waashk/dblp huggingface dataset:
estimating semantic distance using soft semantic constraints in knowledge-source - corpus hybrid models. strictly corpus-based measures of semantic distance conflate co-occurrence information pertaining to the many possible senses of target words. we propose a corpus-thesaurus hybrid method that uses soft constraints to generate word-senseaware distributional profiles (dps) from coarser "concept dps" (derived from a roget-like thesaurus) and sense-unaware traditional word dps (derived from raw text). although it uses a knowledge source, the method is not vocabulary-limited: if the target word is not in the thesaurus, the method falls back gracefully on the word's co-occurrence information. this allows the method to access valuable information encoded in a lexical resource, such as a thesaurus, while still being able to effectively handle domain-specific terms and named entities. experiments on word-pair ranking by semantic distance show the new hybrid method to be superior to others.
bidirectional phrase-based statistical machine translation. this paper investigates the effect of direction in phrase-based statistial machine translation decoding. we compare a typical phrase-based machine translation decoder using a left-to-right decoding strategy to a right-to-left decoder. we also investigate the effectiveness of a bidirectional decoding strategy that integrates both mono-directional approaches, with the aim of reducing the effects due to language specificity. our experimental evaluation was extensive, based on 272 different language pairs, and gave the surprising result that for most of the language pairs, it was better decode from right-to-left than from left-to-right. as expected the relative performance of left-to-right and right-to-left strategies proved to be highly language dependent. the bidirectional approach outperformed the both the left-to-right strategy and the right-to-left strategy, showing consistent improvements that appeared to be unrelated to the specific languages used for translation. bidirectional decoding gave rise to an improvement in performance over a left-to-right decoding strategy in terms of the bleu score in 99% of our experiments.
domain adaptive bootstrapping for named entity recognition. bootstrapping is the process of improving the performance of a trained classifier by iteratively adding data that is labeled by the classifier itself to the training set, and retraining the classifier. it is often used in situations where labeled training data is scarce but unlabeled data is abundant. in this paper, we consider the problem of domain adaptation: the situation where training data may not be scarce, but belongs to a different domain from the target application domain. as the distribution of unlabeled data is different from the training data, standard bootstrapping often has difficulty selecting informative data to add to the training set. we propose an effective domain adaptive bootstrapping algorithm that selects unlabeled target domain data that are informative about the target domain and easy to automatically label correctly. we call these instances bridges, as they are used to bridge the source domain to the target domain. we show that the method outperforms supervised, transductive and bootstrapping algorithms on the named entity recognition task.
wikipedia as frame information repository. in this paper, we address the issue of automatic extending lexical resources by exploiting existing knowledge repositories. in particular, we deal with the new task of linking framenet and wikipedia using a word sense disambiguation system that, for a given pair frame -- lexical unit (f, l), finds the wikipage that best expresses the the meaning of l. the mapping can be exploited to straightforwardly acquire new example sentences and new lexical units, both for english and for all languages available in wikipedia. in this way, it is possible to easily acquire good-quality data as a starting point for the creation of framenet in new languages. the evaluation reported both for the monolingual and the multilingual expansion of framenet shows that the approach is promising.
learning term-weighting functions for similarity measures. measuring the similarity between two texts is a fundamental problem in many nlp and ir applications. among the existing approaches, the cosine measure of the term vectors representing the original texts has been widely used, where the score of each term is often determined by a tfidf formula. despite its simplicity, the quality of such cosine similarity measure is usually domain dependent and decided by the choice of the term-weighting function. in this paper, we propose a novel framework that learns the term-weighting function. given the labeled pairs of texts as training data, the learning procedure tunes the model parameters by minimizing the specified loss function of the similarity score. compared to traditional tfidf term-weighting schemes, our approach shows a significant improvement on tasks such as judging the quality of query suggestions and filtering irrelevant ads for online advertising.
finding short definitions of terms on web pages. we present a system that finds short definitions of terms on web pages. it employs a maximum entropy classifier, but it is trained on automatically generated examples; hence, it is in effect unsupervised. we use rouge-w to generate training examples from encyclopedias and web snippets, a method that outperforms an alternative centroid-based one. after training, our system can be used to find definitions of terms that are not covered by encyclopedias. the system outperforms a comparable publicly available system, as well as a previously published form of our system.
deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing. a number of recent publications have made use of the incremental output of stochastic parsers to derive measures of high utility for psycholinguistic modeling, following the work of hale (2001; 2003; 2006). in this paper, we present novel methods for calculating separate lexical and syntactic surprisal measures from a single incremental parser using a lexicalized pcfg. we also present an approximation to entropy measures that would otherwise be intractable to calculate for a grammar of that size. empirical results demonstrate the utility of our methods in predicting human reading times.
efficient kernels for sentence pair classification. in this paper, we propose a novel class of graphs, the tripartite directed acyclic graphs (tdags), to model first-order rule feature spaces for sentence pair classification. we introduce a novel algorithm for computing the similarity in first-order rewrite rule feature spaces. our algorithm is extremely efficient and, as it computes the similarity of instances that can be represented in explicit feature spaces, it is a valid kernel function.
acquiring translation equivalences of multiword expressions by normalized correlation frequencies. in this paper, we present an algorithm for extracting translations of any given multiword expression from parallel corpora. given a multiword expression to be translated, the method involves extracting a short list of target candidate words from parallel corpora based on scores of normalized frequency, generating possible translations and filtering out common subsequences, and selecting the top-n possible translations using the dice coefficient. experiments show that our approach outperforms the word alignment-based and other naive association-based methods. we also demonstrate that adopting the extracted translations can significantly improve the performance of the moses machine translation system.
multilingual spectral clustering using document similarity propagation. we present a novel approach for multilingual document clustering using only comparable corpora to achieve cross-lingual semantic interoperability. the method models document collections as weighted graph, and supervisory information is given as sets of must-linked constraints for documents in different languages. recursive k-nearest neighbor similarity propagation is used to exploit the prior knowledge and merge two language spaces. spectral method is applied to find the best cuts of the graph. experimental results show that using limited supervisory information, our method achieves promising clustering results. furthermore, since the method does not need any language dependent information in the process, our algorithm can be applied to languages in various alphabetical systems.
person cross document coreference with name perplexity estimates. the person cross document coreference systems depend on the context for making decisions on the possible coreferences between person name mentions. the amount of context required is a parameter that varies from corpora to corpora, which makes it difficult for usual disambiguation methods. in this paper we show that the amount of context required can be dynamically controlled on the basis of the prior probabilities of coreference and we present a new statistical model for the computation of these probabilities. the experiment we carried on a news corpus proves that the prior probabilities of coreference are an important factor for maintaining a good balance between precision and recall for cross document coreference systems.
stream-based randomised language models for smt. randomised techniques allow very big language models to be represented succinctly. however, being batch-based they are unsuitable for modelling an unbounded stream of language whilst maintaining a constant error rate. we present a novel randomised language model which uses an online perfect hash function to efficiently deal with unbounded text streams. translation experiments over a text stream show that our online randomised model matches the performance of batch-based lms without incurring the computational overhead associated with full retraining. this opens up the possibility of randomised language models which continuously adapt to the massive volumes of texts published on the web each day.
large-scale verb entailment acquisition from the web. textual entailment recognition plays a fundamental role in tasks that require indepth natural language understanding. in order to use entailment recognition technologies for real-world applications, a large-scale entailment knowledge base is indispensable. this paper proposes a conditional probability based directional similarity measure to acquire verb entailment pairs on a large scale. we targeted 52,562 verb types that were derived from 108 japanese web documents, without regard for whether they were used in daily life or only in specific fields. in an evaluation of the top 20,000 verb entailment pairs acquired by previous methods and ours, we found that our similarity measure outperformed the previous ones. our method also worked well for the top 100,000 results.
discovery of term variation in japanese web search queries. in this paper we address the problem of identifying a broad range of term variations in japanese web search queries, where these variations pose a particularly thorny problem due to the multiple character types employed in its writing system. our method extends the techniques proposed for english spelling correction of web queries to handle a wider range of term variants including spelling mistakes, valid alternative spellings using multiple character types, transliterations and abbreviations. the core of our method is a statistical model built on the mart algorithm (friedman, 2001). we show that both string and semantic similarity features contribute to identifying term variation in web search queries; specifically, the semantic similarity features used in our system are learned by mining user session and click-through logs, and are useful not only as model features but also in generating term variation candidates efficiently. the proposed method achieves 70% precision on the term variation identification task with the recall slightly higher than 60%, reducing the error rate of a na&iuml;ve baseline by 38%.
combining collocations, lexical and encyclopedic knowledge for metonymy resolution. this paper presents a supervised method for resolving metonymies. we enhance a commonly used feature set with features extracted based on collocation information from corpora, generalized using lexical and encyclopedic knowledge to determine the preferred sense of the potentially metonymic word using methods from unsupervised word sense disambiguation. the methodology developed addresses one issue related to metonymy resolution - the influence of local context. the method developed is applied to the metonymy resolution task from semeval 2007. the results obtained, higher for the countries subtask, on a par for the companies subtask - compared to participating systems - confirm that lexical, encyclopedic and collocation information can be successfully combined for metonymy resolution.
consensus training for consensus decoding in machine translation. we propose a novel objective function for discriminatively tuning log-linear machine translation models. our objective explicitly optimizes the bleu score of expected n-gram counts, the same quantities that arise in forest-based consensus and minimum bayes risk decoding methods. our continuous objective can be optimized using simple gradient ascent. however, computing critical quantities in the gradient necessitates a novel dynamic program, which we also present here. assuming bleu as an evaluation measure, our objective function has two principle advantages over standard max bleu tuning. first, it specifically optimizes model weights for downstream consensus decoding procedures. an unexpected second benefit is that it reduces overfitting, which can improve test set bleu scores when using standard viterbi decoding.
it's not you, it's me: detecting flirting and its misperception in speed-dates. automatically detecting human social intentions from spoken conversation is an important task for dialogue understanding. since the social intentions of the speaker may differ from what is perceived by the hearer, systems that analyze human conversations need to be able to extract both the perceived and the intended social meaning. we investigate this difference between intention and perception by using a spoken corpus of speed-dates in which both the speaker and the listener rated the speaker on flirtatiousness. our flirtation-detection system uses prosodic, dialogue, and lexical features to detect a speaker's intent to flirt with up to 71.5% accuracy, significantly outperforming the baseline, but also outperforming the human inter-locuters. our system addresses lexical feature sparsity given the small amount of training data by using an autoencoder network to map sparse lexical feature vectors into 30 compressed features. our analysis shows that humans are very poor perceivers of intended flirtatiousness, instead often projecting their own intended behavior onto their interlocutors.
can chinese phonemes improve machine transliteration?: a comparative study of english-to-chinese transliteration models. inspired by the success of english grapheme-to-phoneme research in speech synthesis, many researchers have proposed phoneme-based english-to-chinese transliteration models. however, such approaches have severely suffered from the errors in chinese phoneme-to-grapheme conversion. to address this issue, we propose a new english-to-chinese transliteration model and make systematic comparisons with the conventional models. our proposed model relies on the joint use of chinese phonemes and their corresponding english graphemes and phonemes. experiments showed that chinese phonemes in our proposed model can contribute to the performance improvement in english-to-chinese transliteration.
polynomial to linear: efficient classification with conjunctive features. this paper proposes a method that speeds up a classifier trained with many conjunctive features: combinations of (primitive) features. the key idea is to precompute as partial results the weights of primitive feature vectors that appear frequently in the target nlp task. a trie compactly stores the primitive feature vectors with their weights, and it enables the classifier to find for a given feature vector its longest prefix feature vector whose weight has already been computed. experimental results for a japanese dependency parsing task show that our method speeded up the svm and llm classifiers of the parsers, which achieved accuracy of 90.84/90.71%, by a factor of 10.7/11.6.
character-level analysis of semi-structured documents for set expansion. set expansion refers to expanding a partial set of "seed" objects into a more complete set. one system that does set expansion is seal (set expander for any language), which expands entities automatically by utilizing resources from the web in a language-independent fashion. in this paper, we illustrated in detail the construction of character-level wrappers for set expansion implemented in seal. we also evaluated several kinds of wrappers for set expansion and showed that character-based wrappers perform better than html-based wrappers. in addition, we demonstrated a technique that extends seal to learn binary relational concepts (e.g., "x is the mayor of the city y") from only two seeds. we also show that the extended seal has good performance on our evaluation datasets, which includes english and chinese, thus demonstrating language-independence.
improved word alignment with statistics and linguistic heuristics. we present a method to align words in a bitext that combines elements of a traditional statistical approach with linguistic knowledge. we demonstrate this approach for arabic-english, using an alignment lexicon produced by a statistical word aligner, as well as linguistic resources ranging from an english parser to heuristic alignment rules for function words. these linguistic heuristics have been generalized from a development corpus of 100 parallel sentences. our aligner, ualign, outperforms both the commonly used giza++ aligner and the state-of-the-art leaf aligner on f-measure and produces superior scores in end-to-end statistical machine translation, +1.3 bleu points over giza++, and +0.7 over leaf.
word buffering models for improved speech repair parsing. this paper describes a time-series model for parsing transcribed speech containing disfluencies. this model differs from previous parsers in its explicit modeling of a buffer of recent words, which allows it to recognize repairs more easily due to the frequent overlap in words between errors and their repairs. the parser implementing this model is evaluated on the standard switchboard transcribed speech parsing task for overall parsing accuracy and edited word detection.
a compact forest for scalable inference over entailment and paraphrase rules. a large body of recent research has been investigating the acquisition and application of applied inference knowledge. such knowledge may be typically captured as entailment rules, applied over syntactic representations. efficient inference with such knowledge then becomes a fundamental problem. starting out from a formalism for entailment-rule application we present a novel packed data-structure and a corresponding algorithm for its scalable implementation. we proved the validity of the new algorithm and established its efficiency analytically and empirically.
improved statistical machine translation for resource-poor languages using related resource-rich languages. we propose a novel language-independent approach for improving statistical machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. more precisely, we improve the translation from a resource-poor source language x1 into a resource-rich language y given a bi-text containing a limited number of parallel sentences for x1-y and a larger bi-text for x2-y for some resource-rich language x2 that is closely related to x1. the evaluation for indonesian&rarr;english (using malay) and spanish&rarr;english (using portuguese and pretending spanish is resource-poor) shows an absolute gain of up to 1.35 and 3.37 bleu points, respectively, which is an improvement over the rivaling approaches, while using much less additional data.
model adaptation via model interpolation and boosting for web search ranking. this paper explores two classes of model adaptation methods for web search ranking: model interpolation and error-driven learning approaches based on a boosting algorithm. the results show that model interpolation, though simple, achieves the best results on all the open test sets where the test data is very different from the training data. the tree-based boosting algorithm achieves the best performance on most of the closed test sets where the test data and the training data are similar, but its performance drops significantly on the open test sets due to the instability of trees. several methods are explored to improve the robustness of the algorithm, with limited success.
semi-supervised semantic role labeling using the latent words language model. semantic role labeling (srl) has proved to be a valuable tool for performing automatic analysis of natural language texts. currently however, most systems rely on a large training set, which is manually annotated, an effort that needs to be repeated whenever different languages or a different set of semantic roles is used in a certain application. a possible solution for this problem is semi-supervised learning, where a small set of training examples is automatically expanded using unlabeled texts. we present the latent words language model, which is a language model that learns word similarities from unlabeled texts. we use these similarities for different semi-supervised srl methods as additional features or to automatically expand a small training set. we evaluate the methods on the propbank dataset and find that for small training sizes our best performing system achieves an error reduction of 33.27% f1-measure compared to a state-of-the-art supervised baseline.
towards domain-independent argumentative zoning: evidence from chemistry and computational linguistics. argumentative zoning (az) is an analysis of the argumentative and rhetorical structure of a scientific paper. it has been shown to be reliably used by independent human coders, and has proven useful for various information access tasks. annotation experiments have however so far been restricted to one discipline, computational linguistics (cl). here, we present a more informative az scheme with 15 categories in place of the original 7, and show that it can be applied to the life sciences as well as to cl. we use a domain expert to encode basic knowledge about the subject (such as terminology and domain specific rules for individual categories) as part of the annotation guidelines. our results show that non-expert human coders can then use these guidelines to reliably annotate this scheme in two domains, chemistry and computational linguistics.
on the use of virtual evidence in conditional random fields. virtual evidence (ve), first introduced by (pearl, 1988), provides a convenient way of incorporating prior knowledge into bayesian networks. this work generalizes the use of ve to undirected graphical models and, in particular, to conditional random fields (crfs). we show that ve can be naturally encoded into a crf model as potential functions. more importantly, we propose a novel semi-supervised machine learning objective for estimating a crf model integrated with ve. the objective can be optimized using the expectation-maximization algorithm while maintaining the discriminative nature of crfs. when evaluated on the classifieds data, our approach significantly outperforms the best known solutions reported on this task.