waashk/dblp huggingface dataset:
a study on richer syntactic dependencies for structured language modeling. we study the impact of richer syntactic dependencies on the performance of the structured language model (slm) along three dimensions: parsing accuracy (lp/lr), perplexity (ppl) and word-error-rate (wer, n-best re-scoring). we show that our models achieve an improvement in lp/lr, ppl and/or wer over the reported baseline results using the slm on the upenn treebank and wall street journal (wsj) corpora, respectively. analysis of parsing performance shows correlation between the quality of the parser (as measured by precision/recall) and the language model performance (ppl and wer). a remarkable fact is that the enriched slm outperforms the baseline 3-gram model in terms of wer by 10% when used in isolation as a second pass (n-best re-scoring) language model.
aligning features with sense distinction dimensions. in this paper we present word sense disambiguation (wsd) experiments on ten highly polysemous verbs in chinese, where significant performance improvements are achieved using rich linguistic features. our system performs significantly better, and in some cases substantially better, than the baseline on all ten verbs. our results also demonstrate that features extracted from the output of an automatic chinese semantic role labeling system in general benefited the wsd system, even though the amount of improvement was not consistent across the verbs. for a few verbs, semantic role information actually hurt wsd performance. the inconsistency of feature performance is a general characteristic of the wsd task, as has been observed by others. we argue that this result can be explained by the fact that word senses are partitioned along different dimensions for different verbs and the features therefore need to be tailored to particular verbs in order to achieve adequate accuracy on verb sense disambiguation.
a unified statistical model for the identification of english basenp. this paper presents a novel statistical model for automatic identification of english basenp. it uses two steps: the n-best part-of-speech (pos) tagging and basenp identification given the n-best pos-sequences. unlike the other approaches where the two steps are separated, we integrate them into a unified statistical framework. our model also integrates lexical information. finally, viterbi algorithm is applied to make global search in the entire sentence, allowing us to obtain linear complexity for the entire process. compared with other methods using the same testing set, our approach achieves 92.3% in precision and 93.2% in recall. the result is comparable with or better than the previously reported results.
clustering hungarian verbs on the basis of complementation patterns. our paper reports an attempt to apply an unsupervised clustering algorithm to a hungarian treebank in order to obtain semantic verb classes. starting from the hypothesis that semantic metapredicates underlie verbs' syntactic realization, we investigate how one can obtain semantically motivated verb classes by automatic means. the 150 most frequent hungarian verbs were clustered on the basis of their complementation patterns, yielding a set of basic classes and hints about the features that determine verbal subcategorization. the resulting classes serve as a basis for the subsequent analysis of their alternation behavior.
automatic acquisition of adjectival subcategorization from corpora. this paper describes a novel system for acquiring adjectival subcategorization frames (scfs) and associated frequency information from english corpus data. the system incorporates a decision-tree classifier for 30 scf types which tests for the presence of grammatical relations (grs) in the output of a robust statistical parser. it uses a powerful pattern-matching language to classify grs into frames hierarchically in a way that mirrors inheritance-based lexica. the experiments show that the system is able to detect scf types with 70% precision and 66% recall rate. a new tool for linguistic annotation of scfs in corpus data is also introduced which can considerably alleviate the process of obtaining training and test data for subcategorization acquisition.
wordnet-based semantic relatedness measures in automatic speech recognition for meetings. this paper presents the application of wordnet-based semantic relatedness measures to automatic speech recognition (asr) in multi-party meetings. different word-utterance context relatedness measures and utterance-coherence measures are defined and applied to the rescoring of n-best lists. no significant improvements in terms of word-error-rate (wer) are achieved compared to a large word-based n-gram baseline model. we discuss our results and the relation to other work that achieved an improvement with such models for simpler tasks.
a syntax-based statistical translation model. we present a syntax-based statistical translation model. our model transforms a source-language parse tree into a target-language string by applying stochastic operations at each node. these operations capture linguistic differences such as word order and case marking. model parameters are estimated in polynomial time using an em algorithm. the model produces word alignments that are better than those produced by ibm model 5.
multimedia blog creation system using dialogue with intelligent robot. a multimedia blog creation system is described that uses japanese dialogue with an intelligent robot. although multimedia blogs are increasing in popularity, creating blogs is not easy for users who lack high-level information literacy skills. even skilled users have to waste time creating and assigning text descriptions to their blogs and searching related multimedia such as images, music, and illustrations. to enable effortless and enjoyable creation of multimedia blogs, we developed the system on a prototype robot called papero. video messages are recorded and converted into text descriptions by papero using continuous speech recognition. papero then searches for suitable multimedia contents on the internet and databases, and then, based on the search results, chooses appropriate sympathetic comments by using natural language text retrieval. the retrieved contents, papero's comments, and the video recording on the user's blog is automatically uploaded and edited. the system was evaluated by 10 users for creating travel blogs and proved to be helpful for both inexperienced and experienced users. the system enabled easy multimedia-rich blog creation and even provided users the pleasure of chatting with papero.
a decoder for syntax-based statistical mt. this paper describes a decoding algorithm for a syntax-based translation model (yamada and knight, 2001). the model has been extended to incorporate phrasal translations as presented here. in contrast to a conventional word-to-word statistical model, a decoder for the syntax-based model builds up an english parse tree given a sentence in a foreign language. as the model size becomes huge in a practical setting, and the decoder considers multiple syntactic structures for each word alignment, several pruning techniques are necessary. we tested our decoder in a chinese-to-english translation system, and obtained better results than ibm model 4. we also discuss issues concerning the relation between this decoder and a language model.
system demonstration of on-demand information extraction. in this paper, we will describe odie, the on-demand information extraction system. given a user's query, the system will produce tables of the salient information about the topic in structured form. it produces the tables in less than one minute without any knowledge engineering by hand, i.e. pattern creation or paraphrase knowledge creation, which was the largest obstacle in traditional ie. this demonstration is based on the idea and technologies reported in (sekine 06). a substantial speed-up over the previous system (which required about 15 minutes to analyze one year of newspaper) was achieved through a new approach to handling pattern candidates; now less than one minute is required when using 11 years of newspaper corpus. in addition, functionality was added to facilitate investigation of the extracted information.
combination of an automatic and an interactive disambiguation method. in natural language processing, many methods have been proposed to solve the ambiguity problems. in this paper, we propose a technique to combine a method of interactive disambiguation and automatic one for ambiguous words. the characteristic of our method is that the accuracy of the interactive disambiguation is considered. the method solves the two following problems when combining those disambiguation methods: (1) when should the interactive disambiguation be executed? (2) which ambiguous word should be disambiguated when more than one ambiguous words exist in a sentence? our method defines the condition of executing the interaction with users and the order of disambiguation based on the strategy where the accuracy of the result is maximized, considering the accuracy of the interactive disambiguation and automatic one. using this method, user interaction can be controlled while holding the accuracy of results.
multi-class composite n-gram language model for spoken language processing using multiple word clusters. in this paper, a new language model, the multi-class composite n-gram, is proposed to avoid a data sparseness problem for spoken language in that it is difficult to collect training data. the multi-class composite n-gram maintains an accurate word prediction capability and reliability for sparse data with a compact model size based on multiple word clusters, called multi-classes. in the multi-class, the statistical connectivity at each position of the n-grams is regarded as word attributes, and one word cluster each is created to represent the positional attributes. furthermore, by introducing higher order word n-grams through the grouping of frequent word successions, multi-class n-grams are extended to multi-class composite n-grams. in experiments, the multi-class composite n-grams result in 9.5% lower perplexity and a 16% lower word error rate in speech recognition with a 40% smaller parameter size than conventional word 3-grams.
kinds of features for chinese opinionated information retrieval. this paper presents the results of experiments in which we tested different kinds of features for retrieval of chinese opinionated texts. we assume that the task of retrieval of opinionated texts (oir) can be regarded as a subtask of general ir, but with some distinct features. the experiments showed that the best results were obtained from the combination of character-based processing, dictionary look up (maximum matching) and a negation check.
automatic part-of-speech tagging for bengali: an approach for morphologically rich languages in a poor resource scenario. this paper describes our work on building part-of-speech (pos) tagger for bengali. we have use hidden markov model (hmm) and maximum entropy (me) based stochastic taggers. bengali is a morphologically rich language and our taggers make use of morphological and contextual information of the words. since only a small labeled training set is available (45,000 words), simple stochastic approach does not yield very good results. in this work, we have studied the effect of using a morphological analyzer to improve the performance of the tagger. we find that the use of morphology helps improve the accuracy of the tagger especially when less amount of tagged corpora are available.
a selectionist theory of language acquisition. this paper argues that developmental patterns in child language be taken seriously in computational models of language acquisition, and proposes a formal theory that meets this criterion. we first present developmental facts that are problematic for statistical learning approaches which assume no prior knowledge of grammar, and for traditional learnability models which assume the learner moves from one ug-defined grammar to another. in contrast, we view language acquisition as a population of grammars associated with "weights", that compete in a darwinian selectionist process. selection is made possible by the <i>variational</i> properties of individual grammars; specifically, their differential compatibility with the primary linguistic data in the environment. in addition to a convergence proof, we present empirical evidence in child language development, that a learner is best modeled as multiple grammars in co-existence and competition.
on the formalization of invariant mappings for metaphor interpretation. in this paper we provide a formalization of a set of default rules that we claim are required for the transfer of information such as causation, event rate and duration in the interpretation of metaphor. such rules are domain-independent and are identified as invariant adjuncts to any conceptual metaphor. we also show a way of embedding the invariant mappings in a semantic framework.
word sense disambiguation using lexical cohesion in the context. this paper designs a novel lexical hub to disambiguate word sense, using both syntagmatic and paradigmatic relations of words. it only employs the semantic network of wordnet to calculate word similarity, and the edinburgh association thesaurus (eat) to transform contextual space for computing syntagmatic and other domain relations with the target word. without any back-off policy the result on the english lexical sample of senseval-21 shows that lexical cohesion based on edge-counting techniques is a good way of unsupervisedly disambiguating senses.
improving pronoun resolution using statistics-based semantic compatibility information. in this paper we focus on how to improve pronoun resolution using the statistics-based semantic compatibility information. we investigate two unexplored issues that influence the effectiveness of such information: statistics source and learning framework. specifically, we for the first time propose to utilize the web and the twin-candidate model, in addition to the previous combination of the corpus and the single-candidate model, to compute and apply the semantic information. our study shows that the semantic compatibility obtained from the web can be effectively incorporated in the twin-candidate learning model and significantly improve the resolution of neutral pronouns.
kernel-based pronoun resolution with structured syntactic knowledge. syntactic knowledge is important for pronoun resolution. traditionally, the syntactic information for pronoun resolution is represented in terms of features that have to be selected and defined heuristically. in the paper, we propose a kernel-based method that can automatically mine the syntactic information from the parse trees for pronoun resolution. specifically, we utilize the parse trees directly as a structured feature and apply kernel functions to this feature, as well as other normal features, to learn the resolution classifier. in this way, our approach avoids the efforts of decoding the parse trees into the set of flat syntactic features. the experimental results show that our approach can bring significant performance improvement and is reliably effective for the pronoun resolution task.
improving pronoun resolution by incorporating coreferential information of candidates. coreferential information of a candidate, such as the properties of its antecedents, is important for pronoun resolution because it reflects the salience of the candidate in the local discourse. such information, however, is usually ignored in previous learning-based systems. in this paper we present a trainable model which incorporates coreferential information of candidates into pronoun resolution. preliminary experiments show that our model will boost the resolution performance given the right antecedents of the candidates. we further discuss how to apply our model in real resolution where the antecedents of the candidate are found by a separate noun phrase resolution module. the experimental results show that our model still achieves better performance than the baseline.
coreference resolution using competition learning approach. in this paper we propose a competition learning approach to coreference resolution. traditionally, supervised machine learning approaches adopt the single-candidate model. nevertheless the preference relationship between the antecedent candidates cannot be determined accurately in this model. by contrast, our approach adopts a twin-candidate learning model. such a model can present the competition criterion for antecedent candidates reliably, and ensure that the most preferred candidate is selected. furthermore, our approach applies a candidate filter to reduce the computational cost and data noises during training and resolution. the experimental results on muc-6 and muc-7 data set show that our approach can outperform those based on the single-candidate model.
limitations of current grammar induction algorithms. i review a number of grammar induction algorithms (abl, emile, adios), and test them on the eindhoven corpus, resulting in disappointing results, compared to the usually tested corpora (atis, ovis). also, i show that using neither pos-tags induced from biemann's unsupervised pos-tagging algorithm nor hand-corrected pos-tags as input improves this situation. last, i argue for the development of entirely incremental grammar induction algorithms instead of the approaches of the systems discussed before.
counter-training in discovery of semantic patterns. this paper presents a method for unsupervised discovery of semantic patterns. semantic patterns are useful for a variety of text understanding tasks, in particular for locating events in text for information extraction. the method builds upon previously described approaches to iterative unsupervised pattern acquisition. one common characteristic of prior approaches is that the output of the algorithm is a continuous stream of patterns, with gradually degrading precision.our method differs from the previous pattern acquisition algorithms in that it introduces competition among several scenarios simultaneously. this provides natural stopping criteria for the unsupervised learners, while maintaining good precision levels at termination. we discuss the results of experiments with several scenarios, and examine different aspects of the new procedure.
chinese named entity and relation identification system. in this interactive presentation, a chinese named entity and relation identification system is demonstrated. the domain-specific system has a three-stage pipeline architecture which includes word segmentation and part-of-speech (pos) tagging, named entity recognition, and named entity relation identitfication. the experimental results have shown that the average f-measure for word segmentation and pos tagging after correcting errors achieves 92.86 and 90.01 separately. moreover, the overall average f-measure for 6 kinds of name entities and 14 kinds of named entity relations is 83.08% and 70.46% respectively.
minimum bayes risk decoding for bleu. we present a minimum bayes risk (mbr) decoder for statistical machine translation. the approach aims to minimize the expected loss of translation errors with regard to the bleu score. we show that mbr decoding on n-best lists leads to an improvement of translation quality. we report the performance of the mbr decoder on four different tasks: the tc-star epps spanish-english task 2006, the nist chinese-english task 2005 and the gale arabic-english and chinese-english task 2006. the absolute improvement of the bleu score is between 0.2% for the tc-star task and 1.1% for the gale chinese-english task.
decision lists for lexical ambiguity resolution: application to accent restoration in spanish and french. this paper presents a statistical decision procedure for lexical ambiguity resolution. the algorithm exploits both local syntactic patterns and more distant collocational evidence, generating an efficient, effective, and highly perspicuous recipe for resolving a given ambiguity. by identifying and utilizing only the single best disambiguating evidence in a target context, the algorithm avoids the problematic complex modeling of statistical dependencies. although directly applicable to a wide class of ambiguities, the algorithm is described and evaluated in a realistic case study, the problem of restoring missing accents in spanish and french text. current accuracy exceeds 99% on the full task, and typically is over 90% for even the most difficult ambiguities.
semantic enrichment of journal articles using chemical named entity recognition. we describe the semantic enrichment of journal articles with chemical structures and biomedical ontology terms using oscar, a program for chemical named entity recognition (ner). we describe how oscar works and how it can been adapted for general ner. we discuss its implementation in a real publishing workflow and possible applications for enriched articles.
unsupervised word sense disambiguation rivaling supervised methods. this paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated english text, rivals the performance of supervised techniques that require time-consuming hand annotations. the algorithm is based on two powerful constraints---that words tend to have one sense per discourse and one sense per collocation---exploited in an iterative bootstrapping procedure. tested accuracy exceeds 96%.
pivot language approach for phrase-based statistical machine translation. this paper proposes a novel method for phrase-based statistical machine translation based on the use of a pivot language. to translate between languages l s and l t with limited bilingual resources, we bring in a third language, l p , called the pivot language. for the language pairs l s ¿ l p and l p ¿ l t , there exist large bilingual corpora. using only l s ¿ l p and l p ¿ l t bilingual corpora, we can build a translation model for l s ¿ l t . the advantage of this method lies in the fact that we can perform translation between l s and l t even if there is no bilingual corpus available for this language pair. using bleu as a metric, our pivot language approach significantly outperforms the standard model trained on a small bilingual corpus. moreover, with a small l s ¿ l t bilingual corpus available, our method can further improve translation quality by using the additional l s ¿ l p and l p ¿ l t bilingual corpora.
minimally supervised morphological analysis by multimodal alignment. this paper presents a corpus-based algorithm capable of inducing inflectional morphological analyses of both regular and highly irregular forms (such as brought&rarr;bring) from distributional patterns in large monolingual text with no direct supervision. the algorithm combines four original alignment models based on relative corpus frequency, contextual similarity, weighted string similarity and incrementally retrained inflectional transduction probabilities. starting with no paired <inflection,root> examples for training and no prior seeding of legal morphological transformations, accuracy of the induced analyses of 3888 past-tense test cases in english exceeds 99.2% for the set, with currently over 80% accuracy on the most highly irregular forms and 99.7% accuracy on forms exhibiting non-concatenative suffixation.
extracting word sets with non-taxonomical relation. at least two kinds of relations exist among related words: taxonomical relations and thematic relations. both relations identify related words useful to language understanding and generation, information retrieval, and so on. however, although words with taxonomical relations are easy to identify from linguistic resources such as dictionaries and thesauri, words with thematic relations are difficult to identify because they are rarely maintained in linguistic resources. in this paper, we sought to extract thematically (non-taxonomically) related word sets among words in documents by employing case-marking particles derived from syntactic analysis. we then verified the usefulness of word sets with non-taxonomical relation that seems to be a thematic relation for information retrieval.