nils-herrmann/clean_scirepeval huggingface dataset:
Explaining away ambiguity: Learning verb selectional preference with Bayesian networks

This paper presents a Bayesian model for unsupervised learning of verb selectional preferences. For each verb the model creates a Bayesian network whose architecture is determined by the lexical hicrarchy of Wordnet and whose parameters are estimated from a list of verb-object pairs found from a corpus. "Explaining away", a well-known property of Bayesian networks, helps the model deal in a natural fashion with word sense ambiguity in the training data. On a word sense disambiguation test our model performed better than other state of the art systems for unsupervised learning of selectional preferences. Computational complexity problems, ways of improving this approach and methods for implementing "explaining away" in other graphical frameworks are discussed.
Estimators for Stochastic \ Uni cation-Based " Grammars

Log-linear models provide a statistically sound framework for Stochastic \Uniication-Based" Grammars (SUBGs) and stochastic versions of other kinds of grammars. We describe two computationally-tractable ways of estimating the parameters of such grammars from a training corpus of syntactic analyses, and apply these to estimate a stochastic version of Lexical-
Efficient probabilistic top-down and left-corner parsing

This paper examines efficient predictive broad, coverage parsing without dynamic programming. In contrast to bottom-up methods, depth-first top-down parsing produces partial parses that are fully connected trees spanning the entire left context, from which any kind of non-local dependency or partial semantic interpretation can in principle be read. We contrast two predictive parsing approaches, top-down and left-corner parsing, and find both to be viable. In addition, we find that enhancement with non-local information not only improves parser accuracy, but also substantially improves the search efficiency.
Data express gigabit junction with the next-generation Internet

Important as a very high-performance backbone is to the next generation of the Internet, it is no less important that the points at which people connect to the backbone-the so-called points of presence, or POPs-provide an equivalent level of performance. For almost three years, members of the academic, research, and business community in the North Carolina region known as the Research Triangle-the area lying amid the cities of Raleigh, Durham, and Chapel Hill-have been working to create a high-performance regional network. As partners in the North Carolina Network Initiative (NCNI), they have built a network that can serve as a POP handling the multigigabit traffic to be delivered by the next-generation Internet. Thence comes its name: a GigaPOP. Like a great railway junction, the GigaPOP is a central distribution point where huge amounts of digital freight are moved between various end points and the main line (such as the very high-performance Backbone Network Services, or VBNS). The NCNI is one of a handful of regional networking organizations around the country. The others include the California Research and Education Network 2 (Calren2), the Metropolitan REN (MREN) serving the Chicago area, the Houston Area Computational Science Consortium (HACSC), and the New York State Educational and Research Network (Nysernet). NCNI's work, which predates most initiatives for the Internet's second generation, serves as a model of how various regions can latch onto the coming information superhighway to good effect.
A Resource Sensitive Interpretation of Lexical Functional Grammar

This paper investigates whether the fundamental linguistic insights and intuitions of Lexical Functional Grammar (LFG), which is usually presented as a “constraint-based” linguistic theory, can be reformulated in a “resource sensitive” framework using a substructural modal logic. In the approach investigated here, LFG's f-descriptions are replaced with expressions from a multi-modal propositional logic (with permutation and possibly limited contraction). In effect, the feature structure “unification” basis of LFG's f-structures is replaced with a very different resource based mechanism. It turns out that some linguistic analyses that required non-monotonic devices in LFG (such as the “constraint equations” in the Andrews (1982) analysis of Icelandic) can be straightforwardly expressed in the framework presented here. Moreover, a Curry–Howard correspondence between proofs in this logic and λ-terms provides a semantic interpretation as a by-product of the process of showing syntactic well-formedness.
The effect of alternative tree representations on tree bank grammars

The performance of PCFGs estimated from tree banks is shown to be sensitive to the particular way in which linguistic constructions are represented as trees in the tree bank. This paper presents a theoretical analysis of the effect of different tree representations for PP attachment on PCFG models, and introduces a new methodology for empirically examining such effects using tree transformations. It shows that one transformation, which copies the label of a parent node onto the labels of its children, can improve the performance of a PCFG model in terms of labelled precision and recall on held out data from 73% (precision) and 69% (recall) to 80% and 79% respectively. It also points out that if only maximum likelihood parses are of interest then many productions can be ignored, since they are subsumed by combinations of other productions in the grammar. In the Penn II tree bank grammar, almost 9% of productions are subsumed in this way.
PCFG Models of Linguistic Tree Representations

The kinds of tree representations used in a treebank corpus can have a dramatic effect on performance of a parser based on the PCFG estimated from that corpus, causing the estimated likelihood of a tree to differ substantially from its frequency in the training corpus. This paper points out that the Penn II treebank representations are of the kind predicted to have such an effect, and describes a simple node relabeling transformation that improves a treebank PCFG-based parser's average precision and recall by around 8%, or approximately half of the performance difference between a simple PCFG model and the best broad-coverage parsers available today. This performance variation comes about because any PCFG, and hence the corpus of trees from which the PCFG is induced, embodies independence assumptions about the distribution of words and phrases. The particular independence assumptions implicit in a tree representation can be studied theoretically and investigated empirically by means of a tree transformation / detransformation process.
Edge-Based Best-First Chart Parsing

Natural language grammars are often very large and full of ambiguities, making standard computer parsers too slow to be practical for many tasks. Best-first parsing attempts to address this problem by preferentially working to expand subparses that are judged ``good'''' by some probabilistic figure of merit. We explain the standard non-probabilistic and best-first chart parsing paradigms, then describe a new method of best-first parsing which improves upon previous work by ranking subparses at a more fine-grained level, speeding up parsing by approximately a factor of 20 over the best previous results. Moreover, these results are achieved with a higher level of accuracy than is obtained by parsing to exhaustion.
Type-driven semantic interpretation and feature dependencies in R-LFG

Once one has enriched LFG's formal machinery with the linear logic mechanisms needed for semantic interpretation as proposed by Dalrymple et. al., it is natural to ask whether these make any existing components of LFG redundant. As Dalrymple and her colleagues note, LFG's f-structure completeness and coherence constraints fall out as a by-product of the linear logic machinery they propose for semantic interpretation, thus making those f-structure mechanisms redundant. Given that linear logic machinery or something like it is independently needed for semantic interpretation, it seems reasonable to explore the extent to which it is capable of handling feature structure constraints as well. 
R-LFG represents the extreme position that all linguistically required feature structure dependencies can be captured by the resource-accounting machinery of a linear or similiar logic independently needed for semantic interpretation, making LFG's unification machinery redundant. The goal is to show that LFG linguistic analyses can be expressed as clearly and perspicuously using the smaller set of mechanisms of R-LFG as they can using the much larger set of unification-based mechanisms in LFG: if this is the case then we will have shown that positing these extra f-structure mechanisms is not linguistically warranted.
Memoization of Coroutined Constraints

Some linguistic constraints cannot be effectively resolved during parsing at the location in which they are most naturally introduced. This paper shows how constraints can be propagated in a memoizing parser (such as a chart parser) in much the same way that variable bindings are, providing a general treatment of constraint coroutining in memoization. Prolog code for a simple application of our technique to Bouma and van Noord's (1994) categorial grammar analysis of Dutch is provided.
Europe prepares for WRC-95: mobile satellite service issues

European preparations for World Radiocommunication Conferences are coordinated within the European regional posts and telecommunications organization known as the CEPT. The basic objective of the European proposals is to achieve an even playing field for the introduction of competitive mobile satellite services that can provide global services from the earliest feasible common date. Europe believes the additional allocations made at the 1992 conference are sufficient for the foreseeable future, especially now that there is an opportunity every two years to make additional allocations should the need be proven. What must be agreed upon, however, are the allocations for the feeder links to the mobile satellite service to complement the 1992 allocations for service links. Europe also need to consider the technical constraints that were placed on the use of these service links at the 1992 conference. Of particular concern to Europe, is the most unsatisfactory conclusion in 1992 on the date of availability of the 2 GHz mobile satellite service allocations. The article describes the European proposals on these mobile satellite issues.
Kernels for gene regulatory regions

We describe a hierarchy of motif-based kernels for multiple alignments of biological sequences, particularly suitable to process regulatory regions of genes. The kernels incorporate progressively more information, with the most complex kernel accounting for a multiple alignment of orthologous regions, the phylogenetic tree relating the species, and the prior knowledge that relevant sequence patterns occur in conserved motif blocks. These kernels can be used in the presence of a library of known transcription factor binding sites, or de novo by iterating over all k-mers of a given length. In the latter mode, a discriminative classifier built from such a kernel not only recognizes a given class of promoter regions, but as a side effect simultaneously identifies a collection of relevant, discriminative sequence motifs. We demonstrate the utility of the motif-based multiple alignment kernels by using a collection of aligned promoter regions from five yeast species to recognize classes of cell-cycle regulated genes.
An Optimization Framework for the Adaptive Design of Robust Choice Questionnaires

We propose a general framework for adaptively designing choice-based conjoint questionnaires at the individual level. This framework uses complexity control to improve the robustness of the conjoint designs to response error and links the informativeness of conjoint questions to the Hessian of the loss function minimized in partworth estimation. It formalizes and generalizes several methods recently proposed both for questionnaire design and estimation. Simulations as well as an online experiment suggest that it outperforms established benchmarks, especially when response error is high.
Consistency of one-class SVM and related algorithms

We determine the asymptotic limit of the function computed by support vector machines (SVM) and related algorithms that minimize a regularized empirical convex loss function in the reproducing kernel Hilbert space of the Gaussian RBF kernel, in the situation where the number of examples tends to infinity, the bandwidth of the Gaussian kernel tends to 0, and the regularization parameter is held fixed. Non-asymptotic convergence bounds to this limit in the L2 sense are provided, together with upper bounds on the classification error that is shown to converge to the Bayes risk, therefore proving the Bayes-consistency of a variety of methods although the regularization term does not vanish. These results are particularly relevant to the one-class SVM, for which the regularization can not vanish by construction, and which is shown for the first time to be a consistent density level set estimator.
Graph Kernels for Molecular Structure-Activity Relationship Analysis with Support Vector Machines

The support vector machine algorithm together with graph kernel functions has recently been introduced to model structure-activity relationships (SAR) of molecules from their 2D structure, without the need for explicit molecular descriptor computation. We propose two extensions to this approach with the double goal to reduce the computational burden associated with the model and to enhance its predictive accuracy: description of the molecules by a Morgan index process and definition of a second-order Markov model for random walks on 2D structures. Experiments on two mutagenicity data sets validate the proposed extensions, making this approach a possible complementary alternative to other modeling strategies.
Kernel methods in genomics and computational biology

Support vector machines and kernel methods are increasingly popular in genomics and computational biology, due to their good performance in real-world applications and strong modularity that makes them suitable to a wide range of problems, from the classification of tumors to the automatic annotation of proteins. Their ability to work in high dimension, to process non-vectorial data, and the natural framework they provide to integrate heterogeneous data are particularly relevant to various problems arising in computational biology. In this chapter we survey some of the most prominent applications published so far, highlighting the particular developments in kernel methods triggered by problems in biology, and mention a few promising research directions likely to expand in the future.
A novel representation of protein sequences for prediction of subcellular location using support vector machines

As the number of complete genomes rapidly increases, accurate methods to automatically predict the subcellular location of proteins are increasingly useful to help their functional annotation. In order to improve the predictive accuracy of the many prediction methods developed to date, a novel representation of protein sequences is proposed. This representation involves local compositions of amino acids and twin amino acids, and local frequencies of distance between successive (basic, hydrophobic, and other) amino acids. For calculating the local features, each sequence is split into three parts: N‐terminal, middle, and C‐terminal. The N‐terminal part is further divided into four regions to consider ambiguity in the length and position of signal sequences. We tested this representation with support vector machines on two data sets extracted from the SWISS‐PROT database. Through fivefold cross‐validation tests, overall accuracies of more than 87% and 91% were obtained for eukaryotic and prokaryotic proteins, respectively. It is concluded that considering the respective features in the N‐terminal, middle, and C‐terminal parts is helpful to predict the subcellular location.
Supervised enzyme network inference from the integration of genomic data and chemical information

The metabolic network is an important biological network which relates enzyme proteins and chemical compounds. A large number of metabolic pathways remain unknown nowadays, and many enzymes are missing even in known metabolic pathways. There is, therefore, an incentive to develop methods to reconstruct the unknown parts of the metabolic network and to identify genes coding for missing enzymes.



This paper presents new methods to infer enzyme networks from the integration of multiple genomic data and chemical information, in the framework of supervised graph inference. The originality of the methods is the introduction of chemical compatibility as a constraint for refining the network predicted by the network inference engine. The chemical compatibility between two enzymes is obtained automatically from the information encoded by their Enzyme Commission (EC) numbers. The proposed methods are tested and compared on their ability to infer the enzyme network of the yeast Saccharomyces cerevisiae from four datasets for enzymes with assigned EC numbers: gene expression data, protein localization data, phylogenetic profiles and chemical compatibility information. It is shown that the prediction accuracy of the network reconstruction consistently improves owing to the introduction of chemical constraints, the use of a supervised approach and the weighted integration of multiple datasets. Finally, we conduct a comprehensive prediction of a global enzyme network consisting of all enzyme candidate proteins of the yeast to obtain new biological findings.


AVAILABILITY
Softwares are available upon request.
Semigroup Kernels on Measures

We present a family of positive definite kernels on measures, characterized by the fact that the value of the kernel between two measures is a function of their sum. These kernels can be used to derive kernels on structured objects, such as images and texts, by representing these objects as sets of components, such as pixels or words, or more generally as measures on the space of components. Several kernels studied in this work make use of common quantities defined on measures such as entropy or generalized variance to detect similarities. Given an a priori kernel on the space of components itself, the approach is further extended by restating the previous results in a more efficient and flexible framework using the "kernel trick". Finally, a constructive approach to such positive definite kernels through an integral representation theorem is proved, before presenting experimental results on a benchmark experiment of handwritten digits classification to illustrate the validity of the approach.
Kernel Methods in Genomics and Computational Biology

Support vector machines and kernel methods are increasingly popular in genomics and computational biology, due to their good performance in real-world applications and strong modularity that makes them suitable to a wide range of problems, from the classification of tumors to the automatic annotation of proteins. Their ability to work in high dimension, to process non-vectorial data, and the natural framework they provide to integrate heterogeneous data are particularly relevant to various problems arising in computational biology. In this chapter we survey some of the most prominent applications published so far, highlighting the particular developments in kernel methods triggered by problems in biology, and mention a few promising research directions likely to expand in the future.
1 A primer on kernel methods

Kernel methods in general, and support vector machines (SVMs) in particular, are increasingly used to solve various problems in computational biology. They offer versatile tools to process, analyze, and compare many types of data, and offer stateof-the-art performance in many cases. This self-contained introduction to positive definite kernels and kernel methods aims at providing the very basic knowledge and intuition that the reader might find useful in order to fully grasp the technical content of this book.
Protein homology detection using string alignment kernels

Remote homology detection between protein sequences is a central problem in computational biology. Discriminative methods involving support vector machines (SVMs) are currently the most effective methods for the problem of superfamily recognition in the Structural Classification Of Proteins (SCOP) database. The performance of SVMs depends critically on the kernel function used to quantify the similarity between sequences.



We propose new kernels for strings adapted to biological sequences, which we call local alignment kernels. These kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences. When tested in combination with SVM on their ability to recognize SCOP superfamilies on a benchmark dataset, the new kernels outperform state-of-the-art methods for remote homology detection.


AVAILABILITY
Software and data available upon request.
Semigroup Kernels on Finite Sets

Complex objects can often be conveniently represented by finite sets of simpler components, such as images by sets of patches or texts by bags of words. We study the class of positive definite (p.d.) kernels for two such objects that can be expressed as a function of the merger of their respective sets of components. We prove a general integral representation of such kernels and present two particular examples. One of them leads to a kernel for sets of points living in a space endowed itself with a positive definite kernel. We provide experimental results on a benchmark experiment of handwritten digits image classification which illustrate the validity of the approach.
Protein network inference from multiple genomic data: a supervised approach

An increasing number of observations support the hypothesis that most biological functions involve the interactions between many proteins, and that the complexity of living systems arises as a result of such interactions. In this context, the problem of inferring a global protein network for a given organism, using all available genomic data about the organism, is quickly becoming one of the main challenges in current computational biology.



This paper presents a new method to infer protein networks from multiple types of genomic data. Based on a variant of kernel canonical correlation analysis, its originality is in the formalization of the protein network inference problem as a supervised learning problem, and in the integration of heterogeneous genomic data within this framework. We present promising results on the prediction of the protein network for the yeast Saccharomyces cerevisiae from four types of widely available data: gene expressions, protein interactions measured by yeast two-hybrid systems, protein localizations in the cell and protein phylogenetic profiles. The method is shown to outperform other unsupervised protein network inference methods. We finally conduct a comprehensive prediction of the protein network for all proteins of the yeast, which enables us to propose protein candidates for missing enzymes in a biosynthesis pathway.


AVAILABILITY
Softwares are available upon request.