Daniel S. Weld

Also published as: Dan Weld, Daniel Weld


2023

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PaperMage: A Unified Toolkit for Processing, Representing, and Manipulating Visually-Rich Scientific Documents
Kyle Lo | Zejiang Shen | Benjamin Newman | Joseph Chang | Russell Authur | Erin Bransom | Stefan Candra | Yoganand Chandrasekhar | Regan Huff | Bailey Kuehl | Amanpreet Singh | Chris Wilhelm | Angele Zamarron | Marti A. Hearst | Daniel Weld | Doug Downey | Luca Soldaini
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Despite growing interest in applying natural language processing (NLP) and computer vision (CV) models to the scholarly domain, scientific documents remain challenging to work with. They’re often in difficult-to-use PDF formats, and the ecosystem of models to process them is fragmented and incomplete. We introduce PaperMage, an open-source Python toolkit for analyzing and processing visually-rich, structured scientific documents. PaperMage offers clean and intuitive abstractions for seamlessly representing and manipulating both textual and visual document elements. PaperMage achieves this by integrating disparate state-of-the-art NLP and CV models into a unified framework, and provides turn-key recipes for common scientific document processing use-cases. PaperMage has powered multiple research prototypes of AI applications over scientific documents, along with Semantic Scholar’s large-scale production system for processing millions of PDFs. GitHub: https://github.com/allenai/papermage

2022

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Don’t Say What You Don’t Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search
Daniel King | Zejiang Shen | Nishant Subramani | Daniel S. Weld | Iz Beltagy | Doug Downey
Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)

Abstractive summarization systems today produce fluent and relevant output, but often “hallucinate” statements not supported by the source text. We analyze the connection between hallucinations and training data, and find evidence that models hallucinate because they train on target summaries that are unsupported by the source. Based on our findings, we present PINOCCHIO, a new decoding method that improves the consistency of a transformer-based abstractive summarizer by constraining beam search to avoid hallucinations. Given the model states and outputs at a given step, PINOCCHIO detects likely model hallucinations based on various measures of attribution to the source text. PINOCCHIO backtracks to find more consistent output, and can opt to produce no summary at all when no consistent generation can be found. In experiments, we find that PINOCCHIO improves the consistency of generation by an average of 67% on two abstractive summarization datasets, without hurting recall.

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VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups
Zejiang Shen | Kyle Lo | Lucy Lu Wang | Bailey Kuehl | Daniel S. Weld | Doug Downey
Transactions of the Association for Computational Linguistics, Volume 10

Accurately extracting structured content from PDFs is a critical first step for NLP over scientific papers. Recent work has improved extraction accuracy by incorporating elementary layout information, for example, each token’s 2D position on the page, into language model pretraining. We introduce new methods that explicitly model VIsual LAyout (VILA) groups, that is, text lines or text blocks, to further improve performance. In our I-VILA approach, we show that simply inserting special tokens denoting layout group boundaries into model inputs can lead to a 1.9% Macro F1 improvement in token classification. In the H-VILA approach, we show that hierarchical encoding of layout-groups can result in up to 47% inference time reduction with less than 0.8% Macro F1 loss. Unlike prior layout-aware approaches, our methods do not require expensive additional pretraining, only fine-tuning, which we show can reduce training cost by up to 95%. Experiments are conducted on a newly curated evaluation suite, S2-VLUE, that unifies existing automatically labeled datasets and includes a new dataset of manual annotations covering diverse papers from 19 scientific disciplines. Pre-trained weights, benchmark datasets, and source code are available at https://github.com/allenai/VILA.

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GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation
Daniel Khashabi | Gabriel Stanovsky | Jonathan Bragg | Nicholas Lourie | Jungo Kasai | Yejin Choi | Noah A. Smith | Daniel Weld
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

While often assumed a gold standard, effective human evaluation of text generation remains an important, open area for research. We revisit this problem with a focus on producing consistent evaluations that are reproducible—over time and across different populations. We study this goal in different stages of the human evaluation pipeline. In particular, we consider design choices for the annotation interface used to elicit human judgments and their impact on reproducibility. Furthermore, we develop an automated mechanism for maintaining annotator quality via a probabilistic model that detects and excludes noisy annotators. Putting these lessons together, we introduce GENIE: a system for running standardized human evaluations across different generation tasks. We instantiate GENIE with datasets representing four core challenges in text generation: machine translation, summarization, commonsense reasoning, and machine comprehension. For each task, GENIE offers a leaderboard that automatically crowdsources annotations for submissions, evaluating them along axes such as correctness, conciseness, and fluency. We have made the GENIE leaderboards publicly available, and have already ranked 50 submissions from 10 different research groups. We hope GENIE encourages further progress toward effective, standardized evaluations for text generation.

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ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts
Sonia Murthy | Kyle Lo | Daniel King | Chandra Bhagavatula | Bailey Kuehl | Sophie Johnson | Jonathan Borchardt | Daniel Weld | Tom Hope | Doug Downey
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Systems that automatically define unfamiliar terms hold the promise of improving the accessibility of scientific texts, especially for readers who may lack prerequisite background knowledge. However, current systems assume a single “best” description per concept, which fails to account for the many ways a concept can be described. We present ACCoRD, an end-to-end system tackling the novel task of generating sets of descriptions of scientific concepts. Our system takes advantage of the myriad ways a concept is mentioned across the scientific literature to produce distinct, diverse descriptions oftarget concepts in terms of different reference concepts. In a user study, we find that users prefer (1) descriptions produced by our end-to-end system, and (2) multiple descriptions to a single “best” description. We release the ACCoRD corpus which includes 1,275 labeled contexts and 1,787 expert-authored concept descriptions to support research on our task.

2021

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Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models
Tongshuang Wu | Marco Tulio Ribeiro | Jeffrey Heer | Daniel Weld
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

While counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only instantiate limited types of perturbations such as paraphrases or word substitutions. We present Polyjuice, a general-purpose counterfactual generator that allows for control over perturbation types and locations, trained by finetuning GPT-2 on multiple datasets of paired sentences. We show that Polyjuice produces diverse sets of realistic counterfactuals, which in turn are useful in various distinct applications: improving training and evaluation on three different tasks (with around 70% less annotation effort than manual generation), augmenting state-of-the-art explanation techniques, and supporting systematic counterfactual error analysis by revealing behaviors easily missed by human experts.

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Extracting a Knowledge Base of Mechanisms from COVID-19 Papers
Tom Hope | Aida Amini | David Wadden | Madeleine van Zuylen | Sravanthi Parasa | Eric Horvitz | Daniel Weld | Roy Schwartz | Hannaneh Hajishirzi
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We pursue the construction of a knowledge base (KB) of mechanisms—a fundamental concept across the sciences, which encompasses activities, functions and causal relations, ranging from cellular processes to economic impacts. We extract this information from the natural language of scientific papers by developing a broad, unified schema that strikes a balance between relevance and breadth. We annotate a dataset of mechanisms with our schema and train a model to extract mechanism relations from papers. Our experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature, outperforming the prominent PubMed search in a study with clinical experts. Our search engine, dataset and code are publicly available.

2020

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SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search
Tom Hope | Jason Portenoy | Kishore Vasan | Jonathan Borchardt | Eric Horvitz | Daniel Weld | Marti Hearst | Jevin West
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track and explore new directions. Search engines are designed for targeted queries, not for discovery of connections across a corpus. In this paper, we present SciSight, a system for exploratory search of COVID-19 research integrating two key capabilities: first, exploring associations between biomedical facets automatically extracted from papers (e.g., genes, drugs, diseases, patient outcomes); second, combining textual and network information to search and visualize groups of researchers and their ties. SciSight has so far served over 15K users with over 42K page views and 13% returns.

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SPECTER: Document-level Representation Learning using Citation-informed Transformers
Arman Cohan | Sergey Feldman | Iz Beltagy | Doug Downey | Daniel Weld
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, accurate embeddings of documents are a necessity. We propose SPECTER, a new method to generate document-level embedding of scientific papers based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, Specter can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that Specter outperforms a variety of competitive baselines on the benchmark.

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S2ORC: The Semantic Scholar Open Research Corpus
Kyle Lo | Lucy Lu Wang | Mark Neumann | Rodney Kinney | Daniel Weld
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We introduce S2ORC, a large corpus of 81.1M English-language academic papers spanning many academic disciplines. The corpus consists of rich metadata, paper abstracts, resolved bibliographic references, as well as structured full text for 8.1M open access papers. Full text is annotated with automatically-detected inline mentions of citations, figures, and tables, each linked to their corresponding paper objects. In S2ORC, we aggregate papers from hundreds of academic publishers and digital archives into a unified source, and create the largest publicly-available collection of machine-readable academic text to date. We hope this resource will facilitate research and development of tools and tasks for text mining over academic text.

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Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions
Dongyeop Kang | Andrew Head | Risham Sidhu | Kyle Lo | Daniel Weld | Marti A. Hearst
Proceedings of the First Workshop on Scholarly Document Processing

The task of definition detection is important for scholarly papers, because papers often make use of technical terminology that may be unfamiliar to readers. Despite prior work on definition detection, current approaches are far from being accurate enough to use in realworld applications. In this paper, we first perform in-depth error analysis of the current best performing definition detection system and discover major causes of errors. Based on this analysis, we develop a new definition detection system, HEDDEx, that utilizes syntactic features, transformer encoders, and heuristic filters, and evaluate it on a standard sentence-level benchmark. Because current benchmarks evaluate randomly sampled sentences, we propose an alternative evaluation that assesses every sentence within a document. This allows for evaluating recall in addition to precision. HEDDEx outperforms the leading system on both the sentence-level and the document-level tasks, by 12.7 F1 points and 14.4 F1 points, respectively. We note that performance on the high-recall document-level task is much lower than in the standard evaluation approach, due to the necessity of incorporation of document structure as features. We discuss remaining challenges in document-level definition detection, ideas for improvements, and potential issues for the development of reading aid applications.

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CORD-19: The COVID-19 Open Research Dataset
Lucy Lu Wang | Kyle Lo | Yoganand Chandrasekhar | Russell Reas | Jiangjiang Yang | Doug Burdick | Darrin Eide | Kathryn Funk | Yannis Katsis | Rodney Michael Kinney | Yunyao Li | Ziyang Liu | William Merrill | Paul Mooney | Dewey A. Murdick | Devvret Rishi | Jerry Sheehan | Zhihong Shen | Brandon Stilson | Alex D. Wade | Kuansan Wang | Nancy Xin Ru Wang | Christopher Wilhelm | Boya Xie | Douglas M. Raymond | Daniel S. Weld | Oren Etzioni | Sebastian Kohlmeier
Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020

The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 200K times and has served as the basis of many COVID-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for COVID-19.

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TLDR: Extreme Summarization of Scientific Documents
Isabel Cachola | Kyle Lo | Arman Cohan | Daniel Weld
Findings of the Association for Computational Linguistics: EMNLP 2020

We introduce TLDR generation, a new form of extreme summarization, for scientific papers. TLDR generation involves high source compression and requires expert background knowledge and understanding of complex domain-specific language. To facilitate study on this task, we introduce SCITLDR, a new multi-target dataset of 5.4K TLDRs over 3.2K papers. SCITLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden. We propose CATTS, a simple yet effective learning strategy for generating TLDRs that exploits titles as an auxiliary training signal. CATTS improves upon strong baselines under both automated metrics and human evaluations. Data and code are publicly available at https://github.com/allenai/scitldr.

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SpanBERT: Improving Pre-training by Representing and Predicting Spans
Mandar Joshi | Danqi Chen | Yinhan Liu | Daniel S. Weld | Luke Zettlemoyer | Omer Levy
Transactions of the Association for Computational Linguistics, Volume 8

We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERTlarge, our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE.1

2019

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Errudite: Scalable, Reproducible, and Testable Error Analysis
Tongshuang Wu | Marco Tulio Ribeiro | Jeffrey Heer | Daniel Weld
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Though error analysis is crucial to understanding and improving NLP models, the common practice of manual, subjective categorization of a small sample of errors can yield biased and incomplete conclusions. This paper codifies model and task agnostic principles for informative error analysis, and presents Errudite, an interactive tool for better supporting this process. First, error groups should be precisely defined for reproducibility; Errudite supports this with an expressive domain-specific language. Second, to avoid spurious conclusions, a large set of instances should be analyzed, including both positive and negative examples; Errudite enables systematic grouping of relevant instances with filtering queries. Third, hypotheses about the cause of errors should be explicitly tested; Errudite supports this via automated counterfactual rewriting. We validate our approach with a user study, finding that Errudite (1) enables users to perform high quality and reproducible error analyses with less effort, (2) reveals substantial ambiguities in prior published error analyses practices, and (3) enhances the error analysis experience by allowing users to test and revise prior beliefs.

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Pretrained Language Models for Sequential Sentence Classification
Arman Cohan | Iz Beltagy | Daniel King | Bhavana Dalvi | Dan Weld
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in context of the document. Recent successful models for this task have used hierarchical models to contextualize sentence representations, and Conditional Random Fields (CRFs) to incorporate dependencies between subsequent labels. In this work, we show that pretrained language models, BERT (Devlin et al., 2018) in particular, can be used for this task to capture contextual dependencies without the need for hierarchical encoding nor a CRF. Specifically, we construct a joint sentence representation that allows BERT Transformer layers to directly utilize contextual information from all words in all sentences. Our approach achieves state-of-the-art results on four datasets, including a new dataset of structured scientific abstracts.

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BERT for Coreference Resolution: Baselines and Analysis
Mandar Joshi | Omer Levy | Luke Zettlemoyer | Daniel Weld
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We apply BERT to coreference resolution, achieving a new state of the art on the GAP (+11.5 F1) and OntoNotes (+3.9 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO), but that there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. We will release all code and trained models upon publication.

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pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference
Mandar Joshi | Eunsol Choi | Omer Levy | Daniel Weld | Luke Zettlemoyer
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Reasoning about implied relationships (e.g. paraphrastic, common sense, encyclopedic) between pairs of words is crucial for many cross-sentence inference problems. This paper proposes new methods for learning and using embeddings of word pairs that implicitly represent background knowledge about such relationships. Our pairwise embeddings are computed as a compositional function of each word’s representation, which is learned by maximizing the pointwise mutual information (PMI) with the contexts in which the the two words co-occur. We add these representations to the cross-sentence attention layer of existing inference models (e.g. BiDAF for QA, ESIM for NLI), instead of extending or replacing existing word embeddings. Experiments show a gain of 2.7% on the recently released SQuAD 2.0 and 1.3% on MultiNLI. Our representations also aid in better generalization with gains of around 6-7% on adversarial SQuAD datasets, and 8.8% on the adversarial entailment test set by Glockner et al. (2018).

2018

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Semi-Supervised Event Extraction with Paraphrase Clusters
James Ferguson | Colin Lockard | Daniel Weld | Hannaneh Hajishirzi
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Supervised event extraction systems are limited in their accuracy due to the lack of available training data. We present a method for self-training event extraction systems by bootstrapping additional training data. This is done by taking advantage of the occurrence of multiple mentions of the same event instances across newswire articles from multiple sources. If our system can make a high-confidence extraction of some mentions in such a cluster, it can then acquire diverse training examples by adding the other mentions as well. Our experiments show significant performance improvements on multiple event extractors over ACE 2005 and TAC-KBP 2015 datasets.

2017

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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Mandar Joshi | Eunsol Choi | Daniel Weld | Luke Zettlemoyer
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers. We also present two baseline algorithms: a feature-based classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study.

2016

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Effective Crowd Annotation for Relation Extraction
Angli Liu | Stephen Soderland | Jonathan Bragg | Christopher H. Lin | Xiao Ling | Daniel S. Weld
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Exploiting Parallel News Streams for Unsupervised Event Extraction
Congle Zhang | Stephen Soderland | Daniel S. Weld
Transactions of the Association for Computational Linguistics, Volume 3

Most approaches to relation extraction, the task of extracting ground facts from natural language text, are based on machine learning and thus starved by scarce training data. Manual annotation is too expensive to scale to a comprehensive set of relations. Distant supervision, which automatically creates training data, only works with relations that already populate a knowledge base (KB). Unfortunately, KBs such as FreeBase rarely cover event relations (e.g. “person travels to location”). Thus, the problem of extracting a wide range of events — e.g., from news streams — is an important, open challenge. This paper introduces NewsSpike-RE, a novel, unsupervised algorithm that discovers event relations and then learns to extract them. NewsSpike-RE uses a novel probabilistic graphical model to cluster sentences describing similar events from parallel news streams. These clusters then comprise training data for the extractor. Our evaluation shows that NewsSpike-RE generates high quality training sentences and learns extractors that perform much better than rival approaches, more than doubling the area under a precision-recall curve compared to Universal Schemas.

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Design Challenges for Entity Linking
Xiao Ling | Sameer Singh | Daniel S. Weld
Transactions of the Association for Computational Linguistics, Volume 3

Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions of the EL problem and presenting a simple yet effective, modular, unsupervised system, called Vinculum, for entity linking. We conduct an extensive evaluation on nine data sets, comparing Vinculum with two state-of-the-art systems, and elucidate key aspects of the system that include mention extraction, candidate generation, entity type prediction, entity coreference, and coherence.

2014

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Type-Aware Distantly Supervised Relation Extraction with Linked Arguments
Mitchell Koch | John Gilmer | Stephen Soderland | Daniel S. Weld
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Joint Coreference Resolution and Named-Entity Linking with Multi-Pass Sieves
Hannaneh Hajishirzi | Leila Zilles | Daniel S. Weld | Luke Zettlemoyer
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Harvesting Parallel News Streams to Generate Paraphrases of Event Relations
Congle Zhang | Daniel S. Weld
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2011

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Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations
Raphael Hoffmann | Congle Zhang | Xiao Ling | Luke Zettlemoyer | Daniel S. Weld
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Learning First-Order Horn Clauses from Web Text
Stefan Schoenmackers | Jesse Davis | Oren Etzioni | Daniel Weld
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Open Information Extraction Using Wikipedia
Fei Wu | Daniel S. Weld
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Learning 5000 Relational Extractors
Raphael Hoffmann | Congle Zhang | Daniel S. Weld
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Machine Reading at the University of Washington
Hoifung Poon | Janara Christensen | Pedro Domingos | Oren Etzioni | Raphael Hoffmann | Chloe Kiddon | Thomas Lin | Xiao Ling | Mausam | Alan Ritter | Stefan Schoenmackers | Stephen Soderland | Dan Weld | Fei Wu | Congle Zhang
Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading

2009

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Compiling a Massive, Multilingual Dictionary via Probabilistic Inference
Mausam | Stephen Soderland | Oren Etzioni | Daniel Weld | Michael Skinner | Jeff Bilmes
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

2008

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Scaling Textual Inference to the Web
Stefan Schoenmackers | Oren Etzioni | Daniel Weld
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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