4,885 research outputs found
Corpora and evaluation tools for multilingual named entity grammar development
We present an effort for the development of multilingual named entity grammars in a unification-based finite-state formalism (SProUT). Following an extended version of the MUC7 standard, we have developed Named Entity Recognition grammars for German, Chinese, Japanese, French, Spanish, English, and Czech. The grammars recognize person names, organizations, geographical locations, currency, time and date expressions. Subgrammars and gazetteers are shared as much as possible for the grammars of the different languages. Multilingual corpora from the business domain are used for grammar development and evaluation. The annotation format (named entity and other linguistic information) is described. We present an evaluation tool which provides detailed statistics and diagnostics, allows for partial matching of annotations, and supports user-defined mappings between different annotation and grammar output formats
A Survey of Paraphrasing and Textual Entailment Methods
Paraphrasing methods recognize, generate, or extract phrases, sentences, or
longer natural language expressions that convey almost the same information.
Textual entailment methods, on the other hand, recognize, generate, or extract
pairs of natural language expressions, such that a human who reads (and trusts)
the first element of a pair would most likely infer that the other element is
also true. Paraphrasing can be seen as bidirectional textual entailment and
methods from the two areas are often similar. Both kinds of methods are useful,
at least in principle, in a wide range of natural language processing
applications, including question answering, summarization, text generation, and
machine translation. We summarize key ideas from the two areas by considering
in turn recognition, generation, and extraction methods, also pointing to
prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of
Informatics, Athens University of Economics and Business, Greece, 201
Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure
It has been established that incorporating word cluster features derived from large unlabeled corpora can significantly improve prediction of linguistic structure. While previous work has focused primarily on English, we extend these results to other languages along two dimensions. First, we show that these results hold true for a number of languages across families. Second, and more interestingly, we provide an algorithm for inducing cross-lingual clusters and we show that features derived from these clusters significantly improve the accuracy of cross-lingual structure prediction. Specifically, we show that by augmenting direct-transfer systems with cross-lingual cluster features, the relative error of delexicalized dependency parsers, trained on English treebanks and transferred to foreign languages, can be reduced by up to 13%. When applying the same method to direct transfer of named-entity recognizers, we observe relative improvements of up to 26%
GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts
In the context of the rapid development of large language models, we have
meticulously trained and introduced the GujiBERT and GujiGPT language models,
which are foundational models specifically designed for intelligent information
processing of ancient texts. These models have been trained on an extensive
dataset that encompasses both simplified and traditional Chinese characters,
allowing them to effectively handle various natural language processing tasks
related to ancient books, including but not limited to automatic sentence
segmentation, punctuation, word segmentation, part-of-speech tagging, entity
recognition, and automatic translation. Notably, these models have exhibited
exceptional performance across a range of validation tasks using publicly
available datasets. Our research findings highlight the efficacy of employing
self-supervised methods to further train the models using classical text
corpora, thus enhancing their capability to tackle downstream tasks. Moreover,
it is worth emphasizing that the choice of font, the scale of the corpus, and
the initial model selection all exert significant influence over the ultimate
experimental outcomes. To cater to the diverse text processing preferences of
researchers in digital humanities and linguistics, we have developed three
distinct categories comprising a total of nine model variations. We believe
that by sharing these foundational language models specialized in the domain of
ancient texts, we can facilitate the intelligent processing and scholarly
exploration of ancient literary works and, consequently, contribute to the
global dissemination of China's rich and esteemed traditional culture in this
new era.Comment: 22pages,0 figur
Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation
A type description is a succinct noun compound which helps human and machines
to quickly grasp the informative and distinctive information of an entity.
Entities in most knowledge graphs (KGs) still lack such descriptions, thus
calling for automatic methods to supplement such information. However, existing
generative methods either overlook the grammatical structure or make factual
mistakes in generated texts. To solve these problems, we propose a
head-modifier template-based method to ensure the readability and data fidelity
of generated type descriptions. We also propose a new dataset and two automatic
metrics for this task. Experiments show that our method improves substantially
compared with baselines and achieves state-of-the-art performance on both
datasets.Comment: ACL 201
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