waashk/dblp huggingface dataset:
transliteration as constrained optimization. this paper introduces a new method for identifying named-entity (ne) transliterations in bilingual corpora. recent works have shown the advantage of discriminative approaches to transliteration: given two strings (ws, wt) in the source and target language, a classifier is trained to determine if wt is the transliteration of ws. this paper shows that the transliteration problem can be formulated as a constrained optimization problem and thus take into account contextual dependencies and constraints among character bi-grams in the two strings. we further explore several methods for learning the objective function of the optimization problem and show the advantage of learning it discriminately. our experiments show that the new framework results in over 50% improvement in translating english nes to hebrew.
question classification using head words and their hypernyms. question classification plays an important role in question answering. features are the key to obtain an accurate question classifier. in contrast to li and roth (2002)'s approach which makes use of very rich feature space, we propose a compact yet effective feature set. in particular, we propose head word feature and present two approaches to augment semantic features of such head words using wordnet. in addition, lesk's word sense disambiguation (wsd) algorithm is adapted and the depth of hypernym feature is optimized. with further augment of other standard features such as unigrams, our linear svm and maximum entropy (me) models reach the accuracy of 89.2% and 89.0% respectively over a standard benchmark dataset, which outperform the best previously reported accuracy of 86.2%.
ltag dependency parsing with bidirectional incremental construction. in this paper, we first introduce a new architecture for parsing, bidirectional incremental parsing. we propose a novel algorithm for incremental construction, which can be applied to many structure learning problems in nlp. we apply this algorithm to ltag dependency parsing, and achieve significant improvement on accuracy over the previous best result on the same data set.
a simple and effective hierarchical phrase reordering model. while phrase-based statistical machine translation systems currently deliver state-of-the-art performance, they remain weak on word order changes. current phrase reordering models can properly handle swaps between adjacent phrases, but they typically lack the ability to perform the kind of long-distance re-orderings possible with syntax-based systems. in this paper, we present a novel hierarchical phrase reordering model aimed at improving non-local reorderings, which seamlessly integrates with a standard phrase-based system with little loss of computational efficiency. we show that this model can successfully handle the key examples often used to motivate syntax-based systems, such as the rotation of a prepositional phrase around a noun phrase. we contrast our model with reordering models commonly used in phrase-based systems, and show that our approach provides statistically significant bleu point gains for two language pairs: chinese-english (+0.53 on mt05 and +0.71 on mt08) and arabic-english (+0.55 on mt05).
unsupervised multilingual learning for pos tagging. we demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging. the key hypothesis of multilingual learning is that by combining cues from multiple languages, the structure of each becomes more apparent. we formulate a hierarchical bayesian model for jointly predicting bilingual streams of part-of-speech tags. the model learns language-specific features while capturing cross-lingual patterns in tag distribution for aligned words. once the parameters of our model have been learned on bilingual parallel data, we evaluate its performance on a held-out monolingual test set. our evaluation on six pairs of languages shows consistent and significant performance gains over a state-of-the-art monolingual baseline. for one language pair, we observe a relative reduction in error of 53%.
using bilingual knowledge and ensemble techniques for unsupervised chinese sentiment analysis. it is a challenging task to identify sentiment polarity of chinese reviews because the resources for chinese sentiment analysis are limited. instead of leveraging only monolingual chinese knowledge, this study proposes a novel approach to leverage reliable english resources to improve chinese sentiment analysis. rather than simply projecting english resources onto chinese resources, our approach first translates chinese reviews into english reviews by machine translation services, and then identifies the sentiment polarity of english reviews by directly leveraging english resources. furthermore, our approach performs sentiment analysis for both chinese reviews and english reviews, and then uses ensemble methods to combine the individual analysis results. experimental results on a dataset of 886 chinese product reviews demonstrate the effectiveness of the proposed approach. the individual analysis of the translated english reviews outperforms the individual analysis of the original chinese reviews, and the combination of the individual analysis results further improves the performance.
relative rank statistics for dialog analysis. we introduce the relative rank differential statistic which is a non-parametric approach to document and dialog analysis based on word frequency rank-statistics. we also present a simple method to establish semantic saliency in dialog, documents, and dialog segments using these word frequency rank statistics. applications of our technique include the dynamic tracking of topic and semantic evolution in a dialog, topic detection, automatic generation of document tags, and new story or event detection in conversational speech and text. our approach benefits from the robustness, simplicity and efficiency of non-parametric and rank based approaches and consistently outperformed term-frequency and tf-idf cosine distance approaches in several experiments conducted.
computing word-pair antonymy. knowing the degree of antonymy between words has widespread applications in natural language processing. manually-created lexicons have limited coverage and do not include most semantically contrasting word pairs. we present a new automatic and empirical measure of antonymy that combines corpus statistics with the structure of a published thesaurus. the approach is evaluated on a set of closest-opposite questions, obtaining a precision of over 80%. along the way, we discuss what humans consider antonymous and how antonymy manifests itself in utterances.
automatic inference of the temporal location of situations in chinese text. chinese is a language that does not have morphological tense markers that provide explicit grammaticalization of the temporal location of situations (events or states). however, in many nlp applications such as machine translation, information extraction and question answering, it is desirable to make the temporal location of the situations explicit. we describe a machine learning framework where different sources of information can be combined to predict the temporal location of situations in chinese text. our experiments show that this approach significantly outperforms the most frequent tense baseline. more importantly, the high training accuracy shows promise that this challenging problem is solvable to a level where it can be used in practical nlp applications with more training data, better modeling techniques and more informative and generalizable features.
adapting a lexicalized-grammar parser to contrasting domains. most state-of-the-art wide-coverage parsers are trained on newspaper text and suffer a loss of accuracy in other domains, making parser adaptation a pressing issue. in this paper we demonstrate that a ccg parser can be adapted to two new domains, biomedical text and questions for a qa system, by using manually-annotated training data at the pos and lexical category levels only. this approach achieves parser accuracy comparable to that on newspaper data without the need for annotated parse trees in the new domain. we find that retraining at the lexical category level yields a larger performance increase for questions than for biomedical text and analyze the two datasets to investigate why different domains might behave differently for parser adaptation.
htm: a topic model for hypertexts. previously topic models such as plsi (probabilistic latent semantic indexing) and lda (latent dirichlet allocation) were developed for modeling the contents of plain texts. recently, topic models for processing hypertexts such as web pages were also proposed. the proposed hypertext models are generative models giving rise to both words and hyperlinks. this paper points out that to better represent the contents of hypertexts it is more essential to assume that the hyperlinks are fixed and to define the topic model as that of generating words only. the paper then proposes a new topic model for hypertext processing, referred to as hypertext topic model (htm). htm defines the distribution of words in a document (i.e., the content of the document) as a mixture over latent topics in the document itself and latent topics in the documents which the document cites. the topics are further characterized as distributions of words, as in the conventional topic models. this paper further proposes a method for learning the htm model. experimental results show that htm outperforms the baselines on topic discovery and document classification in three datasets.
a generative model for parsing natural language to meaning representations. in this paper, we present an algorithm for learning a generative model of natural language sentences together with their formal meaning representations with hierarchical structures. the model is applied to the task of mapping sentences to hierarchical representations of their underlying meaning. we introduce dynamic programming techniques for efficient training and decoding. in experiments, we demonstrate that the model, when coupled with a discriminative reranking technique, achieves state-of-the-art performance when tested on two publicly available corpora. the generative model degrades robustly when presented with instances that are different from those seen in training. this allows a notable improvement in recall compared to previous models.
ranking reader emotions using pairwise loss minimization and emotional distribution regression. this paper presents two approaches to ranking reader emotions of documents. past studies assign a document to a single emotion category, so their methods cannot be applied directly to the emotion ranking problem. furthermore, whereas previous research analyzes emotions from the writer's perspective, this work examines readers' emotional states. the first approach proposed in this paper minimizes pairwise ranking errors. in the second approach, regression is used to model emotional distributions. experiment results show that the regression method is more effective at identifying the most popular emotion, but the pairwise loss minimization method produces ranked lists of emotions that have better correlations with the correct lists.
an exploration of document impact on graph-based multi-document summarization. the graph-based ranking algorithm has been recently exploited for multi-document summarization by making only use of the sentence-to-sentence relationships in the documents, under the assumption that all the sentences are indistinguishable. however, given a document set to be summarized, different documents are usually not equally important, and moreover, different sentences in a specific document are usually differently important. this paper aims to explore document impact on summarization performance. we propose a document-based graph model to incorporate the document-level information and the sentence-to-document relationship into the graph-based ranking process. various methods are employed to evaluate the two factors. experimental results on the duc2001 and duc2002 datasets demonstrate that the good effectiveness of the proposed model. moreover, the results show the robustness of the proposed model.
selecting sentences for answering complex questions. complex questions that require inferencing and synthesizing information from multiple documents can be seen as a kind of topic-oriented, informative multi-document summarization. in this paper, we have experimented with one empirical and two unsupervised statistical machine learning techniques: k-means and expectation maximization (em), for computing relative importance of the sentences. however, the performance of these approaches depends entirely on the feature set used and the weighting of these features. we extracted different kinds of features (i.e. lexical, lexical semantic, cosine similarity, basic element, tree kernel based syntactic and shallow-semantic) for each of the document sentences in order to measure its importance and relevancy to the user query. we used a local search technique to learn the weights of the features. for all our methods of generating summaries, we have shown the effects of syntactic and shallow-semantic features over the bag of words (bow) features.
probabilistic inference for machine translation. we advance the state-of-the-art for discriminatively trained machine translation systems by presenting novel probabilistic inference and search methods for synchronous grammars. by approximating the intractable space of all candidate translations produced by intersecting an ngram language model with a synchronous grammar, we are able to train and decode models incorporating millions of sparse, heterogeneous features. further, we demonstrate the power of the discriminative training paradigm by extracting structured syntactic features, and achieving increases in translation performance.
summarizing spoken and written conversations. in this paper we describe research on summarizing conversations in the meetings and emails domains. we introduce a conversation summarization system that works in multiple domains utilizing general conversational features, and compare our results with domain-dependent systems for meeting and email data. we find that by treating meetings and emails as conversations with general conversational features in common, we can achieve competitive results with state-of-the-art systems that rely on more domain-specific features.
decomposability of translation metrics for improved evaluation and efficient algorithms. bleu is the de facto standard for evaluation and development of statistical machine translation systems. we describe three real-world situations involving comparisons between different versions of the same systems where one can obtain improvements in bleu scores that are questionable or even absurd. these situations arise because bleu lacks the property of decomposability, a property which is also computationally convenient for various applications. we propose a very conservative modification to bleu and a cross between bleu and word error rate that address these issues while improving correlation with human judgments.
specialized models and ranking for coreference resolution. this paper investigates two strategies for improving coreference resolution: (1) training separate models that specialize in particular types of mentions (e.g., pronouns versus proper nouns) and (2) using a ranking loss function rather than a classification function. in addition to being conceptually simple, these modifications of the standard single-model, classification-based approach also deliver significant performance improvements. specifically, we show that on the ace corpus both strategies produce f-score gains of more than 3% across the three coreference evaluation metrics (muc, b3, and ceaf).
seed and grow: augmenting statistically generated summary sentences using schematic word patterns. we examine the problem of content selection in statistical novel sentence generation. our approach models the processes performed by professional editors when incorporating material from additional sentences to support some initially chosen key summary sentence, a process we refer to as sentence augmentation. we propose and evaluate a method called "seed and grow" for selecting such auxiliary information. additionally, we argue that this can be performed using schemata, as represented by word-pair co-occurrences, and demonstrate its use in statistical summary sentence generation. evaluation results are supportive, indicating that a schemata model significantly improves over the baseline.
an analysis of active learning strategies for sequence labeling tasks. active learning is well-suited to many problems in natural language processing, where unlabeled data may be abundant but annotation is slow and expensive. this paper aims to shed light on the best active learning approaches for sequence labeling tasks such as information extraction and document segmentation. we survey previously used query selection strategies for sequence models, and propose several novel algorithms to address their shortcomings. we also conduct a large-scale empirical comparison using multiple corpora, which demonstrates that our proposed methods advance the state of the art.
language and translation model adaptation using comparable corpora. traditionally, statistical machine translation systems have relied on parallel bi-lingual data to train a translation model. while bi-lingual parallel data are expensive to generate, monolingual data are relatively common. yet monolingual data have been under-utilized, having been used primarily for training a language model in the target language. this paper describes a novel method for utilizing monolingual target data to improve the performance of a statistical machine translation system on news stories. the method exploits the existence of comparable text---multiple texts in the target language that discuss the same or similar stories as found in the source language document. for every source document that is to be translated, a large monolingual data set in the target language is searched for documents that might be comparable to the source documents. these documents are then used to adapt the mt system to increase the probability of generating texts that resemble the comparable document. experimental results obtained by adapting both the language and translation models show substantial gains over the baseline system.
studying the history of ideas using topic models. how can the development of ideas in a scientific field be studied over time? we apply unsupervised topic modeling to the acl anthology to analyze historical trends in the field of computational linguistics from 1978 to 2006. we induce topic clusters using latent dirichlet allocation, and examine the strength of each topic over time. our methods find trends in the field including the rise of probabilistic methods starting in 1988, a steady increase in applications, and a sharp decline of research in semantics and understanding between 1978 and 2001, possibly rising again after 2001. we also introduce a model of the diversity of ideas, topic entropy, using it to show that coling is a more diverse conference than acl, but that both conferences as well as emnlp are becoming broader over time. finally, we apply jensen-shannon divergence of topic distributions to show that all three conferences are converging in the topics they cover.
learning with probabilistic features for improved pipeline models. we present a novel learning framework for pipeline models aimed at improving the communication between consecutive stages in a pipeline. our method exploits the confidence scores associated with outputs at any given stage in a pipeline in order to compute probabilistic features used at other stages downstream. we describe a simple method of integrating probabilistic features into the linear scoring functions used by state of the art machine learning algorithms. experimental evaluation on dependency parsing and named entity recognition demonstrate the superiority of our approach over the baseline pipeline models, especially when upstream stages in the pipeline exhibit low accuracy.
triplet lexicon models for statistical machine translation. this paper describes a lexical trigger model for statistical machine translation. we present various methods using triplets incorporating long-distance dependencies that can go beyond the local context of phrases or n-gram based language models. we evaluate the presented methods on two translation tasks in a reranking framework and compare it to the related ibm model 1. we show slightly improved translation quality in terms of bleu and ter and address various constraints to speed up the training based on expectation-maximization and to lower the overall number of triplets without loss in translation performance.
a dependency-based word subsequence kernel. this paper introduces a new kernel which computes similarity between two natural language sentences as the number of paths shared by their dependency trees. the paper gives a very efficient algorithm to compute it. this kernel is also an improvement over the word subsequence kernel because it only counts linguistically meaningful word subsequences which are based on word dependencies. it overcomes some of the difficulties encountered by syntactic tree kernels as well. experimental results demonstrate the advantage of this kernel over word subsequence and syntactic tree kernels.
a sensory controlled gripper system. in this paper, a gripper system, provided with tactile sensors, is described. various ways of using tactile information have been explored. the work contains two parts. one is concerned with servo control aspects, where the tactile sensors were used as force sensors, together with a position transducer, to implement position and force hybrid control. the other part deals with tactile image processing, where slip detection, object localization and object recognition are discussed and implemented.
robotics: where has it been? where is it going? the history of robotics stretches far into the past, with numerous tales of attempts to create artificial life. the start-up and rapid growth eras, from 1950 till 1985, were dominated by applications in spot welding and spray painting. the current era of disillusionment has occurred because the tasks of assembly and mobility are technologically more difficult than most people had anticipated. the future has many promising applications, from undersea to outerspace. what the future brings will depend on how much of our wealth we invest in it.
a prototype for intelligent computer integrated wood truss fabrication. this paper describes research efforts in which an expert process planning system, based on the semi-intelligent process selector (sips) expert system shell, is linked with an industrial robot arm to perform fixed plant cutting and assembly operations (wood truss fabrication). the operational protocol of this system involves providing truss design input to the process planner via a file of truss feature parameter frames. this planner, which is resident on a ti explorer workstation, generates the fabrication sequence and transfers this sequence to a robot control program which is resident on a separate microcomputer. this control program creates the required robot commands to produce robot movement. this paper provides a general discussion of knowledge-based expert systems and computer-aided process planning followed by the specifics of the development and operation of a sips based automated truss manufacturing system. this latter section includes an overview of the process planning shell utilized in this effort, and the structure of the knowledge bases developed. the makeup and function of the interpreter software package and the specifics of the major system hardware components are also discussed.
economic effects of robotization in japan. in this paper several questions related to robotics are dealt with in the context of the japanese economy. these include the economic distinction of robots and the problems that can be solved by them, as well as the explanation for the rapid spread of robotics in japan.