waashk/dblp huggingface dataset:
exploiting n-best hypotheses for smt self-enhancement. word and n-gram posterior probabilities estimated on n-best hypotheses have been used to improve the performance of statistical machine translation (smt) in a rescoring framework. in this paper, we extend the idea to estimate the posterior probabilities on n-best hypotheses for translation phrase-pairs, target language n-grams, and source word reorderings. the smt system is self-enhanced with the posterior knowledge learned from n-best hypotheses in a re-decoding framework. experiments on nist chinese-to-english task show performance improvements for all the strategies. moreover, the combination of the three strategies achieves further improvements and outperforms the baseline by 0.67 bleu score on nist-2003 set, and 0.64 on nist-2005 set, respectively.
robust extraction of named entity including unfamiliar word. this paper proposes a novel method to extract named entities including unfamiliar words which do not occur or occur few times in a training corpus using a large unannotated corpus. the proposed method consists of two steps. the first step is to assign the most similar and familiar word to each unfamiliar word based on their context vectors calculated from a large unannotated corpus. after that, traditional machine learning approaches are employed as the second step. the experiments of extracting japanese named entities from irex corpus and nhk corpus show the effectiveness of the proposed method.
intrinsic vs. extrinsic evaluation measures for referring expression generation. in this paper we present research in which we apply (i) the kind of intrinsic evaluation metrics that are characteristic of current comparative hlt evaluation, and (ii) extrinsic, human task-performance evaluations more in keeping with nlg traditions, to 15 systems implementing a language generation task. we analyse the evaluation results and find that there are no significant correlations between intrinsic and extrinsic evaluation measures for this task.
mining wikipedia revision histories for improving sentence compression. a well-recognized limitation of research on supervised sentence compression is the dearth of available training data. we propose a new and bountiful resource for such training data, which we obtain by mining the revision history of wikipedia for sentence compressions and expansions. using only a fraction of the available wikipedia data, we have collected a training corpus of over 380,000 sentence pairs, two orders of magnitude larger than the standardly used ziff-davis corpus. using this new-found data, we propose a novel lexicalized noisy channel model for sentence compression, achieving improved results in grammaticality and compression rate criteria with a slight decrease in importance.
lyric-based song sentiment classification with sentiment vector space model. due to the internet's sheer size, complexity, and various routing policies, it is difficult if not impossible to locate the causes of large volumes of bgp update messages that occur from time to time. to provide dependable global data delivery we need diagnostic tools that can pinpoint the exact connectivity changes. we describe an algorithm, called mvschange that can pin down the origin of routing changes due to any single link failure or link restoration. using a simplified model of bgp, called simple path vector protocol (spvp), and a graph model of the internet, mvschange takes as input the spvp update messages collected from multiple vantage points and accurately locates the link that initiated the routing changes. we provide theoretical proof for the correctness of the design.
using automatically transcribed dialogs to learn user models in a spoken dialog system. we use an em algorithm to learn user models in a spoken dialog system. our method requires automatically transcribed (with asr) dialog corpora, plus a model of transcription errors, but does not otherwise need any manual transcription effort. we tested our method on a voice-controlled telephone directory application, and show that our learned models better replicate the true distribution of user actions than those trained by simpler methods and are very similar to user models estimated from manually transcribed dialogs.
extractive summaries for educational science content. this paper describes an extractive summarizer for educational science content called cogent. cogent extends mead based on strategies elicited from an empirical study with domain and instructional experts. cogent implements a hybrid approach integrating both domain independent sentence scoring features and domain-aware features. initial evaluation results indicate that cogent outperforms existing summarizers and generates summaries that closely resemble those generated by human experts.
learning semantic links from a corpus of parallel temporal and causal relations. finding temporal and causal relations is crucial to understanding the semantic structure of a text. since existing corpora provide no parallel temporal and causal annotations, we annotated 1000 conjoined event pairs, achieving inter-annotator agreement of 81.2% on temporal relations and 77.8% on causal relations. we trained machine learning models using features derived from wordnet and the google n-gram corpus, and they outperformed a variety of baselines, achieving an f-measure of 49.0 for temporals and 52.4 for causals. analysis of these models suggests that additional data will improve performance, and that temporal information is crucial to causal relation identification.
language dynamics and capitalization using maximum entropy. this paper studies the impact of written language variations and the way it affects the capitalization task over time. a discriminative approach, based on maximum entropy models, is proposed to perform capitalization, taking the language changes into consideration. the proposed method makes it possible to use large corpora for training. the evaluation is performed over newspaper corpora using different testing periods. the achieved results reveal a strong relation between the capitalization performance and the elapsed time between the training and testing data periods.
smoothing a tera-word language model. frequency counts from very large corpora, such as the web 1t dataset, have recently become available for language modeling. omission of low frequency n-gram counts is a practical necessity for datasets of this size. naive implementations of standard smoothing methods do not realize the full potential of such large datasets with missing counts. in this paper i present a new smoothing algorithm that combines the dirichlet prior form of (mackay and peto, 1995) with the modified back-off estimates of (kneser and ney, 1995) that leads to a 31% perplexity reduction on the brown corpus compared to a baseline implementation of kneser-ney discounting.
construct state modification in the arabic treebank. earlier work in parsing arabic has speculated that attachment to construct state constructions decreases parsing performance. we make this speculation precise and define the problem of attachment to construct state constructions in the arabic treebank. we present the first statistics that quantify the problem. we provide a baseline and the results from a first attempt at a discriminative learning procedure for this task, achieving 80% accuracy.
the good, the bad, and the unknown: morphosyllabic sentiment tagging of unseen words. the omnipresence of unknown words is a problem that any nlp component needs to address in some form. while there exist many established techniques for dealing with unknown words in the realm of pos-tagging, for example, guessing unknown words' semantic properties is a less-explored area with greater challenges. in this paper, we study the semantic field of sentiment and propose five methods for assigning prior sentiment polarities to unknown words based on known sentiment carriers. tested on 2000 cases, the methods mirror human judgements closely in three- and two-way polarity classification tasks, and reach accuracies above 63% and 81%, respectively.
enriching spoken language translation with dialog acts. we first present the tele-immersive environments developed jointly by university of illinois at urbana-champaign and university of california at berkeley. the environment features 3d full and real body capturing, wide field of view, multi-display 3d rendering, and attachment free participant. we then describe a study of collaborative dancing between remotely located dancers in the shared virtual space. two professional dancers are invited to the tele-immersive site of each university. as a preliminary experiment, we let the dancers perform elementary body movements and coordinate their dancing. the coordination requires one dancer to take the lead while the other follows her by appropriate movements. during the experiment, the dancers are dancing at various motion rates to evaluate how well the collaborative dancing is supported with the current technical boundary. our important findings indicate that 1) tele-immersive environments have strong potential impact on the concept of choreography and communication of live dance performance, 2) the presence of multi-display system, real body 3d rendering, audio channel, and less intrusive-ness greatly enhances the immersive and dancing experience, and 3) the level of synchronization achieved by the dancers is higher than that expected from the video rate
query-based sentence fusion is better defined and leads to more preferred results than generic sentence fusion. we show that question-based sentence fusion is a better defined task than generic sentence fusion (q-based fusions are shorter, display less variety in length, yield more identical results and have higher normalized rouge scores). moreover, we show that in a qa setting, participants strongly prefer q-based fusions over generic ones, and have a preference for union over intersection fusions.
blog categorization exploiting domain dictionary and dynamically estimated domains of unknown words. this paper presents an approach to text categorization that i) uses no machine learning and ii) reacts on-the-fly to unknown words. these features are important for categorizing blog articles, which are updated on a daily basis and filled with newly coined words. we categorize 600 blog articles into 12 domains. as a result, our categorization method achieved an accuracy of 94.0% (564/600).
unlexicalised hidden variable models of split dependency grammars. this paper investigates transforms of split dependency grammars into unlexicalised context-free grammars annotated with hidden symbols. our best unlexicalised grammar achieves an accuracy of 88% on the penn treebank data set, that represents a 50% reduction in error over previously published results on unlexicalised dependency parsing.
dictionary definitions based homograph identification using a generative hierarchical model. a solution to the problem of homograph (words with multiple distinct meanings) identification is proposed and evaluated in this paper. it is demonstrated that a mixture model based framework is better suited for this task than the standard classification algorithms - relative improvement of 7% in f1 measure and 14% in cohen's kappa score is observed.
surprising parser actions and reading difficulty. an incremental dependency parser's probability model is entered as a predictor in a linear mixed-effects model of german readers' eye-fixation durations. this dependency-based predictor improves a baseline that takes into account word length, n-gram probability, and cloze predictability that are typically applied in models of human reading. this improvement obtains even when the dependency parser explores a tiny fraction of its search space, as suggested by narrow-beam accounts of human sentence processing such as garden path theory.
icelandic data driven part of speech tagging. data driven pos tagging has achieved good performance for english, but can still lag behind linguistic rule based taggers for morphologically complex languages, such as icelandic. we extend a statistical tagger to handle fine grained tagsets and improve over the best icelandic pos tagger. additionally, we develop a case tagger for non-local case and gender decisions. an error analysis of our system suggests future directions.
you've got answers: towards personalized models for predicting success in community question answering. question answering communities such as yahoo! answers have emerged as a popular alternative to general-purpose web search. by directly interacting with other participants, information seekers can obtain specific answers to their questions. however, user success in obtaining satisfactory answers varies greatly. we hypothesize that satisfaction with the contributed answers is largely determined by the asker's prior experience, expectations, and personal preferences. hence, we begin to develop personalized models of asker satisfaction to predict whether a particular question author will be satisfied with the answers contributed by the community participants. we formalize this problem, and explore a variety of content, structure, and interaction features for this task using standard machine learning techniques. our experimental evaluation over thousands of real questions indicates that indeed it is beneficial to personalize satisfaction predictions when sufficient prior user history exists, significantly improving accuracy over a "one-size-fits-all" prediction model.
self-training for biomedical parsing. parser self-training is the technique of taking an existing parser, parsing extra data and then creating a second parser by treating the extra data as further training data. here we apply this technique to parser adaptation. in particular, we self-train the standard charniak/johnson penn-treebank parser using unlabeled biomedical abstracts. this achieves an f-score of 84.3% on a standard test set of biomedical abstracts from the genia corpus. this is a 20% error reduction over the best previous result on biomedical data (80.2% on the same test set).
distributed listening: a parallel processing approach to automatic speech recognition. while speech recognition systems have come a long way in the last thirty years, there is still room for improvement. although readily available, these systems are sometimes inaccurate and insufficient. the research presented here outlines a technique called distributed listening which demonstrates noticeable improvements to existing speech recognition methods. the distributed listening architecture introduces the idea of multiple, parallel, yet physically separate automatic speech recognizers called listeners. distributed listening also uses a piece of middleware called an interpreter. the interpreter resolves multiple interpretations using the phrase resolution algorithm (pra). these efforts work together to increase the accuracy of the transcription of spoken utterances.
semantic types of some generic relation arguments: detection and evaluation. this paper presents an approach to detection of the semantic types of relation arguments employing the wordnet hierarchy. using the semeval-2007 data, we show that the method allows to generalize relation arguments with high precision for such generic relations as origin-entity, content-container, instrument-agency and some other.
adapting a wsj-trained parser to grammatically noisy text. we present a robust parser which is trained on a treebank of ungrammatical sentences. the treebank is created automatically by modifying penn treebank sentences so that they contain one or more syntactic errors. we evaluate an existing penn-treebank-trained parser on the ungrammatical treebank to see how it reacts to noise in the form of grammatical errors. we re-train this parser on the training section of the ungrammatical treebank, leading to an significantly improved performance on the ungrammatical test sets. we show how a classifier can be used to prevent performance degradation on the original grammatical data.
segmentation for english-to-arabic statistical machine translation. in this paper, we report on a set of initial results for english-to-arabic statistical machine translation (smt). we show that morphological decomposition of the arabic source is beneficial, especially for smaller-size corpora, and investigate different recombination techniques. we also report on the use of factored translation models for english-to-arabic translation.
decompounding query keywords from compounding languages. splitting compound words has proved to be useful in areas such as machine translation, speech recognition or information retrieval (ir). furthermore, real-time ir systems (such as search engines) need to cope with noisy data, as user queries are sometimes written quickly and submitted without review. in this paper we apply a state-of-the-art procedure for german decompounding to other compounding languages, and we show that it is possible to have a single decompounding model that is applicable across languages.
active sample selection for named entity transliteration. this paper introduces a new method for identifying named-entity (ne) transliterations within bilingual corpora. current state-of-the-art approaches usually require annotated data and relevant linguistic knowledge which may not be available for all languages. we show how to effectively train an accurate transliteration classifier using very little data, obtained automatically. to perform this task, we introduce a new active sampling paradigm for guiding and adapting the sample selection process. we also investigate how to improve the classifier by identifying repeated patterns in the training data. we evaluated our approach using english, russian and hebrew corpora.
arabic morphological tagging, diacritization, and lemmatization using lexeme models and feature ranking. we investigate the tasks of general morphological tagging, diacritization, and lemmatization for arabic. we show that for all tasks we consider, both modeling the lexeme explicitly, and retuning the weights of individual classifiers for the specific task, improve the performance.
in-browser summarisation: generating elaborative summaries biased towards the reading context. we investigate elaborative summarisation, where the aim is to identify supplementary information that expands upon a key fact. we envisage such summaries being useful when browsing certain kinds of (hyper-)linked document sets, such as wikipedia articles or repositories of publications linked by citations. for these collections, an elaborative summary is intended to provide additional information on the linking anchor text. our contribution in this paper focuses on identifying and exploring a real task in which summarisation is situated, realised as an in-browser tool. we also introduce a neighbourhood scoring heuristic as a means of scoring matches to relevant passages of the document. in a preliminary evaluation using this method, our summarisation system scores above our baselines and achieves a recall of 57% annotated gold standard sentences.
efficient processing of underspecified discourse representations. underspecification-based algorithms for processing partially disambiguated discourse structure must cope with extremely high numbers of readings. based on previous work on dominance graphs and weighted tree grammars, we provide the first possibility for computing an underspecified discourse description and a best discourse representation efficiently enough to process even the longest discourses in the rst discourse treebank.
splitsvm: fast, space-efficient, non-heuristic, polynomial kernel computation for nlp applications. we present a fast, space efficient and non-heuristic method for calculating the decision function of polynomial kernel classifiers for nlp applications. we apply the method to the maltparser system, resulting in a java parser that parses over 50 sentences per second on modest hardware without loss of accuracy (a 30 time speedup over existing methods). the method implementation is available as the open-source splitsvm java library.
kernels on linguistic structures for answer extraction. natural language processing (nlp) for information retrieval has always been an interesting and challenging research area. despite the high expectations, most of the results indicate that successfully using nlp is very complex. in this paper, we show how support vector machines along with kernel functions can effectively represent syntax and semantics. our experiments on question/answer classification show that the above models highly improve on bag-of-words on a trec dataset.
machine translation system combination using itg-based alignments. given several systems' automatic translations of the same sentence, we show how to combine them into a confusion network, whose various paths represent composite translations that could be considered in a subsequent rescoring step. we build our confusion networks using the method of rosti et al. (2007), but, instead of forming alignments using the tercom script (snover et al., 2006), we create alignments that minimize invwer (leusch et al., 2003), a form of edit distance that permits properly nested block movements of substrings. oracle experiments with chinese newswire and weblog translations show that our confusion networks contain paths which are significantly better (in terms of bleu and ter) than those in tercom-based confusion networks.
combined one sense disambiguation of abbreviations. a process that attempts to solve abbreviation ambiguity is presented. various context-related features and statistical features have been explored. almost all features are domain independent and language independent. the application domain is jewish law documents written in hebrew. such documents are known to be rich in ambiguous abbreviations. various implementations of the one sense per discourse hypothesis are used, improving the features with new variants. an accuracy of 96.09% has been achieved by svm.
using structural information for identifying similar chinese characters. chinese characters that are similar in their pronunciations or in their internal structures are useful for computer-assisted language learning and for psycholinguistic studies. although it is possible for us to employ image-based methods to identify visually similar characters, the resulting computational costs can be very high. we propose methods for identifying visually similar chinese characters by adopting and extending the basic concepts of a proven chinese input method--cangjie. we present the methods, illustrate how they work, and discuss their weakness in this paper.
dimensions of subjectivity in natural language. current research in automatic subjectivity analysis deals with various kinds of subjective statements involving human attitudes and emotions. while all of them are related to subjectivity, these statements usually touch on multiple dimensions such as non-objectivity, uncertainty, vagueness, non-objective measurability, imprecision, and ambiguity, which are inherently different. this paper discusses the differences and relations of six dimensions of subjectivity. conceptual and linguistic characteristics of each dimension will be demonstrated under different contexts.
evolving new lexical association measures using genetic programming. automatic extraction of collocations from large corpora has been the focus of many research efforts. most approaches concentrate on improving and combining known lexical association measures. in this paper, we describe a genetic programming approach for evolving new association measures, which is not limited to any specific language, corpus, or type of collocation. our preliminary experimental results show that the evolved measures outperform three known association measures.