waashk/dblp huggingface dataset:
a discriminative global training algorithm for statistical mt. this paper presents a novel training algorithm for a linearly-scored block sequence translation model. the key component is a new procedure to directly optimize the global scoring function used by a smt decoder. no translation, language, or distortion model probabilities are used as in earlier work on smt. therefore our method, which employs less domain specific knowledge, is both simpler and more extensible than previous approaches. moreover, the training procedure treats the decoder as a black-box, and thus can be used to optimize any decoding scheme. the training algorithm is evaluated on a standard arabic-english translation task.
quasi-destructive graph unification. graph unification is the most expensive part of unification-based grammar parsing. it often takes over 90% of the total parsing time of a sentence. we focus on two speed-up elements in the design of unification algorithms: 1) elimination of excessive copying by only copying successful unifications, 2) finding unification failures as soon as possible. we have developed a scheme to attain these two elements without expensive overhead through temporarily modifying graphs during unification to eliminate copying during unification. we found that parsing relatively long sentences (requiring about 500 top-level unifications during a parse) using our algorithm is approximately twice as fast as parsing the same sentences using wroblewski's algorithm.
statistical phrase-based models for interactive computer-assisted translation. obtaining high-quality machine translations is still a long way off. a post-editing phase is required to improve the output of a machine translation system. an alternative is the so called computer-assisted translation. in this framework, a human translator interacts with the system in order to obtain high-quality translations. a statistical phrase-based approach to computer-assisted translation is described in this article. a new decoder algorithm for interactive search is also presented, that combines monotone and non-monotone search. the system has been assessed in the trans type-2 project for the translation of several printer manuals, from (to) english to (from) spanish, german and french.
graph-structured stack and natural language parsing. a general device for handling nondeterminism in stack operations is described. the device, called a graph-structured stack, can eliminate duplication of operations throughout the nondeterministic processes. this paper then applies the graph-structured stack to various natural language parsing methods, including atn, lr parsing, categorial grammar and principle-based parsing. the relationship between the graph-structured stack and a chart in chart parsing is also discussed.
maximizing top-down constraints for unification-based systems. a left-corner parsing algorithm with topdown filtering has been reported to show very efficient performance for unification-based systems. however, due to the nontermination of parsing with left-recursive grammars, top-down constraints must be weakened. in this paper, a general method of maximizng top-down constraints is proposed. the method provides a procedure to dynamically compute restrictor, a minimum set of features involved in an infinite loop for every propagation path; thus top-down constraints are maximally propagated.
joint learning improves semantic role labeling. despite much recent progress on accurate semantic role labeling, previous work has largely used independent classifiers, possibly combined with separate label sequence models via viterbi decoding. this stands in stark contrast to the linguistic observation that a core argument frame is a joint structure, with strong dependencies between arguments. we show how to build a joint model of argument frames, incorporating novel features that model these interactions into discriminative log-linear models. this system achieves an error reduction of 22% on all arguments and 32% on core arguments over a state-of-the art independent classifier for gold-standard parse trees on propbank.
pronunciation modeling for improved spelling correction. this paper presents a method for incorporating word pronunciation information in a noisy channel model for spelling correction. the proposed method builds an explicit error model for word pronunciations. by modeling pronunciation similarities between words we achieve a substantial performance improvement over the previous best performing models for spelling correction.
discourse obligations in dialogue processing. we show that in modeling social interaction, particularly dialogue, the attitude of obligation can be a useful adjunct to the popularly considered attitudes of belief, goal, and intention and their mutual and shared counterparts. in particular, we show how discourse obligations can be used to account in a natural manner for the connection between a question and its answer in dialogue and how obligations can be used along with other parts of the discourse context to extend the coverage of a dialogue system.
spatial lexicalization in the translation of prepositional phrases. a pattern in the translation of locative prepositional phrases between english and spanish is presented. a way of exploiting this pattern is proposed in the context of a multilingual machine translation system under development.
using word support model to improve chinese input system. this paper presents a word support model (wsm). the wsm can effectively perform homophone selection and syllable-word segmentation to improve chinese input systems. the experimental results show that: (1) the wsm is able to achieve tonal (syllables input with four tones) and toneless (syllables input without four tones) syllable-to-word (stw) accuracies of 99% and 92%, respectively, among the converted words; and (2) while applying the wsm as an adaptation processing, together with the microsoft input method editor 2003 (msime) and an optimized bigram model, the average tonal and toneless stw improvements are 37% and 35%, respectively.
integrated morphological and syntactic disambiguation for modern hebrew. current parsing models are not immediately applicable for languages that exhibit strong interaction between morphology and syntax, e.g., modern hebrew (mh), arabic and other semitic languages. this work represents a first attempt at modeling morphological-syntactic interaction in a generative probabilistic framework to allow for mh parsing. we show that morphological information selected in tandem with syntactic categories is instrumental for parsing semitic languages. we further show that redundant morphological information helps syntactic disambiguation.
tagging english by path voting constraints. we describe a constraint-based tagging approach where individual constraint rules vote on sequences of matching tokens and tags. disambiguation of all tokens in a sentence is performed at the very end by selecting tags that appear on the path that receives the highest vote. this constraint application paradigm makes the outcome of the disambiguation independent of the rule sequence, and hence relieves the rule developer from worrying about potentially conflicting rule sequencing. the approach can also combine statistically and manually obtained constraints, and incorporate negative constraint rules to rule out certain patterns. we have applied this approach to tagging english text from the wall street journal and the brown corpora. our results from the wall street journal corpus indicate that with 400 statistically derived constraint rules and about 800 hand-crafted constraint rules, we can attain an average accuracy of 97.89% on the training corpus and an average accuracy of 97.50% on the testing corpus. we can also relax the single tag per token limitation and allow ambiguous tagging which lets us trade recall and precision.
automatically creating bilingual lexicons for machine translation from bilingual text. a method is presented for automatically augmenting the bilingual lexicon of an existing machine translation system, by extracting bilingual entries from aligned bilingual text. the proposed method only relies on the resources already available in the mt system itself. it is based on the use of bilingual lexical templates to match the terminal symbols in the parses of the aligned sentences.
advances in discriminative parsing. the present work advances the accuracy and training speed of discriminative parsing. our discriminative parsing method has no generative component, yet surpasses a generative baseline on constituent parsing, and does so with minimal linguistic cleverness. our model can incorporate arbitrary features of the input and parse state, and performs feature selection incrementally over an exponential feature space during training. we demonstrate the flexibility of our approach by testing it with several parsing strategies and various feature sets. our implementation is freely available at: http://nlp.cs.nyu.edu/parser/.
supervised and unsupervised learning for sentence compression. in statistics-based summarization - step one: sentence compression, knight and marcu (knight and marcu, 2000) (k&m) present a noisy-channel model for sentence compression. the main difficulty in using this method is the lack of data; knight and marcu use a corpus of 1035 training sentences. more data is not easily available, so in addition to improving the original k&m noisy-channel model, we create unsupervised and semi-supervised models of the task. finally, we point out problems with modeling the task in this way. they suggest areas for future research.
thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. this paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). the classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. a phrase has a positive semantic orientation when it has good associations (e.g., "subtle nuances") and a negative semantic orientation when it has bad associations (e.g., "very cavalier"). in this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word "excellent" minus the mutual information between the given phrase and the word "poor". a review is classified as recommended if the average semantic orientation of its phrases is positive. the algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). the accuracy ranges from 84% for automobile reviews to 66% for movie reviews.
expressing implicit semantic relations without supervision. we present an unsupervised learning algorithm that mines large text corpora for patterns that express implicit semantic relations. for a given input word pair x:y with some unspecified semantic relations, the corresponding output list of patterns (p1,..., pm) is ranked according to how well each pattern pi expresses the relations between x and y. for example, given x = ostrich and y = bird, the two highest ranking output patterns are "x is the largest y" and "y such as the x". the output patterns are intended to be useful for finding further pairs with the same relations, to support the construction of lexicons, ontologies, and semantic networks. the patterns are sorted by pertinence, where the pertinence of a pattern pi for a word pair x:y is the expected relational similarity between the given pair and typical pairs for pi. the algorithm is empirically evaluated on two tasks, solving multiple-choice sat word analogy questions and classifying semantic relations in noun-modifier pairs. on both tasks, the algorithm achieves state-of-the-art results, performing significantly better than several alternative pattern ranking algorithms, based on tf-idf.
named entity extraction based on a maximum entropy model and transformation rules. this paper describes named entity (ne) extraction based on a maximum entropy (m. e.) model and transformation rules. there are two types of named entities when focusing on the relationship between morphemes and nes as defined in the ne task of the irex competition held in 1999. each ne consists of one or more morphemes, or includes a substring of a morpheme. we extract the former type of ne by using the m. e. model. we then extract the latter type of ne by applying transformation rules to the text.
morphological analysis of a large spontaneous speech corpus in japanese. this paper describes two methods for detecting word segments and their morphological information in a japanese spontaneous speech corpus, and describes how to tag a large spontaneous speech corpus accurately by using the two methods. the first method is used to detect any type of word segments. the second method is used when there are several definitions for word segments and their pos categories, and when one type of word segments includes another type of word segments. in this paper, we show that by using semi-automatic analysis we achieve a precision of better than 99% for detecting and tagging short words and 97% for long words; the two types of words that comprise the corpus. we also show that better accuracy is achieved by using both methods than by using only the first.
trimming cfg parse trees for sentence compression using machine learning approaches. sentence compression is a task of creating a short grammatical sentence by removing extraneous words or phrases from an original sentence while preserving its meaning. existing methods learn statistics on trimming context-free grammar (cfg) rules. however, these methods sometimes eliminate the original meaning by incorrectly removing important parts of sentences, because trimming probabilities only depend on parents' and daughters' non-terminals in applied cfg rules. we apply a maximum entropy model to the above method. our method can easily include various features, for example, other parts of a parse tree or words the sentences contain. we evaluated the method using manually compressed sentences and human judgments. we found that our method produced more grammatical and informative compressed sentences than other methods.
a method for relating multiple newspaper articles by using graphs, and its application to webcasting. this paper describes methods for relating (threading) multiple newspaper articles, and for visualizing various characteristics of them by using a directed graph. a set of articles is represented by a set of word vectors, and the similarity between the vectors is then calculated. the graph is constructed from the similarity matrix. by applying some constraints on the chronological ordering of articles, an efficient threading algorithm that runs in o(n) time (where n is the number of articles) is obtained. the constructed graph is visualized with words that represent the topics of the threads, and words that represent new information in each article. the threading technique is suitable for webcasting (push) applications. a threading server determines relationships among articles from various news sources, and creates files containing their threading information. this information is represented in extended markup language (xml), and can be visualized on most web browsers. the xml-based representation and a current prototype are described in this paper.
strategies for adding control information to declarative grammars. strategies are proposed for combining different kinds of constraints in declarative grammars with a detachable layer of control information. the added control information is the basis for parametrized dynamically controlled linguistic deduction, a form of linguistic processing that permits the implementation of plausible linguistic performance models without giving up the declarative formulation of linguistic competence. the information can be used by the linguistic processor for ordering the sequence in which conjuncts and disjuncts are processed, for mixing depth-first and breadth-first search, for cutting off undesired derivations, and for constraint-relaxation.
a statistical model for domain-independent text segmentation. we propose a statistical method that finds the maximum-probability segmentation of a given text. this method does not require training data because it estimates probabilities from the given text. therefore, it can be applied to any text in any domain. an experiment showed that the method is more accurate than or at least as accurate as a state-of-the-art text segmentation system.
reliable measures for aligning japanese-english news articles and sentences. we have aligned japanese and english news articles and sentences to make a large parallel corpus. we first used a method based on cross-language information retrieval (clir) to align the japanese and english articles and then used a method based on dynamic programming (dp) matching to align the japanese and english sentences in these articles. however, the results included many incorrect alignments. to remove these, we propose two measures (scores) that evaluate the validity of alignments. the measure for article alignment uses similarities in sentences aligned by dp matching and that for sentence alignment uses similarities in articles aligned by clir. they enhance each other to improve the accuracy of alignment. using these measures, we have successfully constructed a large-scale article and sentence alignment corpus available to the public.
organizing english reading materials for vocabulary learning. we propose a method of organizing reading materials for vocabulary learning. it enables us to select a concise set of reading texts (from a target corpus) that contains all the target vocabulary to be learned. we used a specialized vocabulary for an english certification test as the target vocabulary and used english wikipedia, a free-content encyclopedia, as the target corpus. the organized reading materials would enable learners not only to study the target vocabulary efficiently but also to gain a variety of knowledge through reading. the reading materials are available on our web site.
word vectors and two kinds of similarity. this paper examines what kind of similarity between words can be represented by what kind of word vectors in the vector space model. through two experiments, three methods for constructing word vectors, i.e., lsa-based, cooccurrence-based and dictionary-based methods, were compared in terms of the ability to represent two kinds of similarity, i.e., taxonomic similarity and associative similarity. the result of the comparison was that the dictionary-based word vectors better reflect taxonomic similarity, while the lsa-based and the cooccurrence-based word vectors better reflect associative similarity.
general-to-specific model selection for subcategorization preference. this paper proposes a novel method for learning probability models of subcategorization preference of verbs. we consider the issues of case dependencies and noun class generalization in a uniform way by employing the maximum entropy modeling method. we also propose a new model selection algorithm which starts from the most general model and gradually examines more specific models. in the experimental evaluation, it is shown that both of the case dependencies and specific sense restriction selected by the proposed method contribute to improving the performance in subcategorization preference resolution.
a minimalist head-corner parser. in the minimalist program (chomsky, 1992) it is assumed that there are different types of projections (lexical and functional) and therefore different types of heads. this paper explains why functional heads are not treated as head-corners by the minimalist head-corner parser described here.
computer aided interpretation of lexical coocurrences. this paper addresses the problem of developing a large semantic lexicon for natural language processing. the increasing availability of machine readable documents offers an opportunity to the field of lexical semantics, by providing experimental evidence of word uses (on-line texts) and word definitions (on-line dictionaries).the system presented hereafter, petrarca, detects word cooccurrences from a large sample of press agency releases on finance and economics, and uses these associations to build a case-based semantic lexicon. synt&aacute;ctically valid cooccurences including a new word w are detected by a high-coverage morphosyntactic analyzer. syntactic relations are interpreted e.g. replaced by case relations, using a a catalogue of patterns/interpretation pairs, a concept type hierarchy, and a set of selectional restriction rules on semantic interpretation types.
effective phrase translation extraction from alignment models. phrase level translation models are effective in improving translation quality by addressing the problem of local re-ordering across language boundaries. methods that attempt to fundamentally modify the traditional ibm translation model to incorporate phrases typically do so at a prohibitive computational cost. we present a technique that begins with improved ibm models to create phrase level knowledge sources that effectively represent local as well as global phrasal context. our method is robust to noisy alignments at both the sentence and corpus level, delivering high quality phrase level translation pairs that contribute to significant improvements in translation quality (as measured by the bleu metric) over word based lexica as well as a competing alignment based method.
multilingual computational semantic lexicons in action: the wysinnwyg approach to nlp. much effort has been put into computational lexicons over the years, and most systems give much room to (lexical) semantic data. however, in these systems, the effort put on the study and representation of lexical items to express the underlying continuum existing in 1) language vagueness and polysemy, and 2) language gaps and mismatches, has remained embryonic. a sense enumeration approach fails from a theoretical point of view to capture the core meaning of words, let alone relate word meanings to one another, and complicates the task of nlp by multiplying ambiguities in analysis and choices in generation. in this paper, i study computational semantic lexicon representation from a multilingual point of view, reconciling different approaches to lexicon representation: i) vagueness for lexemes which have a more or less finer grained semantics with respect to other languages; ii) underspecification for lexemes which have multiple related facets; and, iii) lexical rules to relate systematic polysemy to systematic ambiguity. i build on a what you see is not necessarily what you get (wysinnwyg) approach to provide the nlp system with the "right" lexical data already tuned towards a particular task. in order to do so, i argue for a lexical semantic approach to lexicon representation. i exemplify my study through a cross-linguistic investigation on spatially-based expressions.
the computational lexical semantics of syntagmatic relations. in this paper, we address the issue of syntagmatic expressions from a computational lexical semantic perspective. from a representational viewpoint, we argue for a hybrid approach combining linguistic and conceptual paradigms, in order to account for the continuum we find in natural languages from free combining words to frozen expressions. in particular, we focus on the place of lexical and semantic restricted co-occurrences. from a processing viewpoint, we show how to generate/analyze syntagmatic expressions by using an efficient constraintbased processor, well fitted for a knowledge-driven approach.