Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Computational Linguistics
Title: The ITI TXM corpora: Tissue expressions and protein-protein interactions
Abstract: We report on two large corpora of semantically annotated full-text biomedical research papers created in order to devel op information extraction ( IE) tools for the TXM project. Both corpora have been annotated with a range of entities (CellLine, Complex, DevelopmentalStage, Disease, DrugCompound, ExperimentalMethod, Fragment, Fusion, GOMOP, Gene, Modification, mRNAcDNA, Mutant, Protein, Tissue), normalisations of selected entities to the NCBI Taxonomy, RefSeq, EntrezGene, ChEBI and MeSH and enriched relations (protein-protein interactions, tissue expressions and fr agment- or mutant-protein relations). While one corpus targets protein-protein interactions ( PPIs), the focus of other is on tissue expressions ( TEs). This paper describes the selected markables and the annotation process of the ITI TXM corpora, and provides a detailed breakdown of the inter-annotator agreement (IAA).
Extracted statement — Concept types: mRNAcDNA
Research field: Computational Linguistics
Title: Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Abstract: Knowledge base question answering (KBQA) is an important task in Natural Language Processing. Existing approaches face significant challenges including complex question understanding, necessity for reasoning, and lack of large end-to-end training datasets. In this work, we propose Neuro-Symbolic Question Answering (NSQA), a modular KBQA system, that leverages (1) Abstract Meaning Representation (AMR) parses for task-independent question understanding; (2) a simple yet effective graph transformation approach to convert AMR parses into candidate logical queries that are aligned to the KB; (3) a pipeline-based approach which integrates multiple, reusable modules that are trained specifically for their individual tasks (semantic parser, entity and relationship linkers, and neuro-symbolic reasoner) and do not require end-to-end training data. NSQA achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia (QALD-9 and LC-QuAD 1.0). Furthermore, our analysis emphasizes that AMR is a powerful tool for KBQA systems.
Extracted statement — On evaluation dataset: QALD-9
Research field: Computer and Systems Architecture
Title: Process Oriented Knowledge Management: A Service Based Approach
Abstract: This paper introduces a new viewpoint in knowledge management by introducing KM-Services as a basic concept for Knowledge Management. This text discusses the vision of service oriented knowledge management (KM) as a realisation approach of process oriented knowledge management. In the following process oriented knowledge management as it was defined in the EU-project PROMOTE (IST-1999-11658) is presented and the KM-Service approach to realise process oriented knowledge management is explained. The last part is concerned with an implementation scenario that uses Web-technology to realise a service framework for a KM-system.
Extracted statement — Approach name: PROMOTE
Research field: Computer and Systems Architecture
Title: B-KIDE: a framework and a tool for business process-oriented knowledge infrastructure development
Abstract: The need for an effective management of knowledge is gaining increasing recognition in today's economy. To acknowledge this fact, new promising and powerful technologies have emerged from industrial and academic research. With these innovations maturing, organizations are increasingly willing to adapt such new knowledge management technologies to improve their knowledge-intensive businesses. However, the successful application in given business contexts is a complex, multidimensional challenge and a current research topic. Therefore, this contribution addresses this challenge and introduces a framework for the development of business process-supportive, technological knowledge infrastructures. While business processes represent the organizational setting for the application of knowledge management technologies, knowledge infrastructures represent a concept that can enable knowledge management in organizations. The B-KIDE Framework introduced in this work provides support for the development of knowledge infrastructures that comprise innovative knowledge management functionality and are visibly supportive of an organization's business processes. The developed B-KIDE Tool eases the application of the B-KIDE Framework for knowledge infrastructure developers. Three empirical studies that were conducted with industrial partners from heterogeneous industry sectors corroborate the relevance and viability of the introduced concepts. Copyright © 2005 John Wiley & Sons, Ltd.
Extracted statement — Approach name: B-KIDE
Research field: Computer Engineering
Title: Extension of the BIdo ontology to represent scientific production
Abstract: The SPAR Ontology Network is a suite of complementary ontology modules to describe the scholarly publishing domain. BiDO Standard Bibliometric Measures is part of its set of ontologies. It allows describing of numerical and categorical bibliometric data such as h-index, author citation count, journal impact factor. These measures may be used to evaluate scientific production of researchers. However, they are not enough. In a previous study, we determined the lack of some terms to provide a more complete representation of scientific production. Hence, we have built an extension using the NeOn Methodology to restructure the BiDO ontology. With this extension, it is possible to represent and measure the number of documents from research, the number of citations from a paper and the number of publications in high impact journals according to its area and discipline.
Extracted statement — Ontology: BIDO
Research field: Computer Sciences
Title: BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Abstract: We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and other recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa on GLUE and SQuAD, and achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 3.5 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also replicate other pretraining schemes within the BART framework, to understand their effect on end-task performance.
Extracted statement — has model: BART
Research field: Computer Sciences
Title: CURL: Contrastive Unsupervised Representations for Reinforcement Learning
Abstract: We present CURL: Contrastive Unsupervised Representations for Reinforcement Learning. CURL extracts high-level features from raw pixels using contrastive learning and performs off-policy control on top of the extracted features. CURL outperforms prior pixel-based methods, both model-based and model-free, on complex tasks in the DeepMind Control Suite and Atari Games showing 1.9x and 1.2x performance gains at the 100K environment and interaction steps benchmarks respectively. On the DeepMind Control Suite, CURL is the first image-based algorithm to nearly match the sample-efficiency of methods that use state-based features. Our code is open-sourced and available at this https URL.
Extracted statement — has model: CURL
Research field: Computer Sciences
Title: Dynamic Coattention Networks For Question Answering
Abstract: Several deep learning models have been proposed for question answering. However, due to their single-pass nature, they have no way to recover from local maxima corresponding to incorrect answers. To address this problem, we introduce the Dynamic Coattention Network (DCN) for question answering. The DCN first fuses co-dependent representations of the question and the document in order to focus on relevant parts of both. Then a dynamic pointing decoder iterates over potential answer spans. This iterative procedure enables the model to recover from initial local maxima corresponding to incorrect answers. On the Stanford question answering dataset, a single DCN model improves the previous state of the art from 71.0% F1 to 75.9%, while a DCN ensemble obtains 80.4% F1.
Extracted statement — has model: DCN
Research field: Computer Sciences
Title: A Large Public Corpus of Web Tables containing Time and Context Metadata
Abstract: The Web contains vast amounts of HTML tables. Most of these tables are used for layout purposes, but a small subset of the tables is relational, meaning that they contain structured data describing a set of entities [2]. As these relational Web tables cover a very wide range of different topics, there is a growing body of research investigating the utility of Web table data for completing cross-domain knowledge bases [6], for extending arbitrary tables with additional attributes [7, 4], as well as for translating data values [5]. The existing research shows the potentials of Web tables. However, comparing the performance of the different systems is difficult as up till now each system is evaluated using a different corpus of Web tables and as most of the corpora are owned by large search engine companies and are thus not accessible to the public. In this poster, we present a large public corpus of Web tables which contains over 233 million tables and has been extracted from the July 2015 version of the CommonCrawl. By publishing the corpus as well as all tools that we used to extract it from the crawled data, we intend to provide a common ground for evaluating Web table systems. The main difference of the corpus compared to an earlier corpus that we extracted from the 2012 version of the CommonCrawl as well as the corpus extracted by Eberius et al. [3] from the 2014 version of the CommonCrawl is that the current corpus contains a richer set of metadata for each table. This metadata includes table-specific information such as table orientation, table caption, header row, and key column, but also context information such as the text before and after the table, the title of the HTML page, as well as timestamp information that was found before and after the table. The context information can be useful for recovering the semantics of a table [7]. The timestamp information is crucial for fusing time-depended data, such as alternative population numbers for a city [8].
Extracted statement — Input format: HTML
Research field: Computer Sciences
Title: Schema extraction for tabular data on the web
Abstract: Tabular data is an abundant source of information on the Web, but remains mostly isolated from the latter's interconnections since tables lack links and computer-accessible descriptions of their structure. In other words, the schemas of these tables -- attribute names, values, data types, etc. -- are not explicitly stored as table metadata. Consequently, the structure that these tables contain is not accessible to the crawlers that power search engines and thus not accessible to user search queries. We address this lack of structure with a new method for leveraging the principles of table construction in order to extract table schemas. Discovering the schema by which a table is constructed is achieved by harnessing the similarities and differences of nearby table rows through the use of a novel set of features and a feature processing scheme. The schemas of these data tables are determined using a classification technique based on conditional random fields in combination with a novel feature encoding method called logarithmic binning, which is specifically designed for the data table extraction task. Our method provides considerable improvement over the well-known WebTables schema extraction method. In contrast with previous work that focuses on extracting individual relations, our method excels at correctly interpreting full tables, thereby being capable of handling general tables such as those found in spreadsheets, instead of being restricted to HTML tables as is the case with the WebTables method. We also extract additional schema characteristics, such as row groupings, which are important for supporting information retrieval tasks on tabular data.
Extracted statement — Input format: HTML
Research field: Computer Sciences
Title: An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Abstract: For most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine translation. Given a new sequence modeling task or dataset, which architecture should one use? We conduct a systematic evaluation of generic convolutional and recurrent architectures for sequence modeling. The models are evaluated across a broad range of standard tasks that are commonly used to benchmark recurrent networks. Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory. We conclude that the common association between sequence modeling and recurrent networks should be reconsidered, and convolutional networks should be regarded as a natural starting point for sequence modeling tasks. To assist related work, we have made code available at this http URL .
Extracted statement — has model: LSTM
Research field: Computer Sciences
Title: Mean Actor Critic
Abstract: We propose a new algorithm, Mean Actor-Critic (MAC), for discrete-action continuous-state reinforcement learning. MAC is a policy gradient algorithm that uses the agent's explicit representation of all action values to estimate the gradient of the policy, rather than using only the actions that were actually executed. We prove that this approach reduces variance in the policy gradient estimate relative to traditional actor-critic methods. We show empirical results on two control domains and on six Atari games, where MAC is competitive with state-of-the-art policy search algorithms.
Extracted statement — has model: MAC
Research field: Computer Sciences
Title: MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension
Abstract: Machine comprehension(MC) style question answering is a representative problem in natural language processing. Previous methods rarely spend time on the improvement of encoding layer, especially the embedding of syntactic information and name entity of the words, which are very crucial to the quality of encoding. Moreover, existing attention methods represent each query word as a vector or use a single vector to represent the whole query sentence, neither of them can handle the proper weight of the key words in query sentence. In this paper, we introduce a novel neural network architecture called Multi-layer Embedding with Memory Network(MEMEN) for machine reading task. In the encoding layer, we employ classic skip-gram model to the syntactic and semantic information of the words to train a new kind of embedding layer. We also propose a memory network of full-orientation matching of the query and passage to catch more pivotal information. Experiments show that our model has competitive results both from the perspectives of precision and efficiency in Stanford Question Answering Dataset(SQuAD) among all published results and achieves the state-of-the-art results on TriviaQA dataset.
Extracted statement — has model: MEMEN
Research field: Computer Sciences
Title: Model-Free Episodic Control with State Aggregation
Abstract: Episodic control provides a highly sample-efficient method for reinforcement learning while enforcing high memory and computational requirements. This work proposes a simple heuristic for reducing these requirements, and an application to Model-Free Episodic Control (MFEC) is presented. Experiments on Atari games show that this heuristic successfully reduces MFEC computational demands while producing no significant loss of performance when conservative choices of hyperparameters are used. Consequently, episodic control becomes a more feasible option when dealing with reinforcement learning tasks.
Extracted statement — has model: MFEC
Research field: Computer Sciences
Title: Multi-Perspective Context Matching for Machine Comprehension
Abstract: Previous machine comprehension (MC) datasets are either too small to train end-to-end deep learning models, or not difficult enough to evaluate the ability of current MC techniques. The newly released SQuAD dataset alleviates these limitations, and gives us a chance to develop more realistic MC models. Based on this dataset, we propose a Multi-Perspective Context Matching (MPCM) model, which is an end-to-end system that directly predicts the answer beginning and ending points in a passage. Our model first adjusts each word-embedding vector in the passage by multiplying a relevancy weight computed against the question. Then, we encode the question and weighted passage by using bi-directional LSTMs. For each point in the passage, our model matches the context of this point against the encoded question from multiple perspectives and produces a matching vector. Given those matched vectors, we employ another bi-directional LSTM to aggregate all the information and predict the beginning and ending points. Experimental result on the test set of SQuAD shows that our model achieves a competitive result on the leaderboard.
Extracted statement — has model: MPCM
Research field: Computer Sciences
Title: Phrase-Based & Neural Unsupervised Machine Translation
Abstract: Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs. This work investigates how to learn to translate when having access to only large monolingual corpora in each language. We propose two model variants, a neural and a phrase-based model. Both versions leverage a careful initialization of the parameters, the denoising effect of language models and automatic generation of parallel data by iterative back-translation. These models are significantly better than methods from the literature, while being simpler and having fewer hyper-parameters. On the widely used WMT’14 English-French and WMT’16 German-English benchmarks, our models respectively obtain 28.1 and 25.2 BLEU points without using a single parallel sentence, outperforming the state of the art by more than 11 BLEU points. On low-resource languages like English-Urdu and English-Romanian, our methods achieve even better results than semi-supervised and supervised approaches leveraging the paucity of available bitexts. Our code for NMT and PBSMT is publicly available.
Extracted statement — has model: PBSMT
Research field: Computer Sciences
Title: Crowdsourced semantic annotation of scientific publications and tabular data in PDF
Abstract: Significant amounts of knowledge in science and technology have so far not been published as Linked Open Data but are contained in the text and tables of legacy PDF publications. Making such information available as RDF would, for example, provide direct access to claims and facilitate surveys of related work. A lot of valuable tabular information that till now only existed in PDF documents would also finally become machine understandable. Instead of studying scientific literature or engineering patents for months, it would be possible to collect such input by simple SPARQL queries. The SemAnn approach enables collaborative annotation of text and tables in PDF documents, a format that is still the common denominator of publishing, thus maximising the potential user base. The resulting annotations in RDF format are available for querying through a SPARQL endpoint. To incentivise users with an immediate benefit for making the effort of annotation, SemAnn recommends related papers, taking into account the hierarchical context of annotations in a novel way. We evaluated the usability of SemAnn and the usefulness of its recommendations by analysing annotations resulting from tasks assigned to test users and by interviewing them. While the evaluation shows that even few annotations lead to a good recall, we also observed unexpected, serendipitous recommendations, which confirms the merit of our low-threshold annotation support for the crowd.
Extracted statement — Input format: PDF
Research field: Computer Sciences
Title: Crowdsourced semantic annotation of scientific publications and tabular data in PDF
Abstract: Significant amounts of knowledge in science and technology have so far not been published as Linked Open Data but are contained in the text and tables of legacy PDF publications. Making such information available as RDF would, for example, provide direct access to claims and facilitate surveys of related work. A lot of valuable tabular information that till now only existed in PDF documents would also finally become machine understandable. Instead of studying scientific literature or engineering patents for months, it would be possible to collect such input by simple SPARQL queries. The SemAnn approach enables collaborative annotation of text and tables in PDF documents, a format that is still the common denominator of publishing, thus maximising the potential user base. The resulting annotations in RDF format are available for querying through a SPARQL endpoint. To incentivise users with an immediate benefit for making the effort of annotation, SemAnn recommends related papers, taking into account the hierarchical context of annotations in a novel way. We evaluated the usability of SemAnn and the usefulness of its recommendations by analysing annotations resulting from tasks assigned to test users and by interviewing them. While the evaluation shows that even few annotations lead to a good recall, we also observed unexpected, serendipitous recommendations, which confirms the merit of our low-threshold annotation support for the crowd.
Extracted statement — Output format: RDF
Research field: Computer Sciences
Title: Exploration by Random Network Distillation
Abstract: We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus is the error of a neural network predicting features of the observations given by a fixed randomly initialized neural network. We also introduce a method to flexibly combine intrinsic and extrinsic rewards. We find that the random network distillation (RND) bonus combined with this increased flexibility enables significant progress on several hard exploration Atari games. In particular we establish state of the art performance on Montezuma's Revenge, a game famously difficult for deep reinforcement learning methods. To the best of our knowledge, this is the first method that achieves better than average human performance on this game without using demonstrations or having access to the underlying state of the game, and occasionally completes the first level.
Extracted statement — has model: RND