waashk/dblp huggingface dataset:
a simple unsupervised learner for pos disambiguation rules given only a minimal lexicon. we propose a new model for unsupervised pos tagging based on linguistic distinctions between open and closed-class items. exploiting notions from current linguistic theory, the system uses far less information than previous systems, far simpler computational methods, and far sparser descriptions in learning contexts. by applying simple language acquisition techniques based on counting, the system is given the closed-class lexicon, acquires a large open-class lexicon and then acquires disambiguation rules for both. this system achieves a 20% error reduction for pos tagging over state-of-the-art unsupervised systems tested under the same conditions, and achieves comparable accuracy when trained with much less prior information.
self-training pcfg grammars with latent annotations across languages. we investigate the effectiveness of self-training pcfg grammars with latent annotations (pcfg-la) for parsing languages with different amounts of labeled training data. compared to charniak's lexicalized parser, the pcfg-la parser was more effectively adapted to a language for which parsing has been less well developed (i.e., chinese) and benefited more from self-training. we show for the first time that self-training is able to significantly improve the performance of the pcfg-la parser, a single generative parser, on both small and large amounts of labeled training data. our approach achieves state-of-the-art parsing accuracies for a single parser on both english (91.5%) and chinese (85.2%).
statistical estimation of word acquisition with application to readability prediction. models of language learning play a central role in a wide range of applications: from psycholinguistic theories of how people acquire new word knowledge, to information systems that can automatically match content to users' reading ability. we present a novel statistical approach that can infer the distribution of a word's likely acquisition age automatically from authentic texts collected from the web. we then show that combining these acquisition age distributions for all words in a document provides an effective semantic component for predicting reading difficulty of new texts. we also compare our automatically inferred acquisition ages with norms from existing oral studies, revealing interesting historical trends as well as differences between oral and written word acquisition processes.
re-ranking models based-on small training data for spoken language understanding. the design of practical language applications by means of statistical approaches requires annotated data, which is one of the most critical constraint. this is particularly true for spoken dialog systems since considerably domain-specific conceptual annotation is needed to obtain accurate language understanding models. since data annotation is usually costly, methods to reduce the amount of data are needed. in this paper, we show that better feature representations serve the above purpose and that structure kernels provide the needed improved representation. given the relatively high computational cost of kernel methods, we apply them to just re-rank the list of hypotheses provided by a fast generative model. experiments with support vector machines and different kernels on two different dialog corpora show that our re-ranking models can achieve better results than state-of-the-art approaches when small data is available.
reading to learn: constructing features from semantic abstracts. machine learning offers a range of tools for training systems from data, but these methods are only as good as the underlying representation. this paper proposes to acquire representations for machine learning by reading text written to accommodate human learning. we propose a novel form of semantic analysis called reading to learn, where the goal is to obtain a high-level semantic abstract of multiple documents in a representation that facilitates learning. we obtain this abstract through a generative model that requires no labeled data, instead leveraging repetition across multiple documents. the semantic abstract is converted into a transformed feature space for learning, resulting in improved generalization on a relational learning task.
semantic dependency parsing of nombank and propbank: an efficient integrated approach via a large-scale feature selection. we present an integrated dependency-based semantic role labeling system for english from both nombank and propbank. by introducing assistant argument labels and considering much more feature templates, two optimal feature template sets are obtained through an effective feature selection procedure and help construct a high performance single srl system. from the evaluations on the date set of conll-2008 shared task, the performance of our system is quite close to the state of the art. as to our knowledge, this is the first integrated srl system that achieves a competitive performance against previous pipeline systems.
multi-class confidence weighted algorithms. the recently introduced online confidence-weighted (cw) learning algorithm for binary classification performs well on many binary nlp tasks. however, for multi-class problems cw learning updates and inference cannot be computed analytically or solved as convex optimization problems as they are in the binary case. we derive learning algorithms for the multi-class cw setting and provide extensive evaluation using nine nlp datasets, including three derived from the recently released new york times corpus. our best algorithm out-performs state-of-the-art online and batch methods on eight of the nine tasks. we also show that the confidence information maintained during learning yields useful probabilistic information at test time.
convolution kernels on constituent, dependency and sequential structures for relation extraction. this paper explores the use of innovative kernels based on syntactic and semantic structures for a target relation extraction task. syntax is derived from constituent and dependency parse trees whereas semantics concerns to entity types and lexical sequences. we investigate the effectiveness of such representations in the automated relation extraction from texts. we process the above data by means of support vector machines along with the syntactic tree, the partial tree and the word sequence kernels. our study on the ace 2004 corpus illustrates that the combination of the above kernels achieves high effectiveness and significantly improves the current state-of-the-art.
improving nominal srl in chinese language with verbal srl information and automatic predicate recognition. this paper explores chinese semantic role labeling (srl) for nominal predicates. besides those widely used features in verbal srl, various nominal srl-specific features are first included. then, we improve the performance of nominal srl by integrating useful features derived from a state-of-the-art verbal srl system. finally, we address the issue of automatic predicate recognition, which is essential for a nominal srl system. evaluation on chinese nombank shows that our research in integrating various features derived from verbal srl significantly improves the performance. it also shows that our nominal srl system much outperforms the state-of-the-art ones.
statistical bistratal dependency parsing. we present an inexact search algorithm for the problem of predicting a two-layered dependency graph. the algorithm is based on a k-best version of the standard cubic-time search algorithm for projective dependency parsing, which is used as the backbone of a beam search procedure. this allows us to handle the complex non-local feature dependencies occurring in bistratal parsing if we model the interdependency between the two layers. we apply the algorithm to the syntactic---semantic dependency parsing task of the conll-2008 shared task, and we obtain a competitive result equal to the highest published for a system that jointly learns syntactic and semantic structure.
predicting subjectivity in multimodal conversations. in this research we aim to detect subjective sentences in multimodal conversations. we introduce a novel technique wherein subjective patterns are learned from both labeled and unlabeled data, using n-gram word sequences with varying levels of lexical instantiation. applying this technique to meeting speech and email conversations, we gain significant improvement over state-of-the-art approaches. furthermore, we show that coupling the pattern-based approach with features that capture characteristics of general conversation structure yields additional improvement.
investigation of question classifier in question answering. in this paper, we investigate how an accurate question classifier contributes to a question answering system. we first present a maximum entropy (me) based question classifier which makes use of head word features and their wordnet hypernyms. we show that our question classifier can achieve the state of the art performance in the standard uiuc question dataset. we then investigate quantitatively the contribution of this question classifier to a feature driven question answering system. with our accurate question classifier and some standard question answer features, our question answering system performs close to the state of the art using trec corpus.
a unified model of phrasal and sentential evidence for information extraction. information extraction (ie) systems that extract role fillers for events typically look at the local context surrounding a phrase when deciding whether to extract it. often, however, role fillers occur in clauses that are not directly linked to an event word. we present a new model for event extraction that jointly considers both the local context around a phrase along with the wider sentential context in a probabilistic framework. our approach uses a sentential event recognizer and a plausible role-filler recognizer that is conditioned on event sentences. we evaluate our system on two ie data sets and show that our model performs well in comparison to existing ie systems that rely on local phrasal context.
subjectivity word sense disambiguation. this paper investigates a new task, subjectivity word sense disambiguation (swsd), which is to automatically determine which word instances in a corpus are being used with subjective senses, and which are being used with objective senses. we provide empirical evidence that swsd is more feasible than full word sense disambiguation, and that it can be exploited to improve the performance of contextual subjectivity and sentiment analysis systems.
clustering to find exemplar terms for keyphrase extraction. keyphrases are widely used as a brief summary of documents. since manual assignment is time-consuming, various unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. in practice, the keyphrases of a document should not only be statistically important in the document, but also have a good coverage of the document. based on this observation, we propose an unsupervised method for keyphrase extraction. firstly, the method finds exemplar terms by leveraging clustering techniques, which guarantees the document to be semantically covered by these exemplar terms. then the keyphrases are extracted from the document using the exemplar terms. our method outperforms sate-of-the-art graph-based ranking methods (textrank) by 9.5% in f1-measure.
unsupervised morphological segmentation and clustering with document boundaries. many approaches to unsupervised morphology acquisition incorporate the frequency of character sequences with respect to each other to identify word stems and affixes. this typically involves heuristic search procedures and calibrating multiple arbitrary thresholds. we present a simple approach that uses no thresholds other than those involved in standard application of x2 significance testing. a key part of our approach is using document boundaries to constrain generation of candidate stems and affixes and clustering morphological variants of a given word stem. we evaluate our model on english and the mayan language uspanteko; it compares favorably to two benchmark systems which use considerably more complex strategies and rely more on experimentally chosen threshold values.
unsupervised semantic parsing. we present the first unsupervised approach to the problem of learning a semantic parser, using markov logic. our usp system transforms dependency trees into quasi-logical forms, recursively induces lambda forms from these, and clusters them to abstract away syntactic variations of the same meaning. the map semantic parse of a sentence is obtained by recursively assigning its parts to lambda-form clusters and composing them. we evaluate our approach by using it to extract a knowledge base from biomedical abstracts and answer questions. usp substantially outperforms textrunner, dirt and an informed baseline on both precision and recall on this task.
a comparison of windowless and window-based computational association measures as predictors of syntagmatic human associations. distance-based (windowless) word assocation measures have only very recently appeared in the nlp literature and their performance compared to existing windowed or frequency-based measures is largely unknown. we conduct a large-scale empirical comparison of a variety of distance-based and frequency-based measures for the reproduction of syntagmatic human assocation norms. overall, our results show an improvement in the predictive power of windowless over windowed measures. this provides support to some of the previously published theoretical advantages and makes windowless approaches a promising avenue to explore further. this study also serves as a first comparison of windowed methods across numerous human association datasets. during this comparison we also introduce some novel variations of window-based measures which perform as well as or better in the human association norm task than established measures.
a bayesian model of syntax-directed tree to string grammar induction. tree based translation models are a compelling means of integrating linguistic information into machine translation. syntax can inform lexical selection and reordering choices and thereby improve translation quality. research to date has focussed primarily on decoding with such models, but less on the difficult problem of inducing the bilingual grammar from data. we propose a generative bayesian model of tree-to-string translation which induces grammars that are both smaller and produce better translations than the previous heuristic two-stage approach which employs a separate word alignment step.
effective use of linguistic and contextual information for statistical machine translation. current methods of using lexical features in machine translation have difficulty in scaling up to realistic mt tasks due to a prohibitively large number of parameters involved. in this paper, we propose methods of using new linguistic and contextual features that do not suffer from this problem and apply them in a state-of-the-art hierarchical mt system. the features used in this work are non-terminal labels, non-terminal length distribution, source string context and source dependency lm scores. the effectiveness of our techniques is demonstrated by significant improvements over a strong base-line. on arabic-to-english translation, improvements in lower-cased bleu are 2.0 on nist mt06 and 1.7 on mt08 newswire data on decoding output. on chinese-to-english translation, the improvements are 1.0 on mt06 and 0.8 on mt08 newswire data.
feasibility of human-in-the-loop minimum error rate training. minimum error rate training (mert) involves choosing parameter values for a machine translation (mt) system that maximize performance on a tuning set as measured by an automatic evaluation metric, such as bleu. the method is best when the system will eventually be evaluated using the same metric, but in reality, most mt evaluations have a human-based component. although performing mert with a human-based metric seems like a daunting task, we describe a new metric, rypt, which takes human judgments into account, but only requires human input to build a database that can be reused over and over again, hence eliminating the need for human input at tuning time. in this investigative study, we analyze the diversity (or lack thereof) of the candidates produced during mert, we describe how this redundancy can be used to our advantage, and show that rypt is a better predictor of translation quality than bleu.
identification of event mentions and their semantic class. complex tasks like question answering need to be able to identify events in text and the relations among those events. we show that this event identification task and a related task, identifying the semantic class of these events, can both be formulated as classification problems in a word-chunking paradigm. we introduce a variety of linguistically motivated features for this task and then train a system that is able to identify events with a precision of 82% and a recall of 71%. we then show a variety of analyses of this model, and their implications for the event identification task.
the impact of parse quality on syntactically-informed statistical machine translation. we investigate the impact of parse quality on a syntactically-informed statistical machine translation system applied to technical text. we vary parse quality by varying the amount of data used to train the parser. as the amount of data increases, parse quality improves, leading to improvements in machine translation output and results that significantly outperform a state-of-the-art phrasal baseline.
learning phrasal categories. in this work we learn clusters of contextual annotations for non-terminals in the penn treebank. perhaps the best way to think about this problem is to contrast our work with that of klein and manning (2003). that research used tree-transformations to create various grammars with different contextual annotations on the non-terminals. these grammars were then used in conjunction with a cky parser. the authors explored the space of different annotation combinations by hand. here we try to automate the process -- to learn the "right" combination automatically. our results are not quite as good as those carefully created by hand, but they are close (84.8 vs 85.7).
learning field compatibilities to extract database records from unstructured text. named-entity recognition systems extract entities such as people, organizations, and locations from unstructured text. rather than extract these mentions in isolation, this paper presents a record extraction system that assembles mentions into records (i.e. database tuples). we construct a probabilistic model of the compatibility between field values, then employ graph partitioning algorithms to cluster fields into cohesive records. we also investigate compatibility functions over sets of fields, rather than simply pairs of fields, to examine how higher representational power can impact performance. we apply our techniques to the task of extracting contact records from faculty and student homepages, demonstrating a 53% error reduction over baseline approaches.
capturing out-of-vocabulary words in arabic text. the increasing flow of information between languages has led to a rise in the frequency of non-native or loan words, where terms of one language appear transliterated in another. dealing with such out of vocabulary words is essential for successful cross-lingual information retrieval. for example, techniques such as stemming should not be applied indiscriminately to all words in a collection, and so before any stemming, foreign words need to be identified. in this paper, we investigate three approaches for the identification of foreign words in arabic text: lexicons, language patterns, and n-grams and present that results show that lexicon-based approaches outperform the other techniques.
a discriminative model for tree-to-tree translation. this paper proposes a statistical, tree-to-tree model for producing translations. two main contributions are as follows: (1) a method for the extraction of syntactic structures with alignment information from a parallel corpus of translations, and (2) use of a discriminative, feature-based model for prediction of these target-language syntactic structures---which we call aligned extended projections, or aeps. an evaluation of the method on translation from german to english shows similar performance to the phrase-based model of koehn et al. (2003).
unsupervised named entity transliteration using temporal and phonetic correlation. in this paper we investigate unsupervised name transliteration using comparable corpora, corpora where texts in the two languages deal in some of the same topics --- and therefore share references to named entities --- but are not translations of each other. we present two distinct methods for transliteration, one approach using an unsupervised phonetic transliteration method, and the other using the temporal distribution of candidate pairs. each of these approaches works quite well, but by combining the approaches one can achieve even better results. we believe that the novelty of our approach lies in the phonetic-based scoring method, which is based on a combination of carefully crafted phonetic features, and empirical results from the pronunciation errors of second-language learners of english. unlike previous approaches to transliteration, this method can in principle work with any pair of languages in the absence of a training dictionary, provided one has an estimate of the pronunciation of words in text.
priming effects in combinatory categorial grammar. this paper presents a corpus-based account of structural priming in human sentence processing, focusing on the role that syntactic representations play in such an account. we estimate the strength of structural priming effects from a corpus of spontaneous spoken dialogue, annotated syntactically with combinatory categorial grammar (ccg) derivations. this methodology allows us to test a range of predictions that ccg makes about priming. in particular, we present evidence for priming between lexical and syntactic categories encoding partially satisfied sub-categorization frames, and we show that priming effects exist both for incremental and normal-form ccg derivations.
partially supervised sense disambiguation by learning sense number from tagged and untagged corpora. supervised and semi-supervised sense disambiguation methods will mis-tag the instances of a target word if the senses of these instances are not defined in sense inventories or there are no tagged instances for these senses in training data. here we used a model order identification method to avoid the misclassification of the instances with undefined senses by discovering new senses from mixed data (tagged and untagged corpora). this algorithm tries to obtain a natural partition of the mixed data by maximizing a stability criterion defined on the classification result from an extended label propagation algorithm over all the possible values of the number of senses (or sense number, model order). experimental results on senseval-3 data indicate that it outperforms svm, a one-class partially supervised classification algorithm, and a clustering based model order identification algorithm when the tagged data is incomplete.
distributed language modeling for n-best list re-ranking. in this paper we describe a novel distributed language model for n-best list re-ranking. the model is based on the client/server paradigm where each server hosts a portion of the data and provides information to the client. this model allows for using an arbitrarily large corpus in a very efficient way. it also provides a natural platform for relevance weighting and selection. we applied this model on a 2.97 billion-word corpus and re-ranked the n-best list from hiero, a state-of-the-art phrase-based system. using bleu as a metric, the re-ranked translation achieves a relative improvement of 4.8%, significantly better than the model-best translation.