nils-herrmann/clean_scirepeval huggingface dataset:
Collective multi-label classification

Common approaches to multi-label classification learn independent classifiers for each category, and employ ranking or thresholding schemes for classification. Because they do not exploit dependencies between labels, such techniques are only well-suited to problems in which categories are independent. However, in many domains labels are highly interdependent. This paper explores multi-label conditional random field (CRF)classification models that directly parameterize label co-occurrences in multi-label classification. Experiments show that the models outperform their single-label counterparts on standard text corpora. Even when multi-labels are sparse, the models improve subset classification error by as much as 40%.
A Note on Topical N-grams

Most of the popular topic models (such as Latent Dirichlet Allocation) have an underlying assumption: bag of words. However, text is indeed a sequence of discrete word tokens, and without considering the order of words (in another word, the nearby context where a word is located), the accurate meaning of language cannot be exactly captured by word co-occurrences only. In this sense, collocations of words (phrases) have to be considered. However, like individual words, phrases sometimes show polysemy as well depending on the context. More noticeably, a composition of two (or more) words is a phrase in some contexts, but not in other contexts. In this paper, the authors propose a new probabilistic generative model that automatically determines unigram words and phrases based on context and simultaneously associates them with a mixture of topics. They present very interesting results on large text corpora.
ACL-05 Feature Engineering for Machine Learning in Natural Language Processing

ii Introduction The ACL 2005 Workshop on Feature Engineering for Machine Learning in Natural Language Processing is an opportunity to explore the various dimensions of feature engineering for problems that are of interest to the ACL community. Feature Engineering encompasses feature design, feature selection, feature induction, studies of feature impact (including feature ablation studies), and related topics. In 2003, there was a NIPS workshop on feature engineering (" Feature Extraction and Feature Selection "), but the focus was not on NLP problems specifically. Also, although the various aspects of feature engineering have been dealt with at times in various ACL forums, until now, to our knowledge, the spotlight has never been shone directly on this topic specifically for NLP and language technology problems. We feel that now is the time to look more closely. As experience with machine learning for solving natural language processing tasks accumulates in the field, practitioners are finding that feature engineering is as critical as the choice of machine learning algorithm, if not more so. Feature engineering significantly affects the performance of systems and deserves greater attention. Also, in the wake of the shift in our field away from knowledge engineering and of the successes of data-driven and statistical methods, researchers are likely to make further progress by incorporating additional, sometimes familiar, sources of knowledge as features. Feature design may benefit from expert insight even where the relative merits of features must be assessed through empirical techniques from data. Although some experience in the area of feature engineering is to be found in the theoretical machine learning community, the particular demands of natural language processing leave much to be discovered. In the call for papers, we expressed our intent of bringing together practitioners of NLP, machine learning, information extraction, speech processing, and related fields with the goal of sharing experimental evidence for successful approaches to feature engineering. Judging by the quality and diversity of the submissions received, we believe we have succeeded, and the resulting program should be of great interest to many researchers in the ACL community. We hope that the workshop will contribute to the distillation of best practices and to the discovery of new sources of knowledge and features previously untapped. We also extend an open invitation to the reader to continue investigation in all aspects of feature engineering for machine learning in NLP, including: • Novel methods for discovering or inducing features, …
Topic and Role Discovery in Social Networks

Previous work in social network analysis (SNA) has modeled the existence of links from one entity to another, but not the language content or topics on those links. We present the Author-Recipient-Topic (ART) model for social network analysis, which learns topic distributions based on the direction-sensitive messages sent between entities. The model builds on Latent Dirichlet Allocation (LDA) and the Author-Topic (AT) model, adding the key attribute that distribution over topics is conditioned distinctly on both the sender and recipient--steering the discovery of topics according to the relationships between people. We give results on both the Enron email corpus and a researcher's email archive, providing evidence not only that clearly relevant topics are discovered, but that the ART model better predicts people's roles.
Group and Topic Discovery from Relations and Their Attributes

We present a probabilistic generative model of entity relationships and their attributes that simultaneously discovers groups among the entities and topics among the corresponding textual attributes. Block-models of relationship data have been studied in social network analysis for some time. Here we simultaneously cluster in several modalities at once, incorporating the attributes (here, words) associated with certain relationships. Significantly, joint inference allows the discovery of topics to be guided by the emerging groups, and vice-versa. We present experimental results on two large data sets: sixteen years of bills put before the U.S. Senate, comprising their corresponding text and voting records, and thirteen years of similar data from the United Nations. We show that in comparison with traditional, separate latent-variable models for words, or Block-structures for votes, the Group-Topic model's joint inference discovers more cohesive groups and improved topics.
A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance

The need to measure sequence similarity arises in information extraction, object identity, data mining, biological sequence analysis, and other domains. This paper presents discriminative string-edit CRFs, a finite-state conditional random field model for edit sequences between strings. Conditional random fields have advantages over generative approaches to this problem, such as pair HMMs or the work of Ristad and Yianilos, because as conditionally-trained methods, they enable the use of complex, arbitrary actions and features of the input strings. As in generative models, the training data does not have to specify the edit sequences between the given string pairs. Unlike generative models, however, our model is trained on both positive and negative instances of string pairs. We present positive experimental results on several data sets.
Information Extraction

In 2001 the U.S. Department of Labor was tasked with building a Web site that would help people find continuing education opportunities at community colleges, universities, and organizations across the country. The department wanted its Web site to support fielded Boolean searches over locations, dates, times, prerequisites, instructors, topic areas, and course descriptions. Ultimately it was also interested in mining its new database for patterns and educational trends. This was a major data-integration project, aiming to automatically gather detailed, structured information from tens of thousands of individual institutions every three months.
Group and topic discovery from relations and text

We present a probabilistic generative model of entity relationships and textual attributes that simultaneously discovers groups among the entities and topics among the corresponding text. Block-models of relationship data have been studied in social network analysis for some time. Here we simultaneously cluster in several modalities at once, incorporating the words associated with certain relationships. Significantly, joint inference allows the discovery of groups to be guided by the emerging topics, and vice-versa. We present experimental results on two large data sets: sixteen years of bills put before the U.S. Senate, comprising their corresponding text and voting records, and 43 years of similar data from the United Nations. We show that in comparison with traditional, separate latent-variable models for words or Blockstructures for votes, the Group-Topic model's joint inference improves both the groups and topics discovered.
Automatic Categorization of Email into Folders: Benchmark Experiments on Enron and SRI Corpora

Office workers everywhere are drowning in email—not only spam, but also large quantities of legitimate email to be read and organized for browsing. Although there have been extensive investigations of automatic document categorization, email gives rise to a number of unique challenges, and there has been relatively little study of classifying email into folders. This paper presents an extensive benchmark study of email foldering using two large corpora of real-world email messages and foldering schemes: one from former Enron employees, another from participants in an SRI research project. We discuss the challenges that arise from differences between email foldering and traditional document classification. We show experimental results from an array of automated classification methods and evaluation methodologies, including a new evaluation method of foldering results based on the email timeline, and including enhancements to the exponential gradient method Winnow, providing top-tier accuracy with a fraction the training time of alternative methods. We also establish that classification accuracy in many cases is relatively low, confirming the challenges of email data, and pointing toward email foldering as an important area for further research.
Gene Prediction with Conditional Random Fields

Given a sequence of DNA nucleotide bases, the task of gene prediction is to find subsequences of bases that encode proteins. Reasonable performance on this task has been achieved using generatively trained sequence models, such as hidden Markov models. We propose instead the use of a discriminitively trained sequence model, the conditional random field (CRF). CRFs can naturally incorporate arbitrary, non-independent features of the input without making conditional independence assumptions among the features. This can be particularly important for gene finding, where including evidence from protein databases, EST data, or tiling arrays may improve accuracy. We evaluate our model on human genomic data, and show that CRFs perform better than HMM-based models at incorporating homology evidence from protein databases, achieving a 10% reduction in base-level errors.
Semi-Supervised Sequence Modeling with Syntactic Topic Models

Although there has been significant previous work on semi-supervised learning for classification, there has been relatively little in sequence modeling. This paper presents an approach that leverages recent work in manifold-learning on sequences to discover word clusters from language data, including both syntactic classes and semantic topics. From unlabeled data we form a smooth. low-dimensional feature space, where each word token is projected based on its underlying role as a function or content word. We then use this projection as additional input features to a linear-chain conditional random field trained on limited labeled training data. On standard part-of-speech tagging and Chinese word segmentation data sets we show as much as 14% error reduction due to the unlabeled data, and also statistically-significant improvements over a related semi-supervised sequence tagging method due to Miller et al.
Joint deduplication of multiple record types in relational data

Record deduplication is the task of merging database records that refer to the same underlying entity. In relational data-bases, accurate deduplication for records of one type is often dependent on the decisions made for records of other types. Whereas nearly all previous approaches have merged records of different types independently, this work models these inter-dependencies explicitly to collectively deduplicate records of multiple types. We construct a conditional random field model of deduplication that captures these relational dependencies, and then employ a novel relational partitioning algorithm to jointly deduplicate records. For two citation matching datasets, we show that collectively deduplicating paper and venue records results in up to a 30% error reduction in venue deduplication, and up to a 20% error reduction in paper deduplication.
Detecting anomalies in network traffic using maximum entropy estimation

We develop a behavior-based anomaly detection method that detects network anomalies by comparing the current network traffic against a baseline distribution. The Maximum Entropy technique provides a flexible and fast approach to estimate the baseline distribution, which also gives the network administrator a multi-dimensional view of the network traffic. By computing a measure related to the relative entropy of the network traffic under observation with respect to the baseline distribution, we are able to distinguish anomalies that change the traffic either abruptly or slowly. In addition, our method provides information revealing the type of the anomaly detected. It requires a constant memory and a computation time proportional to the traffic rate.
Direct Maximization of Rank-Based Metrics for Information Retrieval

Ranking is an essential component for a number of tasks, such as information retrieval and collaborative filtering. It is often the case that the underlying task attempts to maximize some evaluation metric, such as mean average precision, over rankings. Most past work on learning how to rank has focused on likelihoodor margin-based approaches. In this work we explore directly maximizing rank-based metrics, which are a family of metrics that only depend on the order of ranked items. This allows us to maximize different metrics for the same training data. We show how the parameter space of linear scoring functions can be reduced to a multinomial manifold. Parameter estimation is accomplished by optimizing the evaluation metric over the manifold. Results from ad hoc information retrieval are given that show our model yields significant improvements in effectiveness over other approaches.
Reducing Labeling Effort for Structured Prediction Tasks

A common obstacle preventing the rapid deployment of supervised machine learning algorithms is the lack of labeled training data. This is particularly expensive to obtain for structured prediction tasks, where each training instance may have multiple, interacting labels, all of which must be correctly annotated for the instance to be of use to the learner. Traditional active learning addresses this problem by optimizing the order in which the examples are labeled to increase learning efficiency. However, this approach does not consider the difficulty of labeling each example, which can vary widely in structured prediction tasks. For example, the labeling predicted by a partially trained system may be easier to correct for some instances than for others. 
 
We propose a new active learning paradigm which reduces not only how many instances the annotator must label, but also how difficult each instance is to annotate. The system also leverages information from partially correct predictions to efficiently solicit annotations from the user. We validate this active learning framework in an interactive information extraction system, reducing the total number of annotation actions by 22%.
Reducin gLabelin gEffor tfo rStructure dPrediction Tasks

A common obstacle preventing the rapid deployment of supervised machine learning algorithms is the lack of labeled training data. This is particularly expensive to obtain for structured prediction tasks, where each training instance may have multiple, interacting labels, all of which must be correctly annotated for the instance to be of use to the learner. Traditional active learning addresses this problem by optimizing the order in which the examples are labeled to increase learning efficiency. However, this approach does not consider the difficulty of labeling each example, which can vary widely in structured prediction tasks. For example, the labeling predicted by a partially trained system may be easier to correct for some instances than for others. We propose a new active learning paradigm which reduces not only how many instances the annotator must label, but also how difficult each instance is to annotate. The system also leverages information from partially correct predictions to efficiently solicit annotations from the user. We validate this active learning framework in an interactive information extraction system, reducing the total number of annotation actions by 22%.
Disambiguating Web appearances of people in a social network

Say you are looking for information about a particular person. A search engine returns many pages for that person's name but which pages are about the person you care about, and which are about other people who happen to have the same name? Furthermore, if we are looking for multiple people who are related in some way, how can we best leverage this social network? This paper presents two unsupervised frameworks for solving this problem: one based on link structure of the Web pages, another using Agglomerative/Conglomerative Double Clustering (A/CDC)---an application of a recently introduced multi-way distributional clustering method. To evaluate our methods, we collected and hand-labeled a dataset of over 1000 Web pages retrieved from Google queries on 12 personal names appearing together in someones in an email folder. On this dataset our methods outperform traditional agglomerative clustering by more than 20%, achieving over 80% F-measure.
Multi-way distributional clustering via pairwise interactions

We present a novel unsupervised learning scheme that simultaneously clusters variables of several types (e.g., documents, words and authors) based on pairwise interactions between the types, as observed in co-occurrence data. In this scheme, multiple clustering systems are generated aiming at maximizing an objective function that measures multiple pairwise mutual information between cluster variables. To implement this idea, we propose an algorithm that interleaves top-down clustering of some variables and bottom-up clustering of the other variables, with a local optimization correction routine. Focusing on document clustering we present an extensive empirical study of two-way, three-way and four-way applications of our scheme using six real-world datasets including the 20 News-groups (20NG) and the Enron email collection. Our multi-way distributional clustering (MDC) algorithms consistently and significantly outperform previous state-of-the-art information theoretic clustering algorithms.
Automating the Construction of Internet Portals with Machine Learning

Domain-specific internet portals are growing in popularity because they gather content from the Web and organize it for easy access, retrieval and search. For example, www.campsearch.com allows complex queries by age, location, cost and specialty over summer camps. This functionality is not possible with general, Web-wide search engines. Unfortunately these portals are difficult and time-consuming to maintain. This paper advocates the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific Internet portals. We describe new research in reinforcement learning, information extraction and text classification that enables efficient spidering, the identification of informative text segments, and the population of topic hierarchies. Using these techniques, we have built a demonstration system: a portal for computer science research papers. It already contains over 50,000 papers and is publicly available at www.cora.justresearch.com. These techniques are widely applicable to portal creation in other domains.
An Exploration of Entity Models, Collective Classification and Relation Description

Traditional information retrieval typically represents data using a bag of words; data mining typically uses a highly structured database representation. This paper explores the middle ground using a representation which we term entity models, in which questions about structured data may be posed and answered, but the complexities and task-specific restrictions of ontologies are avoided. An entity model is a language model or word distribution associated with an entity, such as a person, place or organization. Using these perentity language models, entities may be clustered, links may be detected or described with a short summary, entities may be collectively classified, and question answering may be performed. On a corpus of entities extracted from newswire and the Web, we group entities by profession with 90% accuracy, improve accuracy further on the task of classifying politicians as liberal or conservative using collective classification and conditional random fields, and answer questions about “who a person is” with mean reciprocal rank (MRR) of 0.52.
Accurate Information Extraction from Research Papers using Conditional Random Fields

With the increasing use of research paper search engines, such as CiteSeer, for both literature search and hiring decisions, the accuracy of such systems is of paramount importance. This paper employs Conditional Random Fields (CRFs) for the task of extracting various common fields from the headers and citation of research papers. The basic theory of CRFs is becoming well-understood, but best-practices for applying them to real-world data requires additional exploration. This paper makes an empirical exploration of several factors, including variations on Gaussian, exponential and hyperbolic-L1 priors for improved regularization, and several classes of features and Markov order. On a standard benchmark data set, we achieve new state-of-the-art performance, reducing error in average F1 by 36%, and word error rate by 78% in comparison with the previous best SVM results. Accuracy compares even more favorably against HMMs.