Уикипедия

We explore the use of Wikipedia as external knowledge to improve named entity recog- nition (NER). Our method retrieves the cor- responding Wikipedia entry for each can- didate word sequence …...

. Named entity translation plays an important role in many applications, such as information retrieval and machine translation. In this paper, we focus on translating person names, the most common …...

We propose a new integrated approach based on Markov logic networks (MLNs), an effective com- bination of probabilistic graphical models and first- order logic for statistical relational learning, to ex- …...

In this paper, we describe a system by which the multilingual characteristics of Wikipedia can be utilized to annotate a large corpus of text with Named Entity Recognition (NER) tags …...

Statistical named entity recognisers require costly hand-labelled training data and, as a result, most existing corpora are small. We exploit Wikipedia to create a massive corpus of named entity annotated …...

The use of lexical semantic knowledge in information retrieval has been a field of ac- tive study for a long time. Collaborative knowledge bases like Wikipedia and Wik- tionary, which …...

There have been considerable attempts to incorporate semantic knowledge into coreference resolution systems: different knowledge sources such as WordNet and Wikipedia have been used to boost the per- formance. In …...

Combining information extraction sys- tems yields significantly higher quality re- sources than each system in isolation. In this paper, we generalize such a mixing of sources and features in a …...

Parsers are often the bottleneck for data ac- quisition, processing text too slowly to be widely applied. One way to improve the efficiency of parsers is to construct more confident …...

This paper presents a new method of devel- oping a large-scale hyponymy relation data- base by combining Wikipedia and other Web documents. We attach new words to the hy- ponymy …...

A set of labeled classes of instances is ex- tracted from text and linked into an exist- ing conceptual hierarchy. Besides a signif- icant increase in the coverage of the …...

AbstractDetermining semantic relatedness between words or concepts is a fundamental process to many Natural Language Processing applications. Approaches for this task typically make use of knowledge resources such as WordNet …...

In this paper we present BabelNet – a very large, wide-coverage multilingual se- mantic network. The resource is automat- ically constructed by means of a method- ology that integrates lexicographic …...

We present a novel approach to relation ex- traction that integrates information across doc- uments, performs global inference and re- quires no labelled text. In particular, we tackle relation extraction …...

Entity linking refers entity mentions in a document to their representations in a knowledge base (KB). In this paper, we propose to use additional information sources from Wikipedia to find …...

We propose a novel self-training method for a parser which uses a lexicalised gram- mar and supertagger, focusing on increas- ing the speed of the parser rather than its accuracy. …...

AbstractIn this paper we present a new approach for obtaining the terminology of a given domain using the category and page structures of the Wikipedia in a language independent way. …...

The Semeval task 5 was an opportunity for experimenting with the key term ex- traction module of GROBID, a system for extracting and generating bibliographical information from technical and scientific …...

. Wikipedia is a potentially very useful source of information, but intuitively it is difficult to have confidence in the quality of an encyclopedia that anyone can modify. One aspect …...

AbstractReflecting the rapid growth of science, technology, and culture, it has become common practice to consult tools on the World Wide Web for various terms. Existing search engines provide an …...

Ambiguity of entity mentions and con- cept references is a challenge to mining text beyond surface-level keywords. We describe an effective method of disambi- guating surface forms and resolving them …...

Name ambiguity problem has raised urgent demands for efficient, high-quality named ent- ity disambiguation methods. In recent years, the increasing availability of large-scale, rich semantic knowledge sources (such as Wikipe- …...

Representing documents by vectors that are independent of language enhances machine translation and multilingual text categoriza- tion. We use discriminative training to create a projection of documents from multiple lan- …...

This paper presents the participation of the University of Bari (UBA) at the SemEval- 2010 Cross-Lingual Lexical Substitution Task. The goal of the task is to substi- tute a word …...

AbstractThis article presents a new freely available trilingual corpus (Catalan, Spanish, English) that contains large portions of the Wikipedia and has been automatically enriched with linguistic information. To our knowledge, …...

In internet advertising, negative key phrases are used in order to exclude the display of an advertisement to non-target audience. We describe a method for auto- matically identifying negative key …...

Measuring semantic relatedness between words or concepts is a crucial process to many Natural Language Processing tasks. Exiting methods exploit semantic evidence from a single knowledge source, and are predominantly …...

Disambiguating concepts and entities in a con- text sensitive way is a fundamental problem in natural language processing. The compre- hensiveness of Wikipedia has made the on- line encyclopedia an …...

This paper describes the design, develop- ment and evaluation of a machine transla- tion system between Dutch and Afrikaans developed over a period of around a month and a half. …...

This paper describes a novel approach to the semantic relation detection problem. Instead of relying only on the training instances for a new relation, we leverage the knowledge learned from …...

AbstractIn this paper we propose a method to build fine-grained subjectivity lexicons including nouns, verbs and adjectives. The method, which is applied for Dutch, is based on the comparison of …...

AbstractWe present a framework for the acquisition of sentential paraphrases based on crowdsourcing. The proposed method maximizes the lexical divergence between an original sentence s and its valid paraphrases by …...

AbstractReflecting the rapid growth of science, technology, and culture, it has become common practice to consult tools on the World Wide Web for various terms. Existing search engines provide an …...

In this paper, we design a processing flow to produce linked data in articles, providing anchor- based term’s additional information and related terms in different languages (English to Chi- nese). …...

We explore the interplay of knowledge and structure in co-reference resolution. To inject knowledge, we use a state-of-the-art system which cross-links (or “grounds”) expressions in free text to Wikipedia. We …...

Most set expansion algorithms assume to acquire new instances of different seman- tic categories independently even when we have seed instances of multiple semantic cat- egories. However, in the setting …...

Text prediction is the task of suggesting text while the user is typing. Its main aim is to reduce the number of keystrokes that are needed to type a text. …...

AbstractWe present UBY-LMF, an LMF-based model for large-scale, heterogeneous multilingual lexical-semantic resources (LSRs). UBY-LMF allows the standardization of LSRs down to a fine-grained level of lexical information by employing a …...

AbstractA scientific vocabulary is a set of terms that designate scientific concepts. This set of lexical units can be used in several applications ranging from the development of terminological dictionaries …...

AbstractIn this paper, we propose a simple methodology for building or extending wordnets using easily extractible lexical knowledge from Wiktionary and Wikipedia. This method relies on a large multilingual translation/synonym …...

In this paper, we propose a weakly supervised system, YouCat, for categorizing Youtube videos into different genres like Comedy, Horror, Romance, Sports and Technology The system takes a Youtube video …...

Bilingual lexicons are central components of machine translation and cross-lingual infor- mation retrieval systems. Their manual con- struction requires strong expertise in both lan- guages involved and is a costly …...

Wikipedia articles are annotated by volunteer contributors with numerous links that connect words and phrases to relevant titles. Links to general senses of a word are used concur- rently with …...

We introduce Peripheral Diversity (PD) as a knowledge-based approach to achieve multi- lingual Word Sense Disambiguation (WSD). PD exploits the frequency and diverse use of word senses in semantic subgraphs …...

Web search can be enhanced in powerful ways if to- ken spans in Web text are annotated with disambiguated entities from large catalogs like Freebase. Entity anno- tators need to …...

In this paper we describe KnCe2013-CORE, a system to compute the semantic similarity of two short text snippets. The system com- putes a number of features which are gath- ered …...

Measuring term informativeness is a funda- mental NLP task. Existing methods, mostly based on statistical information in corpora, do not actually measure informativeness of a term with regard to its …...

This article reports on mass experiments sup- porting the idea that data extracted from strongly comparable corpora may successfully be used to build statistical machine translation systems of reasonable translation …...

Wikipedia’s link structure is a valuable resource for natural language processing tasks, but only a fraction of the concepts mentioned in each article are annotated with hyperlinks. In this paper, …...

AbstractIn this paper we present CROMER (CROss-document Main Events and entities Recognition), a novel tool to manually annotate event and entity coreference across clusters of documents. The tool has been …...

AbstractThis paper introduces a distributional thesaurus and sense clusters computed on the complete Google Syntactic N-grams, which is extracted from Google Books, a very large corpus of digitized books published …...

Although hyperlinks enhance the utility of Wikipedia, embedding them in articles imposes a burden on contributors. To alleviate this burden as well as enrich hyperlinks in Wikipedia articles, we propose …...

Statistical parsers trained on labeled data suffer from sparsity, both grammatical and lexical. For parsers based on strongly lexicalized grammar formalisms (such as CCG, which has complex lexical cate- gories …...

In this paper, we present a machine learning approach for word sense alignment (WSA) which combines distances between senses in the graph representations of lexical-semantic resources with gloss similarities. In …...

AbstractThe research focuses on automatic construction of multi-lingual domain-ontologies, i.e., creating a DAG (directed acyclic graph) consisting of concepts relating to a specific domain and the relations between them. The …...

AbstractCurrent supervised parsers are limited by the size of their labelled training data, making improving them with unlabelled data an important goal. We show how a state-of-the-art CCG parser can …...

AbstractIn this paper we present the mapping between WordNet domains and WordNet topics, and the emergent Wikipedia categories. This mapping leads to a coarse alignment between WordNet and Wikipedia, useful …...

In this paper, we investigate the differ- ence between word and sense similarity measures and present means to convert a state-of-the-art word similarity measure into a sense similarity measure. In …...

Most previous work in information extraction from text has focused on named-entity recognition, entity linking, and relation extraction. Less attention has been paid given to extracting the temporal scope for …...

Document enrichment is the task of retrieving additional knowledge from external resource over what is available through source document. This task is essential because of the phenomenon that text is …...

AbstractThe coverage and quality of conceptual information contained in lexical semantic resources is crucial for many tasks in natural language processing. Automatic alignment of complementary resources is one way of …...

Current approaches to cross-lingual informa- tion retrieval (CLIR) rely on standard retrieval models into which query translations by sta- tistical machine translation (SMT) are inte- grated at varying degree. In …...

This paper presents our contribution to the SemEval-2015 Task 7. The task was subdi- vided into three subtasks that consisted of au- tomatically identifying the time period when a piece …...

Wikipedia is a resource of choice ex- ploited in many NLP applications, yet we are not aware of recent attempts to adapt coreference resolution to this resource. In this work, …...

AbstractThe ever increasing importance of machine learning in Natural Language Processing is accompanied by an equally increasing need in large-scale training and evaluation corpora. Due to its size, its openness …...

AbstractThis paper presents how an state-of-the-art SMT system is enriched by using an extra in-domain parallel corpora extracted from Wikipedia. We collect corpora from parallel titles and from parallel fragments …...

AbstractLanguage models are used in applications as diverse as speech recognition, optical character recognition and information retrieval. They are used to predict word appearance, and to weight the importance of …...

AbstractWikipedia has been increasingly used as a knowledge base for open-domain Named Entity Linking and Disambiguation. In this task, a dictionary with entity surface forms plays an important role in …...

The state-of-the-art named entity recognition (NER) systems are statistical machine learn- ing models that have strong generalization ca- pability (i.e., can recognize unseen entities that do not appear in training …...

AbstractWikipedia has become one of the most popular resources in natural language processing and it is used in quantities of applications. However, Wikipedia requires a substantial pre-processing step before it …...

AbstractRecent work in NLP has attempted to deal with low-resource languages but still assumed a resource level that is not present for most languages, e.g., the availability of Wikipedia in …...

AbstractWe describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to …...

AbstractThis paper presents a system that participated in SemEval 2017 Task 10 (subtask A and subtask B): Extracting Keyphrases and Relations from Scientific Publications (Augenstein et al., 2017). Our proposed …...

AbstractThis paper proposes to tackle open-domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article. This …...

AbstractThis paper describes Sew-Embed, our language-independent approach to multilingual and cross-lingual semantic word similarity as part of the SemEval-2017 Task 2. We leverage the Wikipedia-based concept representations developed by Raganato …...

We present a detailed evaluation and analysis of neural sequence-to-sequence models for text simplification on two distinct datasets: Wikipedia and Newsela. We employ both human and automatic evaluation to investigate …...

Hierarchical Text Segmentation is the task of building a hierarchical structure out of text to reflect its sub-topic hierarchy. Current text segmentation approaches are based upon using lexical and/or syntactic …...

AbstractWikipedia provides an invaluable source of parallel multilingual data, which are in high demand for various sorts of linguistic inquiry, including both theoretical and practical studies. We introduce a novel …...

AbstractThis paper describes the participation of ELiRF-UPV team at task 10, Capturing Discriminative Attributes, of SemEval-2018. Our best approach consists of using ConceptNet, Wikipedia and NumberBatch embeddings in order to …...

Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these …...

AbstractExtreme classification is a classification task on an extremely large number of labels (tags). User generated labels for any type of online data can be sparing per individual user but …...

AbstractGrammatical Error Correction (GEC) has been recently modeled using the sequence-to-sequence framework. However, unlike sequence transduction problems such as machine translation, GEC suffers from the lack of plentiful parallel data. …...

AbstractThis paper treats gender bias latent in word embeddings. Previous mitigation attempts rely on the operationalisation of gender bias as a projection over a linear subspace. An alternative approach is …...

AbstractWhen reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entities. …...

AbstractWikipedia can easily be justified as a behemoth, considering the sheer volume of content that is added or removed every minute to its several projects. This creates an immense scope, …...

AbstractMuch previous work on geoparsing has focused on identifying and resolving individual toponyms in text like Adrano, S.Maria di Licodia or Catania. However, geographical locations occur not only as individual …...

AbstractPre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as …...

AbstractThis work presents a replication study of Exploring Neural Text Simplification Models (Nisioi et al., 2017). We were able to successfully replicate and extend the methods presented in the original …...

AbstractThis paper proposes a novel framework for digital curation of Web corpora in order to provide robust estimation of their parameters, such as their composition and the lexicon. In recent …...

AbstractMachine reading is an ambitious goal in NLP that subsumes a wide range of text understanding capabilities. Within this broad framework, we address the task of machine reading the time …...

AbstractThe success of a text simplification system heavily depends on the quality and quantity of complex-simple sentence pairs in the training corpus, which are extracted by aligning sentences between parallel …...

AbstractThis paper describes the NICT Kyoto submission for the WMT’20 Quality Estimation (QE) shared task. We participated in Task 2: Word and Sentence-level Post-editing Effort, which involved Wikipedia data and …...

AbstractAdvances in language modeling architectures and the availability of large text corpora have driven progress in automatic text generation. While this results in models capable of generating coherent texts, it …...

AbstractBuilding predictive models for information extraction from text, such as named entity recognition or the extraction of semantic relationships between named entities in text, requires a large corpus of annotated …...

AbstractWe propose a new multilingual language model benchmark that is composed of 40+ languages spanning several scripts and linguistic families. With around 40 billion characters, we hope this new resource …...

AbstractWe present our work on aligning the Unified Medical Language System (UMLS) to Wikipedia, to facilitate manual alignment of the two resources. We propose a cross-lingual neural reranking model to …...

AbstractIn this paper, we propose CHOLAN, a modular approach to target end-to-end entity linking (EL) over knowledge bases. CHOLAN consists of a pipeline of two transformer-based models integrated sequentially to …...

AbstractThe main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the …...

AbstractThere is a shortage of high-quality corpora for South-Slavic languages. Such corpora are useful to computer scientists and researchers in social sciences and humanities alike, focusing on numerous linguistic, content …...

AbstractWe present a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in a target language. The corpus covers twelve language pairs and directions for …...

AbstractThe number of senses of a given word, or polysemy, is a very subjective notion, which varies widely across annotators and resources. We propose a novel method to estimate polysemy …...

AbstractKnowledge-based visual question answering (VQA) requires answering questions with external knowledge in addition to the content of images. One dataset that is mostly used in evaluating knowledge-based VQA is OK-VQA, …...

In this paper, we address the problem of auto- matic academic curriculum generation. A curricu- lum outlines definitive topics with their sub-topics and enables teachers and students to form an …...

AbstractThe recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from original work. However, the role …...

AbstractExtreme multi-label text classification (XMTC) is the task of finding the most relevant subset labels from an extremely large-scale label collection. Recently, some deep learning models have achieved state-of-the-art results …...

AbstractThis paper describes a resource of Wikipedias in 31 languages categorized into Extended Named Entity (ENE), which has 219 fine-grained NE categories. We first categorized 920 K Japanese Wikipedia pages …...

AbstractAutomating Cross-lingual Science Journalism (CSJ) aims to generate popular science summaries from English scientific texts for non-expert readers in their local language. We introduce CSJ as a downstream task of …...

AbstractThe MultiCoNER II task aims to detect complex, ambiguous, and fine-grained named entities in low-context situations and noisy scenarios like the presence of spelling mistakes and typos for multiple languages. …...

AbstractThe MultiCoNER II shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of context makes the recognition of …...