waashk/dblp huggingface dataset:
experiments with reasoning for temporal relations between events. few attempts have been made to investigate the utility of temporal reasoning within machine learning frameworks for temporal relation classification between events in news articles. this paper presents three settings where temporal reasoning aids machine learned classifiers of temporal relations: (1) expansion of the dataset used for learning; (2) detection of inconsistencies among the automatically identified relations; and (3) selection among multiple temporal relations. feature engineering is another effort in our work to improve classification accuracy.
looking for trouble. this paper presents a method for mining potential troubles or obstacles related to the use of a given object. some example instances of this relation are (medicine, side effect) and (amusement park, height restriction). our acquisition method consists of three steps. first, we use an un-supervised method to collect training samples from web documents. second, a set of expressions generally referring to troubles is acquired by a supervised learning method. finally, the acquired troubles are associated with objects so that each of the resulting pairs consists of an object and a trouble or obstacle in using that object. to show the effectiveness of our method we conducted experiments using a large collection of japanese web documents for acquisition. experimental results show an 85.5% precision for the top 10,000 acquired troubles, and a 74% precision for the top 10% of over 60,000 acquired object-trouble pairs.
anomalies in the wordnet verb hierarchy. the wordnet verb hierarchy is tested, with a view to improving the performance of its applications, revealing topological anomalies and casting doubt on its semantic categories. encoded troponyms frequently misrepresent other kinds of entailment. approaches are proposed for correcting these anomalies including a new top ontology.
tighter integration of rule-based and statistical mt in serial system combination. recent papers have described machine translation (mt) based on an automatic post-editing or serial combination strategy whereby the input language is first translated into the target language by a rule-based mt (rbmt) system, then the target language output is automatically post-edited by a phrase-based statistical machine translation (smt) system. this approach has been shown to improve mt quality over rbmt or smt alone. in this previous work, there was a very loose coupling between the two systems: the smt system only had access to the final 1-best translations from rbmt. furthermore, the previous work involved european language pairs and relatively small training corpora. in this paper, we describe a more tightly integrated serial combination for the chinese-to-english mt task. we will present experimental evaluation results on the 2008 nist constrained data track where a significant gain in terms of both automatic and subjective metrics is achieved through the tighter coupling of the two systems.
computer aided correction and extension of a syntactic wide-coverage lexicon. the effectiveness of parsers based on manually created resources, namely a grammar and a lexicon, rely mostly on the quality of these resources. thus, increasing the parser coverage and precision usually implies improving these two resources. their manual improvement is a time consuming and complex task: identifying which resource is the true culprit for a given mistake is not always obvious, as well as finding the mistake and correcting it. some techniques, like van noord (2004) or sagot and villemonte de la clergerie (2006), bring a convenient way to automatically identify forms having potentially erroneous entries in a lexicon. we have integrated and extended such techniques in a wider process which, thanks to the grammar ability to tell how these forms could be used as part of correct parses, is able to propose lexical corrections for the identified entries. we present in this paper an implementation of this process and discuss the main results we have obtained on a syntactic wide-coverage french lexicon.
non-compositional language model and pattern dictionary development for japanese compound and complex sentences. to realize high quality machine translation, we proposed a non-compositional language model, and developed a sentence pattern dictionary of 226,800 pattern pairs for japanese compound and complex sentences consisting of 2 or 3 clauses. in pattern generation from a parallel corpus, compositional constituents that could be generalized were 74% of independent words, 24% of phrases and only 15% of clauses. this means that in japanese-to-english mt, most of the translation results as shown in the parallel corpus could not be obtained by methods based on compositional semantics. this dictionary achieved a syntactic coverage of 98% and a semantic coverage of 78%. it will substantially improve translation quality.
using discourse commitments to recognize textual entailment. in this paper, we introduce a new framework for recognizing textual entailment (rte) which depends on extraction of the set of publicly-held beliefs -- known as discourse commitments -- that can be ascribed to the author of a text (t) or a hypothesis (h). we show that once a set of commitments have been extracted from a t-h pair, the task of recognizing textual entailment is reduced to the identification of the commitments from a t which support the inference of the h. our system correctly identified entailment relationships in more than 80% of t-h pairs taken from all three of the previous pascal rte challenges, without the need for additional sources of training data.
a local alignment kernel in the context of nlp. this paper discusses local alignment kernels in the context of the relation extraction task. we define a local alignment kernel based on the smith-waterman measure as a sequence similarity metric and proceed with a range of possibilities for computing a similarity between elements of sequences. we propose to use distributional similarity measures on elements and by doing so we are able to incorporate extra information from the unlabeled data into a learning task. our experiments suggest that a la kernel provides promising results on some biomedical corpora largely outperforming a baseline.
syntactic reordering integrated with phrase-based smt. we present a novel approach to word reordering which successfully integrates syntactic structural knowledge with phrase-based smt. this is done by constructing a lattice of alternatives based on automatically learned probabilistic syntactic rules. in decoding, the alternatives are scored based on the output word order, not the order of the input. unlike previous approaches, this makes it possible to successfully integrate syntactic reordering with phrase-based smt. on an english- danish task, we achieve an absolute improvement in translation quality of 1.1 % bleu. manual evaluation supports the claim that the present approach is significantly superior to previous approaches.
discourse level opinion interpretation. this work proposes opinion frames as a representation of discourse-level associations which arise from related opinion topics. we illustrate how opinion frames help gather more information and also assist disambiguation. finally we present the results of our experiments to detect these associations.
investigating statistical techniques for sentence-level event classification. the ability to correctly classify sentences that describe events is an important task for many natural language applications such as question answering (qa) and summarisation. in this paper, we treat event detection as a sentence level text classification problem. we compare the performance of two approaches to this task: a support vector machine (svm) classifier and a language modeling (lm) approach. we also investigate a rule based method that uses hand crafted lists of terms derived from wordnet. these terms are strongly associated with a given event type, and can be used to identify sentences describing instances of that type. we use two datasets in our experiments, and evaluate each technique on six distinct event types. our results indicate that the svm consistently outperform the lm technique for this task. more interestingly, we discover that the manual rule based classification system is a very powerful baseline that outperforms the svm on three of the six event types.
active learning with sampling by uncertainty and density for word sense disambiguation and text classification. this paper addresses two issues of active learning. firstly, to solve a problem of uncertainty sampling that it often fails by selecting outliers, this paper presents a new selective sampling technique, sampling by uncertainty and density (sud), in which a k-nearest-neighbor-based density measure is adopted to determine whether an unlabeled example is an outlier. secondly, a technique of sampling by clustering (sbc) is applied to build a representative initial training data set for active learning. finally, we implement a new algorithm of active learning with sud and sbc techniques. the experimental results from three real-world data sets show that our method outperforms competing methods, particularly at the early stages of active learning.
using web-search results to measure word-group similarity. semantic relatedness between words is important to many nlp tasks, and numerous measures exist which use a variety of resources. thus far, such work is confined to measuring similarity between two words (or two texts), and only a handful utilize the web as a corpus. this paper introduces a distributional similarity measure which uses internet search counts and also extends to calculating the similarity within word-groups. the evaluation results are encouraging: for word-pairs, the correlations with human judgments are comparable with state-of-the-art web-search page-count heuristics. when used to measure similarities within sets of 10 words, the results correlate highly (up to 0.8) with those expected. relatively little comparison has been made between the results of different search-engines. here, we compare experimental results from google, windows live search and yahoo and find noticeable differences.
applying discourse analysis and data mining methods to spoken osce assessments. this paper looks at the transcribed data of patient-doctor consultations in an examination setting. the doctors are internationally qualified and enrolled in a bridging course as preparation for their australian medical council examination. in this study, we attempt to ascertain if there are measurable linguistic features of the consultations, and to investigate whether there is any relevant information about the communicative styles of the qualifying doctors that may predict satisfactory or non-satisfactory examination outcomes. we have taken a discourse analysis approach in this study, where the core unit of analysis is a 'turn'. we approach this problem as a binary classification task and employ data mining methods to see whether the application of which to richly annotated dialogues can produce a system with an adequate predictive capacity.
re-estimation of lexical parameters for treebank pcfgs. we present procedures which pool lexical information estimated from unlabeled data via the inside-outside algorithm, with lexical information from a treebank pcfg. the procedures produce substantial improvements (up to 31.6% error reduction) on the task of determining subcategorization frames of novel verbs, relative to a smoothed penn treebank-trained pcfg. even with relatively small quantities of unlabeled training data, the re-estimated models show promising improvements in labeled bracketing f-scores on wall street journal parsing, and substantial benefit in acquiring the subcategorization preferences of low-frequency verbs.
using hidden markov random fields to combine distributional and pattern-based word clustering. word clustering is a conventional and important nlp task, and the literature has suggested two kinds of approaches to this problem. one is based on the distributional similarity and the other relies on the co-occurrence of two words in lexicosyntactic patterns. although the two methods have been discussed separately, it is promising to combine them since they are complementary with each other. this paper proposes to integrate them using hidden markov random fields and demonstrates its effectiveness through experiments.
topic identification for fine-grained opinion analysis. within the area of general-purpose fine-grained subjectivity analysis, opinion topic identification has, to date, received little attention due to both the difficulty of the task and the lack of appropriately annotated resources. in this paper, we provide an operational definition of opinion topic and present an algorithm for opinion topic identification that, following our new definition, treats the task as a problem in topic coreference resolution. we develop a methodology for the manual annotation of opinion topics and use it to annotate topic information for a portion of an existing general-purpose opinion corpus. in experiments using the corpus, our topic identification approach statistically significantly outperforms several non-trivial baselines according to three evaluation measures.
using syntactic information for improving why-question answering. in this paper, we extend an existing paragraph retrieval approach to why-question answering. the starting-point is a system that retrieves a relevant answer for 73% of the test questions. however, in 41% of these cases, the highest ranked relevant answer is not ranked in the top-10. we aim to improve the ranking by adding a re-ranking module. for re-ranking we consider 31 features pertaining to the syntactic structure of the question and the candidate answer. we find a significant improvement over the baseline for both success@10 and mrr@150. the most important features for re-ranking are the baseline score, the presence of cue words, the question's main verb, and the relation between question focus and document title.
coreference systems based on kernels methods. various types of structural information - e.g., about the type of constructions in which binding constraints apply, or about the structure of names - play a central role in coreference resolution, often in combination with lexical information (as in expletive detection). kernel functions appear to be a promising candidate to capture structure-sensitive similarities and complex feature combinations, but care is required to ensure they are exploited in the best possible fashion. in this paper we propose kernel functions for three subtasks of coreference resolution - binding constraint detection, expletive identification, and aliasing - together with an architecture to integrate them within the standard framework for coreference resolution.
chinese term extraction using minimal resources. this paper presents a new approach for term extraction using minimal resources. a term candidate extraction algorithm is proposed to identify features of the relatively stable and domain independent term delimiters rather than that of the terms. for term verification, a link analysis based method is proposed to calculate the relevance between term candidates and the sentences in the domain specific corpus from which the candidates are extracted. the proposed approach requires no prior domain knowledge, no general corpora, no full segmentation and minimal adaptation for new domains. consequently, the method can be used in any domain corpus and it is especially useful for resource-limited domains. evaluations conducted on two different domains for chinese term extraction show quite significant improvements over existing techniques and also verify the efficiency and relative domain independent nature of the approach. experiments on new term extraction also indicate that the approach is quite effective for identifying new terms in a domain making it useful for domain knowledge update.
homotopy-based semi-supervised hidden markov models for sequence labeling. this paper explores the use of the homotopy method for training a semi-supervised hidden markov model (hmm) used for sequence labeling. we provide a novel polynomial-time algorithm to trace the local maximum of the likelihood function for hmms from full weight on the labeled data to full weight on the unlabeled data. we present an experimental analysis of different techniques for choosing the best balance between labeled and unlabeled data based on the characteristics observed along this path. furthermore, experimental results on the field segmentation task in information extraction show that the homotopy-based method significantly outperforms em-based semi-supervised learning, and provides a more accurate alternative to the use of held-out data to pick the best balance for combining labeled and unlabeled data.
automatic seed word selection for unsupervised sentiment classification of chinese text. we describe and evaluate a new method of automatic seed word selection for un-supervised sentiment classification of product reviews in chinese. the whole method is unsupervised and does not require any annotated training data; it only requires information about commonly occurring negations and adverbials. unsupervised techniques are promising for this task since they avoid problems of domain-dependency typically associated with supervised methods. the results obtained are close to those of supervised classifiers and sometimes better, up to an f1 of 92%.
stopping criteria for active learning of named entity recognition. active learning is a proven method for reducing the cost of creating the training sets that are necessary for statistical nlp. however, there has been little work on stopping criteria for active learning. an operational stopping criterion is necessary to be able to use active learning in nlp applications. we investigate three different stopping criteria for active learning of named entity recognition (ner) and show that one of them, gradient-based stopping, (i) reliably stops active learning, (ii) achieves nearoptimal ner performance, (iii) and needs only about 20% as much training data as exhaustive labeling.
modeling semantic containment and exclusion in natural language inference. we propose an approach to natural language inference based on a model of natural logic, which identifies valid inferences by their lexical and syntactic features, without full semantic interpretation. we greatly extend past work in natural logic, which has focused solely on semantic containment and monotonicity, to incorporate both semantic exclusion and implicativity. our system decomposes an inference problem into a sequence of atomic edits linking premise to hypothesis; predicts a lexical entailment relation for each edit using a statistical classifier; propagates these relations upward through a syntax tree according to semantic properties of intermediate nodes; and composes the resulting entailment relations across the edit sequence. we evaluate our system on the fracas test suite, and achieve a 27% reduction in error from previous work. we also show that hybridizing an existing rte system with our natural logic system yields significant gains on the rte3 test suite.
evaluating unsupervised part-of-speech tagging for grammar induction. this paper explores the relationship between various measures of unsupervised part-of-speech tag induction and the performance of both supervised and unsupervised parsing models trained on induced tags. we find that no standard tagging metrics correlate well with unsupervised parsing performance, and several metrics grounded in information theory have no strong relationship with even supervised parsing performance.
multi-criteria-based strategy to stop active learning for data annotation. in this paper, we address the issue of deciding when to stop active learning for building a labeled training corpus. firstly, this paper presents a new stopping criterion, classification-change, which considers the potential ability of each unlabeled example on changing decision boundaries. secondly, a multi-criteria-based combination strategy is proposed to solve the problem of predefining an appropriate threshold for each confidence-based stopping criterion, such as max-confidence, min-error, and overall-uncertainty. finally, we examine the effectiveness of these stopping criteria on uncertainty sampling and heterogeneous uncertainty sampling for active learning. experimental results show that these stopping criteria work well on evaluation data sets, and the combination strategies outperform individual criteria.
a hybrid generative/discriminative framework to train a semantic parser from an un-annotated corpus. we propose a hybrid generative/discriminative framework for semantic parsing which combines the hidden vector state (hvs) model and the hidden markov support vector machines (hm-svms). the hvs model is an extension of the basic discrete markov model in which context is encoded as a stack-oriented state vector. the hm-svms combine the advantages of the hidden markov models and the support vector machines. by employing a modified k-means clustering method, a small set of most representative sentences can be automatically selected from an un-annotated corpus. these sentences together with their abstract annotations are used to train an hvs model which could be subsequently applied on the whole corpus to generate semantic parsing results. the most confident semantic parsing results are selected to generate a fully-annotated corpus which is used to train the hm-svms. the proposed framework has been tested on the darpa communicator data. experimental results show that an improvement over the baseline hvs parser has been observed using the hybrid framework. when compared with the hm-svms trained from the fully-annotated corpus, the hybrid framework gave a comparable performance with only a small set of lightly annotated sentences.
binning optimization based on ssta for transparently-latched circuits. with increasing process variation, binning has become an important technique to improve the values of fabricated chips, especially in high performance microprocessors where transparent latches are widely used. in this paper, we formulate and solve the binning optimization problem that decides the bin boundaries and their testing order to maximize the benefit (considering the test cost) for a transparently-latched circuit. the problem is decomposed into three sub-problems which are solved sequentially. first, to compute the clock period distribution of the transparently-latched circuit, a sample-based ssta approach is developed which is based on the generalized stochastic collocation method (gscm) with sparse grid technique. the minimal clock period on each sample point is found by solving a minimal cycle ratio problem in the constraint graph. second, a greedy algorithm is proposed to maximize the sales profit by iteratively assigning each boundary to its optimal position. then, an optimal algorithm of o(n log n) runtime is used to generate the optimal testing order of bin boundaries to minimize the test cost, based on alphabetic tree. experiments on all the iscas'89 sequential benchmarks with 65-nm technology show 6.69% profit improvement and 14.00% cost reduction in average. the results also demonstrate that the proposed ssta method achieves an error of 0.70% and speedup of 110x in average compared with the monte carlo simulation.