nils-herrmann/clean_scirepeval huggingface dataset:
Real-World Applications of Bayesian Networks - Introduction.

A seal adapted for environments in which high temperatures are experienced and low pressure drops are met is made by coiling in plural spirals about itself a metallic work-hardened tape which has, in cross-section a shape in the form of an open V, a throat-like recess having a semicircumferential outline being formed in correspondence with the apex of the V. The terminal portions of the spirals are held together by rivets or by spot-welding. A machine is also provided to shape the seal, which comprises specially shaped roller pairs arranged in sequential order, so as to effect on the metal tape all the operations which are necessary to impart the expected final shape thereto.
Decision-Theoretic Foundations for Causal Reasoning

We present a definition of cause and effect in terms of decision-theoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that this approach provides added clarity to the notion of cause. Also in this paper, we examine the encoding of causal relationships in directed acyclic graphs. We describe a special class of influence diagrams, those in canonical form, and show its relationship to Pearl's representation of cause and effect. Finally, we show how canonical form facilitates counterfactual reasoning.
Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains

We examine Bayesian methods for learning Bayesian networks from a combination of prior knowledge and statistical data. In particular, we unify the approaches we presented at last year's conference for discrete and Gaussian domains. We derive a general Bayesian scoring metric, appropriate for both domains. We then use this metric in combination with well-known statistical facts about the Dirichlet and normal--Wishart distributions to derive our metrics for discrete and Gaussian domains.
Decision-Theoretic: troubleshooting : Real-World Applications of Bayesian Networks

We have described a decision-theoretic approach for generating troubleshooting plans under uncertainty that interleaves both observations and repair actions. Our approach is based on a set of approximations to an exact method for a simple special case. Despite our approximations, we have seen that our planner produces expected troubleshooting costs that are close to optimal and significantly lower than those of simple planners
Real-world applications of Bayesian networks

As long as knowledge-based systems have been built, facilities for handling uncertainty have been an integral part. In the early days of rule-based programming, the predominant methods used variants on probability calculus to combine certainty factors associated with applicable rules. Although it was recognized that certainty factors did not conform to the well-established theory of probability, these methods were nevertheless favored because the probabilistic techniques available at the time seemed to require either specifying an intractable number of parameters, or assuming an unrealistic set of independence relationships. Today, methods based rmly in probability theory have once again begun to gain acceptance in the computer-science and uncertain-reasoning communities. The \breakthrough" was a graphical modeling language for representing uncertain relationships. Although Bayesian networks have gained widespread use in knowledge-based systems relatively recently, they are based on modeling 1
A Bayesian Approach to Learning Causal Networks

Whereas acausal Bayesian networks represent probabilistic independence, causal Bayesian networks represent causal relationships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal networks are fairly well developed. These methods often employ assumptions to facilitate the construction of priors, including the assumptions of parameter independence, parameter modularity, and likelihood equivalence. We show that although these assumptions also can be appropriate for learning causal networks, we need additional assumptions in order to learn causal networks. We introduce two sufficient assumptions, called mechanism independence and component independence. We show that these new assumptions, when combined with parameter independence, parameter modularity, and likelihood equivalence, allow us to apply methods for learning acausal networks to learn causal networks.
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.
Effective statistical models for syntactic and semantic disambiguation

This thesis focuses on building effective statistical models for disambiguation of sophisticated syntactic and semantic natural language (NL) structures. We advance the state of the art in several domains by (i) choosing representations that encode domain knowledge more effectively and (ii) developing machine learning algorithms that deal with the specific properties of NL disambiguation tasks—sparsity of training data and large, structured spaces of hidden labels. 
For the task of syntactic disambiguation, we propose a novel representation of parse trees that connects the words of the sentence with the hidden syntactic structure in a direct way. Experimental evaluation on parse selection for a Head Driven Phrase Structure Grammar shows the new representation achieves superior performance compared to previous models. For the task of disambiguating the semantic role structure of verbs, we build a more accurate model, which captures the knowledge that the semantic frame of a verb is a joint structure with strong dependencies between arguments. We achieve this using a Conditional Random Field without Markov independence assumptions on the sequence of semantic role labels. 
To address the sparsity problem in machine learning for NL, we develop a method for incorporating many additional sources of information, using Markov chains in the space of words. The Markov chain framework makes it possible to combine multiple knowledge sources, to learn how much to trust each of them, and to chain inferences together. It achieves large gains in the task of disambiguating prepositional phrase attachments.
Robust Textual Inference via Graph Matching

We present a system for deciding whether a given sentence can be inferred from text. Each sentence is represented as a directed graph (extracted from a dependency parser) in which the nodes represent words or phrases, and the links represent syntactic and semantic relationships. We develop a learned graph matching approach to approximate entailment using the amount of the sentence's semantic content which is contained in the text. We present results on the Recognizing Textual Entailment dataset (Dagan et al., 2005), and show that our approach outperforms Bag-Of-Words and TF-IDF models. In addition, we explore common sources of errors in our approach and how to remedy them.
The unsupervised learning of natural language structure

There is precisely one complete language processing system to date: the human brain. Though there is debate on how much built-in bias human learners might have, we definitely acquire language in a primarily unsupervised fashion. On the other hand, computational approaches to language processing are almost exclusively supervised, relying on hand-labeled corpora for training. This reliance is largely due to unsupervised approaches having repeatedly exhibited discouraging performance. In particular, the problem of learning syntax (grammar) from completely unannotated text has received a great deal of attention for well over a decade, with little in the way of positive results. We argue that previous methods for this task have generally underperformed because of the representations they used. Overly complex models are easily distracted by non-syntactic correlations (such as topical associations), while overly simple models aren't rich enough to capture important first-order properties of language (such as directionality, adjacency, and valence). 
In this work, we describe several syntactic representations and associated probabilistic models which are designed to capture the basic character of natural language syntax as directly as possible. First, we examine a nested, distributional method which induces bracketed tree structures. Second, we examine a dependency model which induces word-to-word dependency structures. Finally, we demonstrate that these two models perform better in combination than they do alone. With these representations, high-quality analyses can be learned from surprisingly little text, with no labeled examples, in several languages (we show experiments with English, German, and Chinese). Our results show above-baseline performance in unsupervised parsing in each of these languages. 
Grammar induction methods are useful since parsed corpora exist for only a small number of languages. More generally, most high-level NLP tasks, such as machine translation and question-answering, lack richly annotated corpora, making unsupervised methods extremely appealing even for common languages like English. Finally, while the models in this work are not intended to be cognitively plausible, their effectiveness can inform the investigation of what biases are or are not needed in the human acquisition of language.
Unsupervised Learning of Field Segmentation Models for Information Extraction

The applicability of many current information extraction techniques is severely limited by the need for supervised training data. We demonstrate that for certain field structured extraction tasks, such as classified advertisements and bibliographic citations, small amounts of prior knowledge can be used to learn effective models in a primarily unsupervised fashion. Although hidden Markov models (HMMs) provide a suitable generative model for field structured text, general unsupervised HMM learning fails to learn useful structure in either of our domains. However, one can dramatically improve the quality of the learned structure by exploiting simple prior knowledge of the desired solutions. In both domains, we found that unsupervised methods can attain accuracies with 400 unlabeled examples comparable to those attained by supervised methods on 50 labeled examples, and that semi-supervised methods can make good use of small amounts of labeled data.
Morphological features help POS tagging of unknown words across language varieties

Part-of-speech tagging, like any supervised statistical NLP task, is more difficult when test sets are very different from training sets, for example when tagging across genres or language varieties. We examined the problem of POS tagging of different varieties of Mandarin Chinese (PRC-Mainland, PRCHong Kong, and Taiwan). An analytic study first showed that unknown words were a major source of difficulty in cross-variety tagging. Unknown words in English tend to be proper nouns. By contrast, we found that Mandarin unknown words were mostly common nouns and verbs. We showed these results are caused by the high frequency of morphological compounding in Mandarin; in this sense Mandarin is more like German than English. Based on this analysis, we propose a variety of new morphological unknown-word features for POS tagging, extending earlier work by others on unknown-word tagging in English and German. Our features were implemented in a maximum entropy Markov model. Our system achieves state-of-the-art performance in Mandarin tagging, including improving unknown-word tagging performance on unseen varieties in Chinese Treebank 5.0 from 61% to 80% correct.
Exploring the boundaries: gene and protein identification in biomedical text

BackgroundGood automatic information extraction tools offer hope for automatic processing of the exploding biomedical literature, and successful named entity recognition is a key component for such tools.MethodsWe present a maximum-entropy based system incorporating a diverse set of features for identifying gene and protein names in biomedical abstracts.ResultsThis system was entered in the BioCreative comparative evaluation and achieved a precision of 0.83 and recall of 0.84 in the "open" evaluation and a precision of 0.78 and recall of 0.85 in the "closed" evaluation.ConclusionCentral contributions are rich use of features derived from the training data at multiple levels of granularity, a focus on correctly identifying entity boundaries, and the innovative use of several external knowledge sources including full MEDLINE abstracts and web searches.
Accurate Unsupervised Learning of Field Structure Models for Information Extraction

The applicability of current information extraction techniques is severely limited by the need for supervised training data. We demonstrate that for certainfield structuredextraction tasks, small amounts of prior knowledge can be used to effectively learn models in a primarily unsupervised fashion. Many text information sources exhibit a latent field structure: such documents can be viewed as dense sequences of semantically coherent fields. Examples include classified advertisements and bibliographic citations, which we investigate here. Although hidden Markov models (HMMs) provide a suitable generative model for field structured text, general unsupervised HMM learning fails to learn useful structure in either of our domains. However, we show that one can dramatically improve the quality of the learned structure by exploiting simple prior knowledge of the desired solutions. In both domains, unsupervised methods can attain accuracies comparable to simple supervised methods trained on the same data, using a combination of structural model constraints and targeted initializations.
Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling

Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference. We use this technique to augment an existing CRF-based information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints. This technique results in an error reduction of up to 9% over state-of-the-art systems on two established information extraction tasks.
Romance is so complex

In this paper I want to look at what the evidence from Complex Predicates can tell us about the design parameters of an empirically adequate theory of Universal Grammar (UG). This is a fertile field for investigation because, according to the standard assumptions of the field, complex predicates are monoclausal with respect to some properties and multiclausal with respect to others and this tension can only be resolved by giving up some cherished beliefs. After introducing the problem in Section 1, Sections 2–4 will lay out the basis of the dilemma. Sections 2 and 3 argue that Romance complex predicates have an articulated rightwardbranching phrase structure, and cannot be analyzed as some sort of verb compound or verbal complex while conversely Section 4 shows how in many respects a complex predicate does behave just like a single predicate. Hence we require a notion of monoclausality that these complex predicates satisfy despite their articulated phrase structure. Section 5 then draws out the implications of this result for theories that have monostratal syntactic levels (in the sense of Ladusaw 1988) such as LFG, HPSG or Categorial Grammar. While many of the same concerns occur in a multistratal theory such as GB or RG (and have long been debated in those frameworks), there is more room for movement in such a theory, because complex predicates can be multiclausal on one stratum but monoclausal on another stratum. In a monostratal theory, the problem is more cleanly cut – on each level, one must regard a complex predicate as either one clause or as embedded clauses – and if a
Stochastic HPSG Parse Disambiguation using the Redwoods Corpus

This article details our experiments on HPSG parse disambiguation, based on the Redwoods treebank. Using existing and novel stochastic models, we evaluate the usefulness of different information sources for disambiguation – lexical, syntactic, and semantic. We perform careful comparisons of generative and discriminative models using equivalent features and show the consistent advantage of discriminatively trained models. Our best system performs at over 76% sentence exact match accuracy.
A Joint Model for Semantic Role Labeling

We present a semantic role labeling system submitted to the closed track of the CoNLL-2005 shared task. The system, introduced in (Toutanova et al., 2005), implements a joint model that captures dependencies among arguments of a predicate using log-linear models in a discriminative re-ranking framework. We also describe experiments aimed at increasing the robustness of the system in the presence of syntactic parse errors. Our final system achieves F1-Measures of 76.68 and 78.45 on the development and the WSJ portion of the test set, respectively.
Robust Textual Inference using Diverse Knowledge Sources

We present a machine learning approach to robust textual inference, in which parses of the text and the hypothesis sentences are used to measure their asymmetric “similarity”, and thereby to decide if the hypothesis can be inferred. This idea is realized in two different ways. In the first, each sentence is represented as a graph (extracted from a dependency parser) in which the nodes are words/phrases, and the links represent dependencies. A learned, asymmetric, graph-matching cost is then computed to measure the similarity between the text and the hypothesis. In the second approach, the text and the hypothesis are parsed into the logical formula-like representation used by (Harabagiu et al., 2000). An abductive theorem prover (using learned costs for making different types of assumptions in the proof) is then applied to try to infer the hypothesis from the text, and the total “cost” of proving the hypothesis is used to decide if the hypothesis is entailed.
Template Sampling for Leveraging Domain Knowledge in Information Extraction

We initially describe a feature-rich discriminative Conditional Random Field ( CRF) model for Information Extraction in the workshop announcements domain, which offers good baseline performance in the PASCAL shared task. We then propose a method for leveraging domain knowledge in Information Extraction tasks, scoring candidate document labellings as one-value-per-field templates according to domain feasibility after generating sample labellings from a trained sequence classifier. Our relational models evaluate these templates according to our intuitions about agreement in the domain: workshop acronyms should resemble their names, workshop dates occur after paper submission dates. These methods see a 5% f-score improvement in fields retrieved when sampling labellings from a Maximum-Entropy Markov Model, however we do not observe improvement over a CRF model. We discuss reasons for this, including the problem of recovering all field instances from a best template, and propose future work in adapting such a model to the CRF, a better standalone system.
A system for identifying named entities in biomedical text: how results from two evaluations reflect on both the system and the evaluations: Conference Papers

We present a maximum entropy-based system for identifying named entities (NEs) in biomedical abstracts and present its performance in the only two biomedical named entity recognition (NER) comparative evaluations that have been held to date, namely BioCreative and Coling BioNLP. Our system obtained an exact match F-score of 83.2p in the BioCreative evaluation and 70.1p in the BioNLP evaluation. We discuss our system in detail, including its rich use of local features, attention to correct boundary identification, innovative use of external knowledge resources, including parsing and web searches, and rapid adaptation to new NE sets. We also discuss in depth problems with data annotation in the evaluations which caused the final performance to be lower than optimal.
Using Feature Conjunctions across Examples for Learning Pairwise Classifiers

We propose a kernel method for using combinations of features across example pairs in learning pairwise classifiers. Pairwise classifiers, which identify whether two examples belong to the same class or not, are important components in duplicate detection, entity matching, and other clustering applications. Existing methods for learning pairwise classifiers from labeled training data are based on string edit distance or common features between two examples. However, if two examples from the same class have few common features, these methods have difficulties in finding these pairs and achieving high recall. One typical example is to check whether two abbreviated author names in different citations refer to the same person or not. Since similarities between examples from the same class become close to zero, classifiers fail to distinguish positive pairs from negative pairs. One approach to avoiding the problem of zero similarities is using conjunctions of different features across examples, but implementing this idea straightforwardly makes the computational cost prohibitive for practical problems. Using a kernel on pair instances, our method can use feature conjunctions across examples without actually doing feature mappings, which are computationally expensive. The kernel is a tensor product of two inner products on the original feature space. The corresponding feature mapping generates conjunctions of features only across the two different examples while that of the conventional polynomial kernel also generates conjunctions of features from the same example, which are irrelevant to pairwise classification and cause deterioration of accuracy. Our experiments on the author matching problem show that this method can give a precision 4 to 8 times higher than that of previous methods at medium recall levels.