Word embeddings are already well studied in the general domain, usually trained on large text corpora, and have been evaluated for example on word similarity and analogy tasks, but also as an input to downstream NLP processes. In contrast, in this work we explore the suitability of word embedding technologies in the specialized digital humanities domain. After training embedding models of various types on two popular fantasy novel book series, we evaluate their performance on two task types: term analogies, and word intrusion. To this end, we manually construct test datasets with domain experts. Among the contributions are the evaluation of various word embedding techniques on the different task types, with the findings that even embeddings trained on small corpora perform well for example on the word intrusion task. Furthermore, we provide extensive and high-quality datasets in digital humanities for further investigation, as well as the implementation to easily reproduce or extend the experiments.
The majority of terms in our analogy and word intrusion datasets are named entities denoted with proper names. In contrast to other types of noun phrases, proper names are not well studied in DS @cite_37 @cite_1 . In recent years, some works appeared that investigate the specifics of entities. @cite_24 predict discrete referential attributes of entities from DS models. @cite_35 study entities and concepts in DS, esp. the respective instance-of and hypernymy relations. While our datasets are not directed at specific types of relations, they contain instance-of analogy tasks. Herbelot @cite_37 contextualizes concepts with local entity information, and point out important characteristics of proper names (uniqueness, instantiation and individuality). @cite_1 predict semantic relations between entities collected from Freebase with the analogy method and a feed-forward neural network, and analyze factors for task difficulty, such as 1:n relations and relations with many instances.
@cite_24 @cite_35 @cite_37 @cite_1
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Disambiguating entity references by annotating them with unique ids from a catalog is a critical step in the enrichment of unstructured content. In this paper, we show that topic models, such as Latent Dirichlet Allocation (LDA) and its hierarchical variants, form a natural class of models for learning accurate entity disambiguation models from crowd-sourced knowledge bases such as Wikipedia. Our main contribution is a semi-supervised hierarchical model called Wikipedia-based Pachinko Allocation Model (WPAM) that exploits: (1) All words in the Wikipedia corpus to learn word-entity associations (unlike existing approaches that only use words in a small fixed window around annotated entity references in Wikipedia pages), (2) Wikipedia annotations to appropriately bias the assignment of entity labels to annotated (and co-occurring unannotated) words during model learning, and (3) Wikipedia's category hierarchy to capture co-occurrence patterns among entities. We also propose a scheme for pruning spurious nodes from Wikipedia's crowd-sourced category hierarchy. In our experiments with multiple real-life datasets, we show that WPAM outperforms state-of-the-art baselines by as much as 16 in terms of disambiguation accuracy. This thesis describes a novel statistical named-entity (i.e. “proper name”) recognition system known as “MENE” (Maximum Entropy Named Entity). Named entity (N.E.) recognition is a form of information extraction in which we seek to classify every word in a document as being a person-name, organization, location, date, time, monetary value, percentage, or “none of the above”. The task has particular significance for Internet search engines, machine translation, the automatic indexing of documents, and as a foundation for work on more complex information extraction tasks. Two of the most significant problems facing the constructor of a named entity system are the questions of portability and system performance. A practical N.E. system will need to be ported frequently to new bodies of text and even to new languages. The challenge is to build a system which can be ported with minimal expense (in particular minimal programming by a computational linguist) while maintaining a high degree of accuracy in the new domains or languages. MENE attempts to address these issues through the use of maximum entropy probabilistic modeling. It utilizes a very flexible object-based architecture which allows it to make use of a broad range of knowledge sources in making its tagging decisions. In the DARPA-sponsored MUC-7 named entity evaluation, the system displayed an accuracy rate which was well-above the median, demonstrating that it can achieve the performance goal. In addition, we demonstrate that the system can be used as a post-processing tool to enhance the output of a hand-coded named entity recognizer through experiments in which MENE improved on the performance of N.E. systems from three different sites. Furthermore, when all three external recognizers are combined under MENE, we are able to achieve very strong results which, in some cases, appear to be competitive with human performance. Finally, we demonstrate the trans-lingual portability of the system. We ported the system to two Japanese-language named entity tasks, one of which involved a new named entity category, “artifact”. Our results on these tasks were competitive with the best systems built by native Japanese speakers despite the fact that the author speaks no Japanese. Given a query consisting of a mention (name string) and a background document, entity disambiguation calls for linking the mention to an entity from reference knowledge base like Wikipedia. Existing studies typically use hand-crafted features to represent mention, context and entity, which is laborintensive and weak to discover explanatory factors of data. In this paper, we address this problem by presenting a new neural network approach. The model takes consideration of the semantic representations of mention, context and entity, encodes them in continuous vector space and effectively leverages them for entity disambiguation. Specifically, we model variable-sized contexts with convolutional neural network, and embed the positions of context words to factor in the distance between context word and mention. Furthermore, we employ neural tensor network to model the semantic interactions between context and mention. We conduct experiments for entity disambiguation on two benchmark datasets from TAC-KBP 2009 and 2010. Experimental results show that our method yields state-of-the-art performances on both datasets. Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB) (e.g., Wikipedia). In this paper, we propose a novel embedding method specifically designed for NED. The proposed method jointly maps words and entities into the same continuous vector space. We extend the skip-gram model by using two models. The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words. By combining contexts based on the proposed embedding with standard NED features, we achieved state-of-the-art accuracy of 93.1 on the standard CoNLL dataset and 85.2 on the TAC 2010 dataset.