for supervised metonymy resolution. we present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. we show that syntactic head-modifier relations are a high precision feature for metonymy recognition but suffer from data sparseness. we partially overcome this problem by integrating a thesaurus and introducing simpler grammatical features, thereby preserving precision and increasing recall. our algorithm generalises over two levels of contextual similarity. resulting inferences exceed the complexity of inferences undertaken in word sense disambiguation. we also compare automatic and manual methods for syntactic feature extraction.
a study on convolution kernels for shallow statistic parsing. in this paper we have designed and experimented novel convolution kernels for automatic classification of predicate arguments. their main property is the ability to process structured representations. support vector machines (svms), using a combination of such kernels and the flat feature kernel, classify prop-bank predicate arguments with accuracy higher than the current argument classification state-of-the-art.additionally, experiments on framenet data have shown that svms are appealing for the classification of semantic roles even if the proposed kernels do not produce any improvement.
learning word sense with feature selection and order identification capabilities. this paper presents an unsupervised word sense learning algorithm, which induces senses of target word by grouping its occurrences into a "natural" number of clusters based on the similarity of their contexts. for removing noisy words in feature set, feature selection is conducted by optimizing a cluster validation criterion subject to some constraint in an unsupervised manner. gaussian mixture model and minimum description length criterion are used to estimate cluster structure and cluster number. experimental results show that our algorithm can find important feature subset, estimate model order (cluster number) and achieve better performance than another algorithm which requires cluster number to be provided.
investigating cue selection and placement in tutorial discourse. our goal is to identify the features that predict cue selection and placement in order to devise strategies for automatic text generation. much previous work in this area has relied on ad hoc methods. our coding scheme for the exhaustive analysis of discourse allows a systematic evaluation and refinement of hypotheses concerning cues. we report two results based on this analysis: a comparison of the distribution of since and because in our corpus, and the impact of embeddedness on cue selection.
word sense disambiguation using label propagation based semi-supervised learning. shortage of manually sense-tagged data is an obstacle to supervised word sense disambiguation methods. in this paper we investigate a label propagation based semi-supervised learning algorithm for wsd, which combines labeled and unlabeled data in learning process to fully realize a global consistency assumption: similar examples should have similar labels. our experimental results on benchmark corpora indicate that it consistently outperforms svm when only very few labeled examples are available, and its performance is also better than monolingual bootstrapping, and comparable to bilingual bootstrapping.
semantic information preprocessing for natural language interfaces to databases. an approach is described for supplying selectional restrictions to parsers in natural language interfaces (nlis) to databases by extracting the selectional restrictions from semantic descriptions of those nlis. automating the process of finding selectional restrictions reduces nli development time and may avoid errors introduced by hand-coding selectional restrictions.
a bootstrapping approach to named entity classification using successive learners. this paper presents a new bootstrapping approach to named entity (ne) classification. this approach only requires a few common noun/pronoun seeds that correspond to the concept for the target ne type, e.g. he/she/man/woman for person ne. the entire bootstrapping procedure is implemented as training two successive learners: (i) a decision list is used to learn the parsing-based high precision ne rules; (ii) a hidden markov model is then trained to learn string sequence-based ne patterns. the second learner uses the training corpus automatically tagged by the first learner. the resulting ne system approaches supervised ne performance for some ne types. the system also demonstrates intuitive support for tagging user-defined ne types. the differences of this approach from the co-training-based ne bootstrapping are also discussed.
dealing with distinguishing descriptions in a guided composition system. the goal of this paper is to provide computable account for some definite descriptions. to this end, we define in terms of inclusion the notion of distinguishing description and of distinguishable entities introduced by [dale 89]. these definitions allow us to give conditions of wellformedness for incomplete distinguishing descriptions. we also extend the notion of distinguishing description to take into account cases of synonymy and hyponymy.we describe a real application of a guided composition system where this sort of expressions arise.
weakly supervised learning for cross-document person name disambiguation supported by information extraction. it is fairly common that different people are associated with the same name. in tracking person entities in a large document pool, it is important to determine whether multiple mentions of the same name across documents refer to the same entity or not. previous approach to this problem involves measuring context similarity only based on co-occurring words. this paper presents a new algorithm using information extraction support in addition to co-occurring words. a learning scheme with minimal supervision is developed within the bayesian framework. maximum entropy modeling is then used to represent the probability distribution of context similarities based on heterogeneous features. statistical annealing is applied to derive the final entity coreference chains by globally fitting the pairwise context similarities. benchmarking shows that our new approach significantly outperforms the existing algorithm by 25 percentage points in overall f-measure.
applying co-training to reference resolution. in this paper, we investigate the practical applicability of co-training for the task of building a classifier for reference resolution. we are concerned with the question if co-training can significantly reduce the amount of manual labeling work and still produce a classifier with an acceptable performance.
right association revisited. consideration of when right association works and when it fails lead to a restatement of this parsing principle in terms of the notion of heaviness. a computational investigation of a syntactically annotated corpus provides evidence for this proposal and suggest circumstances when ra is likely to make correct attachment predictions.
aligning a parallel english-chinese corpus statistically with lexical criteria. we describe our experience with automatic alignment of sentences in parallel english-chinese texts. our report concerns three related topics: (1) progress on the hkust english-chinese parallel bilingual corpus; (2) experiments addressing the applicability of gale & church's (1991) length-based statistical method to the task of alignment involving a non-indo-european language; and (3) an improved statistical method that also incorporates domain-specific lexical cues.
automatic detection of syllable boundaries combining the advantages of treebank and bracketed corpora training. an approach to automatic detection of syllable boundaries is presented. we demonstrate the use of several manually constructed grammars trained with a novel algorithm combining the advantages of treebank and bracketed corpora training. we investigate the effect of the training corpus size on the performance of our system. the evaluation shows that a hand-written grammar performs better on finding syllable boundaries than does a treebank grammar.
a psycholinguistically motivated parser for ccg. considering the speed in which humans resolve syntactic ambiguity, and the overwhelming evidence that syntactic ambiguity is resolved through selection of the analysis whose interpretation is the most 'sensible', one comes to the conclusion that interpretation, hence parsing take place incrementally, just about every word. considerations of parsimony in the theory of the syntactic processor lead one to explore the simplest of parsers: one which represents only analyses as defined by the grammar and no other information.toward this aim of a simple, incremental parser i explore the proposal that the competence grammar is a combinatory categorial grammar (ccg). i address the problem of the proliferating analyses that stem from ccg's associativity of derivation. my solution involves maintaining only the maximally incremental analysis and, when necessary, computing the maximally right-branching analysis. i use results from the study of rewrite systems to show that this computation is efficient.
an algorithm for simultaneously bracketing parallel texts by aligning words. we describe a grammarless method for simultaneously bracketing both halves of a parallel text and giving word alignments, assuming only a translation lexicon for the language pair. we introduce inversion-invariant transduction grammars which serve as generative models for parallel bilingual sentences with weak order constraints. focusing on transduction grammars for bracketing, we formulate a normal form, and a stochastic version amenable to a maximum-likelihood bracketing algorithm. several extensions and experiments are discussed.
a flexible stand-off data model with query language for multi-level annotation. we present an implemented xml data model and a new, simplified query language for multi-level annotated corpora. the new query language involves automatic conversion of queries into the underlying, more complicated mmaxql query language. it supports queries for sequential and hierarchical, but also associative (e.g. coreferential) relations. the simplified query language has been designed with non-expert users in mind.
pseudo-projective dependency parsing. in order to realize the full potential of dependency-based syntactic parsing, it is desirable to allow non-projective dependency structures. we show how a data-driven deterministic dependency parser, in itself restricted to projective structures, can be combined with graph transformation techniques to produce non-projective structures. experiments using data from the prague dependency treebank show that the combined system can handle non-projective constructions with a precision sufficient to yield a significant improvement in overall parsing accuracy. this leads to the best reported performance for robust non-projective parsing of czech.
inducing probabilistic syllable classes using multivariate clustering. an approach to automatic detection of syllable structure is presented. we demonstrate a novel application of em-based clustering to multivariate data, exemplified by the induction of 3- and 5-dimensional probabilistic syllable classes. the qualitative evaluation shows that the method yields phonologically meaningful syllable classes. we then propose a novel approach to grapheme-to-phoneme conversion and show that syllable structure represents valuable information for pronunciation systems.
a markov language learning model for finite parameter spaces. this paper shows how to formally characterize language learning in a finite parameter space as a markov structure. important new language learning results follow directly: explicitly calculated sample complexity learning times under different input distribution assumptions (inclding childes database language input) and learning regimes. we also briefly describe a new way to formally model (rapid) diachronic syntax change.
extracting parallel sub-sentential fragments from non-parallel corpora. we present a novel method for extracting parallel sub-sentential fragments from comparable, non-parallel bilingual corpora. by analyzing potentially similar sentence pairs using a signal processing-inspired approach, we detect which segments of the source sentence are translated into segments in the target sentence, and which are not. this method enables us to extract useful machine translation training data even from very non-parallel corpora, which contain no parallel sentence pairs. we evaluate the quality of the extracted data by showing that it improves the performance of a state-of-the-art statistical machine translation system.
difficulty indices for the named entity task in japanese. we propose indices to measure the difficulty of the named entity (ne) task by looking at test corpora, based on expressions inside and outside the nes. these indices are intended to estimate the difficulty of each task without actually using an ne system and to be unbiased towards a specific system. the values of the indices are compared with the systems' performance in japanese documents. we also discuss the difference between ne classes with the indices and show useful clues which will make it easier to recognize nes.
optimizing typed feature structure grammar parsing through non-statistical indexing. this paper introduces an indexing method based on static analysis of grammar rules and type signatures for typed feature structure grammars (tfsgs). the static analysis tries to predict at compile-time which feature paths will cause unification failure during parsing at run-time. to support the static analysis, we introduce a new classification of the instances of variables used in tfsgs, based on what type of structure sharing they create. the indexing actions that can be performed during parsing are also enumerated. non-statistical indexing has the advantage of not requiring training, and, as the evaluation using large-scale hpsgs demonstrates, the improvements are comparable with those of statistical optimizations. such statistical optimizations rely on data collected during training, and their performance does not always compensate for the training costs.
multi-engine machine translation with voted language model. the paper describes a particular approach to multiengine machine translation (memt), where we make use of voted language models to selectively combine translation outputs from multiple off-the-shelf mt systems. experiments are done using large corpora from three distinct domains. the study found that the use of voted language models leads to an improved performance of memt systems.
an estimate of referent of noun phrases in japanese sentences. in machine translation and man-machine dialogue, it is important to clarify referents of noun phrases. we present a method for determining the referents of noun phrases in japanese sentences by using the referential properties, modifiers, and possessors of noun phrases. since the japanese language has no articles, it is difficult to decide whether a noun phrase has an antecedent or not. we had previously estimated the referential properties of noun phrases that correspond to articles by using clue words in the sentences (murata and nagao 1993). by using these referential properties, our system determined the referents of noun phrases in japanese sentences. furthermore we used the modifiers and possessors of noun phrases in determining the referents of noun phrases. as a result, on training sentences we obtained a precision rate of 82% and a recall rate of 85% in the determination of the referents of noun phrases that have antecedents. on test sentences, we obtained a precision rate of 79% and a recall rate of 77%.
supervised ranking in open-domain text summarization. the paper proposes and empirically motivates an integration of supervised learning with unsupervised learning to deal with human biases in summarization. in particular, we explore the use of probabilistic decision tree within the clustering framework to account for the variation as well as regularity in human created summaries. the corpus of human created extracts is created from a newspaper corpus and used as a test set. we build probabilistic decision trees of different flavors and integrate each of them with the clustering framework. experiments with the corpus demonstrate that the mixture of the two paradigms generally gives a significant boost in performance compared to cases where either of the two is considered alone.
leveraging reusability: cost-effective lexical acquisition for large-scale ontology translation. thesauri and ontologies provide important value in facilitating access to digital archives by representing underlying principles of organization. translation of such resources into multiple languages is an important component for providing multilingual access. however, the specificity of vocabulary terms in most ontologies precludes fully-automated machine translation using general-domain lexical resources. in this paper, we present an efficient process for leveraging human translations when constructing domain-specific lexical resources. we evaluate the effectiveness of this process by producing a probabilistic phrase dictionary and translating a thesaurus of 56,000 concepts used to catalogue a large archive of oral histories. our experiments demonstrate a cost-effective technique for accurate machine translation of large ontologies.
error mining for wide-coverage grammar engineering. parsing systems which rely on hand-coded linguistic descriptions can only perform adequately in as far as these descriptions are correct and complete.the paper describes an error mining technique to discover problems in hand-coded linguistic descriptions for parsing such as grammars and lexicons. by analysing parse results for very large unannotated corpora, the technique discovers missing, incorrect or incomplete linguistic descriptions.the technique uses the frequency of n-grams of words for arbitrary values of n. it is shown how a new combination of suffix arrays and perfect hash finite automata allows an efficient implementation.
automatic text summarization based on the global document annotation. the gda (global document annotation) project proposes a tag set which allows machines to automatically infer the underlying semantic/pragmatic structure of documents. its objectives are to promote development and spread of nlp/ai applications to render gda-tagged documents versatile and intelligent contents, which should motivate www (world wide web) users to tag their documents as part of content authoring. this paper discusses automatic text summarization based on gda. its main features are a domain/style-free algorithm and personalization on summarization which reflects readers' interests and preferences. in order to calculate the importance score of a text element, the algorithm uses spreading activation on an intradocument network which connects text elements via thematic, rhetorical, and coreferential relations. the proposed method is flexible enough to dynamically generate summaries of various sizes. a summary browser supporting personalization is reported as well.
head corner parsing for discontinuous constituency. i describe a head-driven parser for a class of grammars that handle discontinuous constituency by a richer notion of string combination than ordinary concatenation. the parser is a generalization of the left-corner parser (matsumoto et al., 1983) and can be used for grammars written in powerful formalisms such as non-concatenative versions of hpsg (pollard, 1984; reape, 1989).
speech dialogue with facial displays: multimodal human-computer conversation. human face-to-face conversation is an ideal model for human-computer dialogue. one of the major features of face-to-face communication is its multiplicity of communication channels that act on multiple modalities. to realize a natural multimodal dialogue, it is necessary to study how humans perceive information and determine the information to which humans are sensitive. a face is an independent communication channel that conveys emotional and conversational signals, encoded as facial expressions. we have developed an experimental system that integrates speech dialogue and facial animation, to investigate the effect of introducing communicative facial expressions as a new modality in human-computer conversation. our experiments have showen that facial expressions are helpful, especially upon first contact with the system. we have also discovered that featuring facial expressions at an early stage improves subsequent interaction.
the intersection of finite state automata and definite clause grammars. bernard lang defines parsing as the calculation of the intersection of a fsa (the input) and a cfg. viewing the input for parsing as a fsa rather than as a string combines well with some approaches in speech understanding systems, in which parsing takes a word lattice as input (rather than a word string). furthermore, certain techniques for robust parsing can be modelled as finite state transducers.in this paper we investigate how we can generalize this approach for unification grammars. in particular we will concentrate on how we might the calculation of the intersection of a fsa and a dcg. it is shown that existing parsing algorithms can be easily extended for fsa inputs. however, we also show that the termination properties change drastically: we show that it is undecidable whether the intersection of a fsa and a dcg is empty (even if the dcg is off-line parsable).furthermore we discuss approaches to cope with the problem.