waashk/dblp huggingface dataset:
using cycles and quasi-cycles to disambiguate dictionary glosses. we present a novel graph-based algorithm for the automated disambiguation of glosses in lexical knowledge resources. a dictionary graph is built starting from senses (vertices) and explicit or implicit relations in the dictionary (edges). the approach is based on the identification of edge sequences which constitute cycles in the dictionary graph (possibly with one edge reversed) and relate a source to a target word sense. experiments are performed on the disambiguation of ambiguous words in the glosses of wordnet and two machine-readable dictionaries.
personalizing pagerank for word sense disambiguation. in this paper we propose a new graph-based method that uses the knowledge in a lkb (based on wordnet) in order to perform unsupervised word sense disambiguation. our algorithm uses the full graph of the lkb efficiently, performing better than previous approaches in english all-words datasets. we also show that the algorithm can be easily ported to other languages with good results, with the only requirement of having a wordnet. in addition, we make an analysis of the performance of the algorithm, showing that it is efficient and that it could be tuned to be faster.
structural, transitive and latent models for biographic fact extraction. this paper presents six novel approaches to biographic fact extraction that model structural, transitive and latent properties of biographical data. the ensemble of these proposed models substantially outperforms standard pattern-based biographic fact extraction methods and performance is further improved by modeling inter-attribute correlations and distributions over functions of attributes, achieving an average extraction accuracy of 80% over seven types of biographic attributes.
learning efficient parsing. a corpus-based technique is described to improve the efficiency of wide-coverage high-accuracy parsers. by keeping track of the derivation steps which lead to the best parse for a very large collection of sentences, the parser learns which parse steps can be filtered without significant loss in parsing accuracy, but with an important increase in parsing efficiency. an interesting characteristic of our approach is that it is self-learning, in the sense that it uses unannotated corpora.
incremental dialogue processing in a micro-domain. this paper describes a fully incremental dialogue system that can engage in dialogues in a simple domain, number dictation. because it uses incremental speech recognition and prosodic analysis, the system can give rapid feedback as the user is speaking, with a very short latency of around 200ms. because it uses incremental speech synthesis and self-monitoring, the system can react to feedback from the user as the system is speaking. a comparative evaluation shows that na&iuml;ve users preferred this system over a non-incremental version, and that it was perceived as more human-like.
improving grammaticality in statistical sentence generation: introducing a dependency spanning tree algorithm with an argument satisfaction model. abstract-like text summarisation requires a means of producing novel summary sentences. in order to improve the grammaticality of the generated sentence, we model a global (sentence) level syntactic structure. we couch statistical sentence generation as a spanning tree problem in order to search for the best dependency tree spanning a set of chosen words. we also introduce a new search algorithm for this task that models argument satisfaction to improve the linguistic validity of the generated tree. we treat the allocation of modifiers to heads as a weighted bipartite graph matching (or assignment) problem, a well studied problem in graph theory. using bleu to measure performance on a string regeneration task, we found an improvement, illustrating the benefit of the spanning tree approach armed with an argument satisfaction model.
end-to-end evaluation in simultaneous translation. this paper presents the end-to-end evaluation of an automatic simultaneous translation system, built with state-of-the-art components. it shows whether, and for which situations, such a system might be advantageous when compared to a human interpreter. using speeches in english translated into spanish, we present the evaluation procedure and we discuss the results both for the recognition and translation components as well as for the overall system. even if the translation process remains the achilles' heel of the system, the results show that the system can keep at least half of the information, becoming potentially useful for final users.
analysing wikipedia and gold-standard corpora for ner training. named entity recognition (ner) for english typically involves one of three gold standards: muc, conll, or bbn, all created by costly manual annotation. recent work has used wikipedia to automatically create a massive corpus of named entity annotated text. we present the first comprehensive cross-corpus evaluation of ner. we identify the causes of poor cross-corpus performance and demonstrate ways of making them more compatible. using our process, we develop a wikipedia corpus which outperforms gold standard corpora on cross-corpus evaluation by up to 11%.
semi-supervised polarity lexicon induction. we present an extensive study on the problem of detecting polarity of words. we consider the polarity of a word to be either positive or negative. for example, words such as good, beautiful, and wonderful are considered as positive words; whereas words such as bad, ugly, and sad are considered negative words. we treat polarity detection as a semi-supervised label propagation problem in a graph. in the graph, each node represents a word whose polarity is to be determined. each weighted edge encodes a relation that exists between two words. each node (word) can have two labels: positive or negative. we study this framework in two different resource availability scenarios using wordnet and openoffice thesaurus when wordnet is not available. we report our results on three different languages: english, french, and hindi. our results indicate that label propagation improves significantly over the baseline and other semi-supervised learning methods like mincuts and randomized mincuts for this task.
weakly supervised part-of-speech tagging for morphologically-rich, resource-scarce languages. this paper examines unsupervised approaches to part-of-speech (pos) tagging for morphologically-rich, resource-scarce languages, with an emphasis on goldwater and griffiths's (2007) fully-bayesian approach originally developed for english pos tagging. we argue that existing unsupervised pos taggers unrealistically assume as input a perfect pos lexicon, and consequently, we propose a weakly supervised fully-bayesian approach to pos tagging, which relaxes the unrealistic assumption by automatically acquiring the lexicon from a small amount of pos-tagged data. since such relaxation comes at the expense of a drop in tagging accuracy, we propose two extensions to the bayesian framework and demonstrate that they are effective in improving a fully-bayesian pos tagger for bengali, our representative morphologically-rich, resource-scarce language.
treebank grammar techniques for non-projective dependency parsing. an open problem in dependency parsing is the accurate and efficient treatment of non-projective structures. we propose to attack this problem using chart-parsing algorithms developed for mildly context-sensitive grammar formalisms. in this paper, we provide two key tools for this approach. first, we show how to reduce non-projective dependency parsing to parsing with linear context-free rewriting systems (lcfrs), by presenting a technique for extracting lcfrs from dependency treebanks. for efficient parsing, the extracted grammars need to be transformed in order to minimize the number of nonterminal symbols per production. our second contribution is an algorithm that computes this transformation for a large, empirically relevant class of grammars.
dependency trees and the strong generative capacity of ccg. we propose a novel algorithm for extracting dependencies from the derivations of a large fragment of ccg. unlike earlier proposals, our dependency structures are always tree-shaped. we then use these dependency trees to compare the strong generative capacities of ccg and tag and obtain surprising results: both formalisms generate the same languages of derivation trees --- but the mechanisms they use to bring the words in these trees into a linear order are incomparable.
n-gram-based statistical machine translation versus syntax augmented machine translation: comparison and system combination. in this paper we compare and contrast two approaches to machine translation (mt): the cmu-uka syntax augmented machine translation system (samt) and upc-talp n-gram-based statistical machine translation (smt). samt is a hierarchical syntax-driven translation system underlain by a phrase-based model and a target part parse tree. in n-gram-based smt, the translation process is based on bilingual units related to word-to-word alignment and statistical modeling of the bilingual context following a maximum-entropy framework. we provide a step-by-step comparison of the systems and report results in terms of automatic evaluation metrics and required computational resources for a smaller arabic-to-english translation task (1.5m tokens in the training corpus). human error analysis clarifies advantages and disadvantages of the systems under consideration. finally, we combine the output of both systems to yield significant improvements in translation quality.
unsupervised methods for head assignments. we present several algorithms for assigning heads in phrase structure trees, based on different linguistic intuitions on the role of heads in natural language syntax. starting point of our approach is the observation that a head-annotated treebank defines a unique lexicalized tree substitution grammar. this allows us to go back and forth between the two representations, and define objective functions for the unsupervised learning of head assignments in terms of features of the implicit lexicalized tree grammars. we evaluate algorithms based on the match with gold standard head-annotations, and the comparative parsing accuracy of the lexicalized grammars they give rise to. on the first task, we approach the accuracy of hand-designed heuristics for english and inter-annotation-standard agreement for german. on the second task, the implied lexicalized grammars score 4% points higher on parsing accuracy than lexicalized grammars derived by commonly used heuristics.
supervised domain adaption for wsd. the lack of positive results on supervised domain adaptation for wsd have cast some doubts on the utility of hand-tagging general corpora and thus developing generic supervised wsd systems. in this paper we show for the first time that our wsd system trained on a general source corpus (bnc) and the target corpus, obtains up to 22% error reduction when compared to a system trained on the target corpus alone. in addition, we show that as little as 40% of the target corpus (when supplemented with the source corpus) is sufficient to obtain the same results as training on the full target data. the key for success is the use of unlabeled data with svd, a combination of kernels and svm.
fast full parsing by linear-chain conditional random fields. this paper presents a chunking-based discriminative approach to full parsing. we convert the task of full parsing into a series of chunking tasks and apply a conditional random field (crf) model to each level of chunking. the probability of an entire parse tree is computed as the product of the probabilities of individual chunking results. the parsing is performed in a bottom-up manner and the best derivation is efficiently obtained by using a depth-first search algorithm. experimental results demonstrate that this simple parsing framework produces a fast and reasonably accurate parser.
semi-supervised training for the averaged perceptron pos tagger. this paper describes pos tagging experiments with semi-supervised training as an extension to the (supervised) averaged perceptron algorithm, first introduced for this task by (collins, 2002). experiments with an iterative training on standard-sized supervised (manually annotated) dataset (106 tokens) combined with a relatively modest (in the order of 108 tokens) unsupervised (plain) data in a bagging-like fashion showed significant improvement of the pos classification task on typologically different languages, yielding better than state-of-the-art results for english and czech (4.12 % and 4.86 % relative error reduction, respectively; absolute accuracies being 97.44 % and 95.89 %).
improvements in analogical learning: application to translating multi-terms of the medical domain. handling terminology is an important matter in a translation workflow. however, current machine translation (mt) systems do not yet propose anything proactive upon tools which assist in managing terminological databases. in this work, we investigate several enhancements to analogical learning and test our implementation on translating medical terms. we show that the analogical engine works equally well when translating from and into a morphologically rich language, or when dealing with language pairs written in different scripts. combining it with a phrase-based statistical engine leads to significant improvements.
reconstructing false start errors in spontaneous speech text. this paper presents a conditional random field-based approach for identifying speaker-produced disfluencies (i.e. if and where they occur) in spontaneous speech transcripts. we emphasize false start regions, which are often missed in current disfluency identification approaches as they lack lexical or structural similarity to the speech immediately following. we find that combining lexical, syntactic, and language model-related features with the output of a state-of-the-art disfluency identification system improves overall word-level identification of these and other errors. improvements are reinforced under a stricter evaluation metric requiring exact matches between cleaned sentences annotator-produced reconstructions, and altogether show promise for general reconstruction efforts.
correcting a pos-tagged corpus using three complementary methods. the quality of the part-of-speech (pos) annotation in a corpus is crucial for the development of pos taggers. in this paper, we experiment with three complementary methods for automatically detecting errors in the pos annotation for the icelandic frequency dictionary corpus. the first two methods are language independent and we argue that the third method can be adapted to other morphologically complex languages. once possible errors have been detected, we examine each error candidate and hand-correct the corresponding pos tag if necessary. overall, based on the three methods, we hand-correct the pos tagging of 1,334 tokens (0.23% of the tokens) in the corpus. furthermore, we re-evaluate existing state-of-the-art pos taggers on icelandic text using the corrected corpus.
slacker semantics: why superficiality, dependency and avoidance of commitment can be the right way to go. this paper discusses computational compositional semantics from the perspective of grammar engineering, in the light of experience with the use of minimal recursion semantics in delph-in grammars. the relationship between argument indexation and semantic role labelling is explored and a semantic dependency notation (dmrs) is introduced.
automatic single-document key fact extraction from newswire articles. this paper addresses the problem of extracting the most important facts from a news article. our approach uses syntactic, semantic, and general statistical features to identify the most important sentences in a document. the importance of the individual features is estimated using generalized iterative scaling methods trained on an annotated newswire corpus. the performance of our approach is evaluated against 300 unseen news articles and shows that use of these features results in statistically significant improvements over a provenly robust baseline, as measured using metrics such as precision, recall and rouge.
who is "you"? combining linguistic and gaze features to resolve second-person references in dialogue. we explore the problem of resolving the second person english pronoun you in multi-party dialogue, using a combination of linguistic and visual features. first, we distinguish generic and referential uses, then we classify the referential uses as either plural or singular, and finally, for the latter cases, we identify the addressee. in our first set of experiments, the linguistic and visual features are derived from manual transcriptions and annotations, but in the second set, they are generated through entirely automatic means. results show that a multimodal system is often preferable to a unimodal one.
a logic of semantic representations for shallow parsing. one way to construct semantic representations in a robust manner is to enhance shallow language processors with semantic components. here, we provide a model theory for a semantic formalism that is designed for this, namely robust minimal recursion semantics (rmrs). we show that rmrs supports a notion of entailment that allows it to form the basis for comparing the semantic output of different parses of varying depth.
cube summing, approximate inference with non-local features, and dynamic programming without semirings. we introduce cube summing, a technique that permits dynamic programming algorithms for summing over structures (like the forward and inside algorithms) to be extended with non-local features that violate the classical structural independence assumptions. it is inspired by cube pruning (chiang, 2007; huang and chiang, 2007) in its computation of non-local features dynamically using scored k-best lists, but also maintains additional residual quantities used in calculating approximate marginals. when restricted to local features, cube summing reduces to a novel semiring (k-best+residual) that generalizes many of the semirings of goodman (1999). when non-local features are included, cube summing does not reduce to any semiring, but is compatible with generic techniques for solving dynamic programming equations.
growing finely-discriminating taxonomies from seeds of varying quality and size. concept taxonomies offer a powerful means for organizing knowledge, but this organization must allow for many overlapping and fine-grained perspectives if a general-purpose taxonomy is to reflect concepts as they are actually employed and reasoned about in everyday usage. we present here a means of bootstrapping finely-discriminating taxonomies from a variety of different starting points, or seeds, that are acquired from three different sources: wordnet, conceptnet and the web at large.
semi-supervised semantic role labeling. large scale annotated corpora are prerequisite to developing high-performance semantic role labeling systems. unfortunately, such corpora are expensive to produce, limited in size, and may not be representative. our work aims to reduce the annotation effort involved in creating resources for semantic role labeling via semi-supervised learning. our algorithm augments a small number of manually labeled instances with unlabeled examples whose roles are inferred automatically via annotation projection. we formulate the projection task as a generalization of the linear assignment problem. we seek to find a role assignment in the unlabeled data such that the argument similarity between the labeled and unlabeled instances is maximized. experimental results on semantic role labeling show that the automatic annotations produced by our method improve performance over using hand-labeled instances alone.
a robust and extensible exemplar-based model of thematic fit. this paper presents a new, exemplar-based model of thematic fit. in contrast to previous models, it does not approximate thematic fit as argument plausibility or 'fit with verb selectional preferences', but directly as semantic role plausibility for a verb-argument pair, through similarity-based generalization from previously seen verb-argument pairs. this makes the model very robust for data sparsity. we argue that the model is easily extensible to a model of semantic role ambiguity resolution during online sentence comprehension. the model is evaluated on human semantic role plausibility judgments. its predictions correlate significantly with the human judgments. it rivals two state-of-the-art models of thematic fit and exceeds their performance on previously unseen or low-frequency items.
incremental parsing with parallel multiple context-free grammars. parallel multiple context-free grammar (pmcfg) is an extension of context-free grammar for which the recognition problem is still solvable in polynomial time. we describe a new parsing algorithm that has the advantage to be incremental and to support pmcfg directly rather than the weaker mcfg formalism. the algorithm is also top-down which allows it to be used for grammar based word prediction.
using non-lexical features to identify effective indexing terms for biomedical illustrations. automatic image annotation is an attractive approach for enabling convenient access to images found in a variety of documents. since image captions and relevant discussions found in the text can be useful for summarizing the content of images, it is also possible that this text can be used to generate salient indexing terms. unfortunately, this problem is generally domain-specific because indexing terms that are useful in one domain can be ineffective in others. thus, we present a supervised machine learning approach to image annotation utilizing non-lexical features extracted from image-related text to select useful terms. we apply this approach to several subdomains of the biomedical sciences and show that we are able to reduce the number of ineffective indexing terms.
data-driven semantic analysis for multilingual wsd and lexical selection in translation. a common way of describing the senses of ambiguous words in multilingual word sense disambiguation (wsd) is by reference to their translation equivalents in another language. the theoretical soundness of the senses induced in this way can, however, be doubted. this type of cross-lingual sense identification has implications for multilingual wsd and mt evaluation as well. in this article, we first present some arguments in favour of a more thorough analysis of the semantic information that may be induced by the equivalents of ambiguous words found in parallel corpora. then, we present an unsupervised wsd method and a lexical selection method that exploit the results of a data-driven sense induction method. finally, we show how this automatically acquired information can be exploited for a multilingual wsd and mt evaluation more sensitive to lexical semantics.
em works for pronoun anaphora resolution. we present an algorithm for pronoun-anaphora (in english) that uses expectation maximization (em) to learn virtually all of its parameters in an unsupervised fashion. while em frequently fails to find good models for the tasks to which it is set, in this case it works quite well. we have compared it to several systems available on the web (all we have found so far). our program significantly outperforms all of them. the algorithm is fast and robust, and has been made publically available for downloading.