waashk/dblp huggingface dataset:
arabic ocr error correction using character segment correction, language modeling, and shallow morphology. this paper explores the use of a character segment based character correction model, language modeling, and shallow morphology for arabic ocr error correction. experimentation shows that character segment based correction is superior to single character correction and that language modeling boosts correction, by improving the ranking of candidate corrections, while shallow morphology had a small adverse effect. further, given sufficiently large corpus to extract a dictionary and to train a language model, word based correction works well for a morphologically rich language such as arabic.
a weakly supervised learning approach for spoken language understanding. in this paper, we present a weakly supervised learning approach for spoken language understanding in domain-specific dialogue systems. we model the task of spoken language understanding as a successive classification problem. the first classifier (topic classifier) is used to identify the topic of an input utterance. with the restriction of the recognized target topic, the second classifier (semantic classifier) is trained to extract the corresponding slot-value pairs. it is mainly data-driven and requires only minimally annotated corpus for training whilst retaining the understanding robustness and deepness for spoken language. most importantly, it allows the employment of weakly supervised strategies for training the two classifiers. we first apply the training strategy of combining active learning and self-training (tur et al., 2005) for topic classifier. also, we propose a practical method for bootstrapping the topic-dependent semantic classifiers from a small amount of labeled sentences. experiments have been conducted in the context of chinese public transportation information inquiry domain. the experimental results demonstrate the effectiveness of our proposed slu framework and show the possibility to reduce human labeling efforts significantly.
empirical study on the performance stability of named entity recognition model across domains. when a machine learning-based named entity recognition system is employed in a new domain, its performance usually degrades. in this paper, we provide an empirical study on the impact of training data size and domain information on the performance stability of named entity recognition models. we present an informative sample selection method for building high quality and stable named entity recognition models across domains. experimental results show that the performance of the named entity recognition model is enhanced significantly after being trained with these informative samples.
a hybrid markov/semi-markov conditional random field for sequence segmentation. markov order-1 conditional random fields (crfs) and semi-markov crfs are two popular models for sequence segmentation and labeling. both models have advantages in terms of the type of features they most naturally represent. we propose a hybrid model that is capable of representing both types of features, and describe efficient algorithms for its training and inference. we demonstrate that our hybrid model achieves error reductions of 18% and 25% over a standard order-1 crf and a semi-markov crf (resp.) on the task of chinese word segmentation. we also propose the use of a powerful feature for the semi-markov crf: the log conditional odds that a given token sequence constitutes a chunk according to a generative model, which reduces error by an additional 13%. our best system achieves 96.8% f-measure, the highest reported score on this test set.
entity annotation based on inverse index operations. entity annotation involves attaching a label such as 'name' or 'organization' to a sequence of tokens in a document. all the current rule-based and machine learning-based approaches for this task operate at the document level. we present a new and generic approach to entity annotation which uses the inverse index typically created for rapid key-word based searching of a document collection. we define a set of operations on the inverse index that allows us to create annotations defined by cascading regular expressions. the entity annotations for an entire document corpus can be created purely of the index with no need to access the original documents. experiments on two publicly available data sets show very significant performance improvements over the document-based annotators.
feature subsumption for opinion analysis. lexical features are key to many approaches to sentiment analysis and opinion detection. a variety of representations have been used, including single words, multi-word ngrams, phrases, and lexico-syntactic patterns. in this paper, we use a subsumption hierarchy to formally define different types of lexical features and their relationship to one another, both in terms of representational coverage and performance. we use the subsumption hierarchy in two ways: (1) as an analytic tool to automatically identify complex features that outperform simpler features, and (2) to reduce a feature set by removing unnecessary features. we show that reducing the feature set improves performance on three opinion classification tasks, especially when combined with traditional feature selection.
relevance feedback models for recommendation. we extended language modeling approaches in information retrieval (ir) to combine collaborative filtering (cf) and content-based filtering (cbf). our approach is based on the analogy between ir and cf, especially between cf and relevance feedback (rf). both cf and rf exploit users' preference/relevance judgments to recommend items. we first introduce a multinomial model that combines cf and cbf in a language modeling framework. we then generalize the model to another multinomial model that approximates the polya distribution. this generalized model outperforms the multinomial model by 3.4% for cbf and 17.4% for cf in recommending english wikipedia articles. the performance of the generalized model for three different datasets was comparable to that of a state-of-the-art item-based cf method.
sentiment retrieval using generative models. ranking documents or sentences according to both topic and sentiment relevance should serve a critical function in helping users when topics and sentiment polarities of the targeted text are not explicitly given, as is often the case on the web. in this paper, we propose several sentiment information retrieval models in the framework of probabilistic language models, assuming that a user both inputs query terms expressing a certain topic and also specifies a sentiment polarity of interest in some manner. we combine sentiment relevance models and topic relevance models with model parameters estimated from training data, considering the topic dependence of the sentiment. our experiments prove that our models are effective.
unsupervised relation disambiguation with order identification capabilities. we present an unsupervised learning approach to disambiguate various relations between name entities by use of various lexical and syntactic features from the contexts. it works by calculating eigen-vectors of an adjacency graph's laplacian to recover a submanifold of data from a high dimensionality space and then performing cluster number estimation on the eigenvectors. this method can address two difficulties encoutered in hasegawa et al. (2004)'s hierarchical clustering: no consideration of manifold structure in data, and requirement to provide cluster number by users. experiment results on ace corpora show that this spectral clustering based approach outperforms hasegawa et al. (2004)'s hierarchical clustering method and a plain k-means clustering method.
semantic role labeling via instance-based learning. this paper demonstrates two methods to improve the performance of instance-based learning (ibl) algorithms for the problem of semantic role labeling (srl). two ibl algorithms are utilized: k-nearest neighbor (knn), and priority maximum likelihood (pml) with a modified back-off combination method. the experimental data are the wsj23 and brown corpus test sets from the conll-2005 shared task. it is shown that applying the tree-based predicate-argument recognition algorithm (para) to the data as a preprocessing stage allows knn and pml to deliver f1: 68.61 and 71.02 respectively on the wsj23, and f1: 56.96 and 60.55 on the brown corpus; an increase of 8.28 in f1 measurement over the most recent published pml results for this problem (palmer et al., 2005). training times for ibl algorithms are very much faster than for other widely used techniques for srl (e.g. parsing, support vector machines, perceptrons, etc); and the feature reduction effects of para yield testing and processing speeds of around 1.0 second per sentence for knn and 0.9 second per sentence for pml respectively, suggesting that ibl could be a more practical way to perform srl for nlp applications where it is employed; such as realtime machine translation or automatic speech recognition.
quality assessment of large scale knowledge resources. this paper presents an empirical evaluation of the quality of publicly available large-scale knowledge resources. the study includes a wide range of manually and automatically derived large-scale knowledge resources. in order to establish a fair and neutral comparison, the quality of each knowledge resource is indirectly evaluated using the same method on a word sense disambiguation task. the evaluation framework selected has been the senseval-3 english lexical sample task. the study empirically demonstrates that automatically acquired knowledge resources surpass both in terms of precision and recall the knowledge resources derived manually, and that the combination of the knowledge contained in these resources is very close to the most frequent sense classifier. as far as we know, this is the first time that such a quality assessment has been performed showing a clear picture of the current state-of-the-art of publicly available wide coverage semantic resources.
phrasetable smoothing for statistical machine translation. we discuss different strategies for smoothing the phrasetable in statistical mt, and give results over a range of translation settings. we show that any type of smoothing is a better idea than the relative-frequency estimates that are often used. the best smoothing techniques yield consistent gains of approximately 1% (absolute) according to the bleu metric.
humor: prosody analysis and automatic recognition for f*r*i*e*n*d*s*. we analyze humorous spoken conversations from a classic comedy television show, friends, by examining acoustic-prosodic and linguistic features and their utility in automatic humor recognition. using a simple annotation scheme, we automatically label speaker turns in our corpus that are followed by laughs as humorous and the rest as non-humorous. our humor-prosody analysis reveals significant differences in prosodic characteristics (such as pitch, tempo, energy etc.) of humorous and non-humorous speech, even when accounted for the gender and speaker differences. humor recognition was carried out using standard supervised learning classifiers, and shows promising results significantly above the baseline.
partially supervised coreference resolution for opinion summarization through structured rule learning. combining fine-grained opinion information to produce opinion summaries is important for sentiment analysis applications. toward that end, we tackle the problem of source coreference resolution -- linking together source mentions that refer to the same entity. the partially supervised nature of the problem leads us to define and approach it as the novel problem of partially supervised clustering. we propose and evaluate a new algorithm for the task of source coreference resolution that outperforms competitive baselines.
extremely lexicalized models for accurate and fast hpsg parsing. this paper describes an extremely lexicalized probabilistic model for fast and accurate hpsg parsing. in this model, the probabilities of parse trees are defined with only the probabilities of selecting lexical entries. the proposed model is very simple, and experiments revealed that the implemented parser runs around four times faster than the previous model and that the proposed model has a high accuracy comparable to that of the previous model for probabilistic hpsg, which is defined over phrase structures. we also developed a hybrid of our probabilistic model and the conventional phrase-structure-based model. the hybrid model is not only significantly faster but also significantly more accurate by two points of precision and recall compared to the previous model.
solving the problem of cascading errors: approximate bayesian inference for linguistic annotation pipelines. the end-to-end performance of natural language processing systems for compound tasks, such as question answering and textual entailment, is often hampered by use of a greedy 1-best pipeline architecture, which causes errors to propagate and compound at each stage. we present a novel architecture, which models these pipelines as bayesian networks, with each low level task corresponding to a variable in the network, and then we perform approximate inference to find the best labeling. our approach is extremely simple to apply but gains the benefits of sampling the entire distribution over labels at each stage in the pipeline. we apply our method to two tasks -- semantic role labeling and recognizing textual entailment -- and achieve useful performance gains from the superior pipeline architecture.
unsupervised information extraction approach using graph mutual reinforcement. information extraction (ie) is the task of extracting knowledge from unstructured text. we present a novel unsupervised approach for information extraction based on graph mutual reinforcement. the proposed approach does not require any seed patterns or examples. instead, it depends on redundancy in large data sets and graph based mutual reinforcement to induce generalized "extraction patterns". the proposed approach has been used to acquire extraction patterns for the ace (automatic content extraction) relation detection and characterization (rdc) task. ace rdc is considered a hard task in information extraction due to the absence of large amounts of training data and inconsistencies in the available data. the proposed approach achieves superior performance which could be compared to supervised techniques with reasonable training data.
joint extraction of entities and relations for opinion recognition. we present an approach for the joint extraction of entities and relations in the context of opinion recognition and analysis. we identify two types of opinion-related entities --- expressions of opinions and sources of opinions --- along with the linking relation that exists between them. inspired by roth and yih (2004), we employ an integer linear programming approach to solve the joint opinion recognition task, and show that global, constraint-based inference can significantly boost the performance of both relation extraction and the extraction of opinion-related entities. performance further improves when a semantic role labeling system is incorporated. the resulting system achieves f-measures of 79 and 69 for entity and relation extraction, respectively, improving substantially over prior results in the area.
discriminative methods for transliteration. we present two discriminative methods for name transliteration. the methods correspond to local and global modeling approaches in modeling structured output spaces. both methods do not require alignment of names in different languages -- their features are computed directly from the names themselves. we perform an experimental evaluation of the methods for name transliteration from three languages (arabic, korean, and russian) into english, and compare the methods experimentally to a state-of-the-art joint probabilistic modeling approach. we find that the discriminative methods outperform probabilistic modeling, with the global discriminative modeling approach achieving the best performance in all languages.
automatic classification of citation function. citation function is defined as the author's reason for citing a given paper (e.g. acknowledgement of the use of the cited method). the automatic recognition of the rhetorical function of citations in scientific text has many applications, from improvement of impact factor calculations to text summarisation and more informative citation indexers. we show that our annotation scheme for citation function is reliable, and present a supervised machine learning framework to automatically classify citation function, using both shallow and linguistically-inspired features. we find, amongst other things, a strong relationship between citation function and sentiment classification.
detecting parser errors using web-based semantic filters. nlp systems for tasks such as question answering and information extraction typically rely on statistical parsers. but the efficacy of such parsers can be surprisingly low, particularly for sentences drawn from heterogeneous corpora such as the web. we have observed that incorrect parses often result in wildly implausible semantic interpretations of sentences, which can be detected automatically using semantic information obtained from the web. based on this observation, we introduce web-based semantic filtering---a novel, domain-independent method for automatically detecting and discarding incorrect parses. we measure the effectiveness of our filtering system, called woodward, on two test collections. on a set of trec questions, it reduces error by 67%. on a set of more complex penn treebank sentences, the reduction in error rate was 20%.
bestcut: a graph algorithm for coreference resolution. in this paper we describe a coreference resolution method that employs a classification and a clusterization phase. in a novel way, the clusterization is produced as a graph cutting algorithm, in which nodes of the graph correspond to the mentions of the text, whereas the edges of the graph constitute the confidences derived from the coreference classification. in experiments, the graph cutting algorithm for coreference resolution, called bestcut, achieves state-of-the-art performance.
random indexing using statistical weight functions. random indexing is a vector space technique that provides an efficient and scalable approximation to distributional similarity problems. we present experiments showing random indexing to be poor at handling large volumes of data and evaluate the use of weighting functions for improving the performance of random indexing. we find that random index is robust for small data sets, but performance degrades because of the influence high frequency attributes in large data sets. the use of appropriate weight functions improves this significantly.
style & topic language model adaptation using hmm-lda. adapting language models across styles and topics, such as for lecture transcription, involves combining generic style models with topic-specific content relevant to the target document. in this work, we investigate the use of the hidden markov model with latent dirichlet allocation (hmm-lda) to obtain syntactic state and semantic topic assignments to word instances in the training corpus. from these context-dependent labels, we construct style and topic models that better model the target document, and extend the traditional bag-of-words topic models to n-grams. experiments with static model interpolation yielded a perplexity and relative word error rate (wer) reduction of 7.1% and 2.1%, respectively, over an adapted trigram baseline. adaptive interpolation of mixture components further reduced perplexity by 9.5% and wer by a modest 0.3%.
statistical machine reordering. reordering is currently one of the most important problems in statistical machine translation systems. this paper presents a novel strategy for dealing with it: statistical machine reordering (smr). it consists in using the powerful techniques developed for statistical machine translation (smt) to translate the source language (s) into a reordered source language (s'), which allows for an improved translation into the target language (t). the smt task changes from s2t to s'2t which leads to a monotonized word alignment and shorter translation units. in addition, the use of classes in smr helps to infer new word reorderings. experiments are reported in the esen wmt06 tasks and the zhen iwslt05 task and show significant improvement in translation quality.
context-dependent term relations for information retrieval. co-occurrence analysis has been used to determine related words or terms in many nlp-related applications such as query expansion in information retrieval (ir). however, related words are usually determined with respect to a single word, without relevant information for its application context. for example, the word "programming" may be considered to be strongly related to "java", and applied inappropriately to expand a query on "java travel". to solve this problem, we propose to add another context word in the relation to specify the appropriate context of the relation, leading to term relations of the form "(java, travel) &rarr; indonesia". the extracted relations are used for query expansion in ir. our experiments on several trec collections show that this new type of context-dependent relations performs much better than the traditional co-occurrence relations.
two graph-based algorithms for state-of-the-art wsd. this paper explores the use of two graph algorithms for unsupervised induction and tagging of nominal word senses based on corpora. our main contribution is the optimization of the free parameters of those algorithms and its evaluation against publicly available gold standards. we present a thorough evaluation comprising supervised and unsupervised modes, and both lexical-sample and all-words tasks. the results show that, in spite of the information loss inherent to mapping the induced senses to the gold-standard, the optimization of parameters based on a small sample of nouns carries over to all nouns, performing close to supervised systems in the lexical sample task and yielding the second-best wsd systems for the senseval-3 all-words task.
corrective models for speech recognition of inflected languages. this paper presents a corrective model for speech recognition of inflected languages. the model, based on a discriminative framework, incorporates word n-grams features as well as factored morphological features, providing error reduction over the model based solely on word n-gram features. experiments on a large vocabulary task, namely the czech portion of the malach corpus, demonstrate performance gain of about 1.1--1.5% absolute in word error rate, wherein morphological features contribute about a third of the improvement. a simple feature selection mechanism based on x2 statistics is shown to be effective in reducing the number of features by about 70% without any loss in performance, making it feasible to explore yet larger feature spaces.
re-evaluating machine translation results with paraphrase support. in this paper, we present paraeval, an automatic evaluation framework that uses paraphrases to improve the quality of machine translation evaluations. previous work has focused on fixed n-gram evaluation metrics coupled with lexical identity matching. paraeval addresses three important issues: support for paraphrase/synonym matching, recall measurement, and correlation with human judgments. we show that paraeval correlates significantly better than bleu with human assessment in measurements for both fluency and adequacy.