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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Distributional methods have proven to excel at capturing fuzzy, graded aspects of meaning (Italy is more similar to Spain than to Germany). In contrast, it is difficult to extract the values of more specific attributes of word referents from distributional representations, attributes of the kind typically found in structured knowledge bases (Italy has 60 million inhabitants). In this paper, we pursue the hypothesis that distributional vectors also implicitly encode referential attributes. We show that a standard supervised regression model is in fact sufficient to retrieve such attributes to a reasonable degree of accuracy: When evaluated on the prediction of both categorical and numeric attributes of countries and cities, the model consistently reduces baseline error by 30 , and is not far from the upper bound. Further analysis suggests that our model is able to “objectify” distributional representations for entities, anchoring them more firmly in the external world in measurable ways. The authors have received funding from DFG (SFB 732, project B9). and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 715154; AMORE), as well as under the Marie Sklodowska-Curie grant agreement No 655577 (LOVe). This paper investigates the representation of proper names in distributional semantics. We define three properties we expect names to display: uniqueness (being a unique entity), instantiation (being an instance of a relevant kind) and individuality (being separable from the subspace of concepts). We show that taking a standard distribution as the representation of a name does not satisfy those properties particularly well. We propose an alternative method to compute a name vector, which relies on re-weighting the distribution of the appropriate named entity type – in effect, producing an individual out of a kind. We illustrate the behaviour of such representations over some characters from two English novels. Comunicacio presentada a la 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), celebrat els dies 3 i 4 d'agost de 2017 a Vancouver, Canada.