waashk/dblp huggingface dataset:
a discriminative candidate generator for string transformations. string transformation, which maps a source string s into its desirable form t*, is related to various applications including stemming, lemmatization, and spelling correction. the essential and important step for string transformation is to generate candidates to which the given string s is likely to be transformed. this paper presents a discriminative approach for generating candidate strings. we use substring substitution rules as features and score them using an l1-regularized logistic regression model. we also propose a procedure to generate negative instances that affect the decision boundary of the model. the advantage of this approach is that candidate strings can be enumerated by an efficient algorithm because the processes of string transformation are tractable in the model. we demonstrate the remarkable performance of the proposed method in normalizing inflected words and spelling variations.
complexity of finding the bleu-optimal hypothesis in a confusion network. confusion networks are a simple representation of multiple speech recognition or translation hypotheses in a machine translation system. a typical operation on a confusion network is to find the path which minimizes or maximizes a certain evaluation metric. in this article, we show that this problem is generally np-hard for the popular bleu metric, as well as for smaller variants of bleu. this also holds for more complex representations like generic word graphs. in addition, we give an efficient polynomial-time algorithm to calculate unigram bleu on confusion networks, but show that even small generalizations of this data structure render the problem to be np-hard again. since finding the optimal solution is thus not always feasible, we introduce an approximating algorithm based on a multi-stack decoder, which finds a (not necessarily optimal) solution for n-gram bleu in polynomial time.
coarse-to-fine syntactic machine translation using language projections. the intersection of tree transducer-based translation models with n-gram language models results in huge dynamic programs for machine translation decoding. we propose a multipass, coarse-to-fine approach in which the language model complexity is incrementally introduced. in contrast to previous order-based bigram-to-trigram approaches, we focus on encoding-based methods, which use a clustered encoding of the target language. across various encoding schemes, and for multiple language pairs, we show speed-ups of up to 50 times over single-pass decoding while improving bleu score. moreover, our entire decoding cascade for trigram language models is faster than the corresponding bigram pass alone of a bigram-to-trigram decoder.
cocqa: co-training over questions and answers with an application to predicting question subjectivity orientation. an increasingly popular method for finding information online is via the community question answering (cqa) portals such as yahoo! answers, naver, and baidu knows. searching the cqa archives, and ranking, filtering, and evaluating the submitted answers requires intelligent processing of the questions and answers posed by the users. one important task is automatically detecting the question's subjectivity orientation: namely, whether a user is searching for subjective or objective information. unfortunately, real user questions are often vague, ill-posed, poorly stated. furthermore, there has been little labeled training data available for real user questions. to address these problems, we present cocqa, a co-training system that exploits the association between the questions and contributed answers for question analysis tasks. the co-training approach allows cocqa to use the effectively unlimited amounts of unlabeled data readily available in cqa archives. in this paper we study the effectiveness of cocqa for the question subjectivity classification task by experimenting over thousands of real users' questions.
a phrase-based alignment model for natural language inference. the alignment problem---establishing links between corresponding phrases in two related sentences---is as important in natural language inference (nli) as it is in machine translation (mt). but the tools and techniques of mt alignment do not readily transfer to nli, where one cannot assume semantic equivalence, and for which large volumes of bitext are lacking. we present a new nli aligner, the manli system, designed to address these challenges. it uses a phrase-based alignment representation, exploits external lexical resources, and capitalizes on a new set of supervised training data. we compare the performance of manli to existing nli and mt aligners on an nli alignment task over the well-known recognizing textual entailment data. we show that manli significantly outperforms existing aligners, achieving gains of 6.2% in f1 over a representative nli aligner and 10.5% over giza++.
indirect-hmm-based hypothesis alignment for combining outputs from machine translation systems. this paper presents a new hypothesis alignment method for combining outputs of multiple machine translation (mt) systems. an indirect hidden markov model (ihmm) is proposed to address the synonym matching and word ordering issues in hypothesis alignment. unlike traditional hmms whose parameters are trained via maximum likelihood estimation (mle), the parameters of the ihmm are estimated indirectly from a variety of sources including word semantic similarity, word surface similarity, and a distance-based distortion penalty. the ihmm-based method significantly outperforms the state-of-the-art ter-based alignment model in our experiments on nist benchmark datasets. our combined smt system using the proposed method achieved the best chinese-to-english translation result in the constrained training track of the 2008 nist open mt evaluation.
multimodal subjectivity analysis of multiparty conversation. we investigate the combination of several sources of information for the purpose of subjectivity recognition and polarity classification in meetings. we focus on features from two modalities, transcribed words and acoustics, and we compare the performance of three different textual representations: words, characters, and phonemes. our experiments show that character-level features outperform wordlevel features for these tasks, and that a careful fusion of all features yields the best performance.1
cheap and fast - but is it good? evaluating non-expert annotations for natural language tasks. human linguistic annotation is crucial for many natural language processing tasks but can be expensive and time-consuming. we explore the use of amazon's mechanical turk system, a significantly cheaper and faster method for collecting annotations from a broad base of paid non-expert contributors over the web. we investigate five tasks: affect recognition, word similarity, recognizing textual entailment, event temporal ordering, and word sense disambiguation. for all five, we show high agreement between mechanical turk non-expert annotations and existing gold standard labels provided by expert labelers. for the task of affect recognition, we also show that using non-expert labels for training machine learning algorithms can be as effective as using gold standard annotations from experts. we propose a technique for bias correction that significantly improves annotation quality on two tasks. we conclude that many large labeling tasks can be effectively designed and carried out in this method at a fraction of the usual expense.
refining generative language models using discriminative learning. we propose a new approach to language modeling which utilizes discriminative learning methods. our approach is an iterative one: starting with an initial language model, in each iteration we generate 'false' sentences from the current model, and then train a classifier to discriminate between them and sentences from the training corpus. to the extent that this succeeds, the classifier is incorporated into the model by lowering the probability of sentences classified as false, and the process is repeated. we demonstrate the effectiveness of this approach on a natural language corpus and show it provides an 11.4% improvement in perplexity over a modified kneser-ney smoothed trigram.
two languages are better than one (for syntactic parsing). we show that jointly parsing a bitext can substantially improve parse quality on both sides. in a maximum entropy bitext parsing model, we define a distribution over source trees, target trees, and node-to-node alignments between them. features include monolingual parse scores and various measures of syntactic divergence. using the translated portion of the chinese treebank, our model is trained iteratively to maximize the marginal likelihood of training tree pairs, with alignments treated as latent variables. the resulting bitext parser outperforms state-of-the-art monolingual parser baselines by 2.5 f1 at predicting english side trees and 1.8 f1 at predicting chinese side trees (the highest published numbers on these corpora). moreover, these improved trees yield a 2.4 bleu increase when used in a downstream mt evaluation.
n-gram weighting: reducing training data mismatch in cross-domain language model estimation. in domains with insufficient matched training data, language models are often constructed by interpolating component models trained from partially matched corpora. since the n-grams from such corpora may not be of equal relevance to the target domain, we propose an n-gram weighting technique to adjust the component n-gram probabilities based on features derived from readily available segmentation and metadata information for each corpus. using a log-linear combination of such features, the resulting model achieves up to a 1.2% absolute word error rate reduction over a linearly interpolated baseline language model on a lecture transcription task.
maximum entropy based rule selection model for syntax-based statistical machine translation. this paper proposes a novel maximum entropy based rule selection (mers) model for syntax-based statistical machine translation (smt). the mers model combines local contextual information around rules and information of sub-trees covered by variables in rules. therefore, our model allows the decoder to perform context-dependent rule selection during decoding. we incorporate the mers model into a state-of-the-art linguistically syntax-based smt model, the tree-to-string alignment template model. experiments show that our approach achieves significant improvements over the baseline system.
learning to predict code-switching points. predicting possible code-switching points can help develop more accurate methods for automatically processing mixed-language text, such as multilingual language models for speech recognition systems and syntactic analyzers. we present in this paper exploratory results on learning to predict potential code-switching points in spanish-english. we trained different learning algorithms using a transcription of code-switched discourse. to evaluate the performance of the classifiers, we used two different criteria: 1) measuring precision, recall, and f-measure of the predictions against the reference in the transcription, and 2) rating the naturalness of artificially generated code-switched sentences. average scores for the code-switched sentences generated by our machine learning approach were close to the scores of those generated by humans.
construction of an idiom corpus and its application to idiom identification based on wsd incorporating idiom-specific features. some phrases can be interpreted either idiomatically (figuratively) or literally in context, and the precise identification of idioms is indispensable for full-fledged natural language processing (nlp). to this end, we have constructed an idiom corpus for japanese. this paper reports on the corpus and the results of an idiom identification experiment using the corpus. the corpus targets 146 ambiguous idioms, and consists of 102, 846 sentences, each of which is annotated with a literal/idiom label. for idiom identification, we targeted 90 out of the 146 idioms and adopted a word sense disambiguation (wsd) method using both common wsd features and idiom-specific features. the corpus and the experiment are the largest of their kind, as far as we know. as a result, we found that a standard supervised wsd method works well for the idiom identification and achieved an accuracy of 89.25% and 88.86% with/without idiom-specific features and that the most effective idiom-specific feature is the one involving the adjacency of idiom constituents.
better binarization for the cky parsing. we present a study on how grammar binarization empirically affects the efficiency of the cky parsing. we argue that binarizations affect parsing efficiency primarily by affecting the number of incomplete constituents generated, and the effectiveness of binarization also depends on the nature of the input. we propose a novel binarization method utilizing rich information learnt from training corpus. experimental results not only show that different binarizations have great impacts on parsing efficiency, but also confirm that our learnt binarization outperforms other existing methods. furthermore we show that it is feasible to combine existing parsing speed-up techniques with our binarization to achieve even better performance.
scalable language processing algorithms for the masses: a case study in computing word co-occurrence matrices with mapreduce. this paper explores the challenge of scaling up language processing algorithms to increasingly large datasets. while cluster computing has been available in commercial environments for several years, academic researchers have fallen behind in their ability to work on large datasets. i discuss two barriers contributing to this problem: lack of a suitable programming model for managing concurrency and difficulty in obtaining access to hardware. hadoop, an open-source implementation of google's mapreduce framework, provides a compelling solution to both issues. its simple programming model hides system-level details from the developer, and its ability to run on commodity hardware puts cluster computing within the reach of many academic research groups. this paper illustrates these points with a case study in building word cooccurrence matrices from large corpora. i conclude with an analysis of an alternative computing model based on renting instead of buying computer clusters.
improving interactive machine translation via mouse actions. although machine translation (mt) is a very active research field which is receiving an increasing amount of attention from the research community, the results that current mt systems are capable of producing are still quite far away from perfection. because of this, and in order to build systems that yield correct translations, human knowledge must be integrated into the translation process, which will be carried out in our case in an interactive-predictive (ip) framework. in this paper, we show that considering mouse actions as a significant information source for the underlying system improves the productivity of the human translator involved. in addition, we also show that the initial translations that the mt system provides can be quickly improved by an expert by only performing additional mouse actions. in this work, we will be using word graphs as an efficient interface between a phrase-based mt system and the ip engine.
bayesian unsupervised topic segmentation. this paper describes a novel bayesian approach to unsupervised topic segmentation. unsupervised systems for this task are driven by lexical cohesion: the tendency of well-formed segments to induce a compact and consistent lexical distribution. we show that lexical cohesion can be placed in a bayesian context by modeling the words in each topic segment as draws from a multinomial language model associated with the segment; maximizing the observation likelihood in such a model yields a lexically-cohesive segmentation. this contrasts with previous approaches, which relied on hand-crafted cohesion metrics. the bayesian framework provides a principled way to incorporate additional features such as cue phrases, a powerful indicator of discourse structure that has not been previously used in unsupervised segmentation systems. our model yields consistent improvements over an array of state-of-the-art systems on both text and speech datasets. we also show that both an entropy-based analysis and a well-known previous technique can be derived as special cases of the bayesian framework.
phrase translation probabilities with itg priors and smoothing as learning objective. the conditional phrase translation probabilities constitute the principal components of phrase-based machine translation systems. these probabilities are estimated using a heuristic method that does not seem to optimize any reasonable objective function of the word-aligned, parallel training corpus. earlier efforts on devising a better understood estimator either do not scale to reasonably sized training data, or lead to deteriorating performance. in this paper we explore a new approach based on three ingredients (1) a generative model with a prior over latent segmentations derived from inversion transduction grammar (itg), (2) a phrase table containing all phrase pairs without length limit, and (3) smoothing as learning objective using a novel maximum-a-posteriori version of deleted estimation working with expectation-maximization. where others conclude that latent segmentations lead to overfitting and deteriorating performance, we show here that these three ingredients give performance equivalent to the heuristic method on reasonably sized training data.
acquiring domain-specific dialog information from task-oriented human-human interaction through an unsupervised learning. we describe an approach for acquiring the domain-specific dialog knowledge required to configure a task-oriented dialog system that uses human-human interaction data. the key aspects of this problem are the design of a dialog information representation and a learning approach that supports capture of domain information from in-domain dialogs. to represent a dialog for a learning purpose, we based our representation, the form-based dialog structure representation, on an observable structure. we show that this representation is sufficient for modeling phenomena that occur regularly in several dissimilar task-oriented domains, including information-access and problem-solving. with the goal of ultimately reducing human annotation effort, we examine the use of unsupervised learning techniques in acquiring the components of the form-based representation (i.e. task, subtask, and concept). these techniques include statistical word clustering based on mutual information and kullback-liebler distance, texttiling, hmm-based segmentation, and bisecting k-mean document clustering. with some modifications to make these algorithms more suitable for inferring the structure of a spoken dialog, the unsupervised learning algorithms show promise.
revealing the structure of medical dictations with conditional random fields. automatic processing of medical dictations poses a significant challenge. we approach the problem by introducing a statistical framework capable of identifying types and boundaries of sections, lists and other structures occurring in a dictation, thereby gaining explicit knowledge about the function of such elements. training data is created semi-automatically by aligning a parallel corpus of corrected medical reports and corresponding transcripts generated via automatic speech recognition. we highlight the properties of our statistical framework, which is based on conditional random fields (crfs) and implemented as an efficient, publicly available toolkit. finally, we show that our approach is effective both under ideal conditions and for real-life dictation involving speech recognition errors and speech-related phenomena such as hesitation and repetitions.
stacking dependency parsers. we explore a stacked framework for learning to predict dependency structures for natural language sentences. a typical approach in graph-based dependency parsing has been to assume a factorized model, where local features are used but a global function is optimized (mcdonald et al., 2005b). recently nivre and mcdonald (2008) used the output of one dependency parser to provide features for another. we show that this is an example of stacked learning, in which a second predictor is trained to improve the performance of the first. further, we argue that this technique is a novel way of approximating rich non-local features in the second parser, without sacrificing efficient, model-optimal prediction. experiments on twelve languages show that stacking transition-based and graph-based parsers improves performance over existing state-of-the-art dependency parsers.
improving chinese semantic role classification with hierarchical feature selection strategy. in recent years, with the development of chinese semantically annotated corpus, such as chinese proposition bank and normalization bank, the chinese semantic role labeling (srl) task has been boosted. similar to english, the chinese srl can be divided into two tasks: semantic role identification (sri) and classification (src). many features were introduced into these tasks and promising results were achieved. in this paper, we mainly focus on the second task: src. after exploiting the linguistic discrepancy between numbered arguments and argms, we built a semantic role classifier based on a hierarchical feature selection strategy. different from the previous src systems, we divided src into three sub tasks in sequence and trained models for each sub task. under the hierarchical architecture, each argument should first be determined whether it is a numbered argument or an argm, and then be classified into fine-gained categories. finally, we integrated the idea of exploiting argument interdependence into our system and further improved the performance. with the novel method, the classification precision of our system is 94.68%, which outperforms the strong baseline significantly. it is also the state-of-the-art on chinese src.
seeded discovery of base relations in large corpora. relationship discovery is the task of identifying salient relationships between named entities in text. we propose novel approaches for two sub-tasks of the problem: identifying the entities of interest, and partitioning and describing the relations based on their semantics. in particular, we show that term frequency patterns can be used effectively instead of supervised ner, and that the p-median clustering objective function naturally uncovers relation exemplars appropriate for describing the partitioning. furthermore, we introduce a novel application of relationship discovery: the unsupervised identification of protein-protein interaction phrases.
multilingual subjectivity analysis using machine translation. although research in other languages is increasing, much of the work in subjectivity analysis has been applied to english data, mainly due to the large body of electronic resources and tools that are available for this language. in this paper, we propose and evaluate methods that can be employed to transfer a repository of subjectivity resources across languages. specifically, we attempt to leverage on the resources available for english and, by employing machine translation, generate resources for subjectivity analysis in other languages. through comparative evaluations on two different languages (romanian and spanish), we show that automatic translation is a viable alternative for the construction of resources and tools for subjectivity analysis in a new target language.