Named entities are usually composable and extensible. Typical examples are names of symptoms and diseases in medical areas. To distinguish these entities from general entities, we name them . In this paper, we present an attention-based Bi-GRU-CapsNet model to detect hypernymy relationship between compound entities. Our model consists of several important components. To avoid the out-of-vocabulary problem, English words or Chinese characters in compound entities are fed into the bidirectional gated recurrent units. An attention mechanism is designed to focus on the differences between the two compound entities. Since there are some different cases in hypernymy relationship between compound entities, capsule network is finally employed to decide whether the hypernymy relationship exists or not. Experimental results demonstrate
Distributional methods represent a category of methods which use the distributional representations of entity pairs, i.e. the contexts with which each entity occurs separately in the corpus. Some studies follow the distributional inclusion hypothesis @cite_15 @cite_16 . Some studies assume the hypernyms of an entity co-occur with it frequently @cite_11 . Recently, with the popularity of word embeddings, most focus has shifted towards supervised distributional methods @cite_18 @cite_9 @cite_12 . In contrast with path-based methods, distributional methods do not require co-occurrence of an entity pair, and these methods usually perform better than path-based methods. However, many times a compound entity is more like a sentence rather than a word. When representing compound entities with distributional vectors through their contextual information, these methods often suffer from the Out-Of-Vocabulary (OOV) problem because of the absence of compound entities in the corpus.
@cite_18 @cite_9 @cite_15 @cite_16 @cite_12 @cite_11
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Distributional approaches to meaning acquisition utilize distributional properties of linguistic entities as the building blocks of semantics. In doing so, they rely fundamentally on a set of assumptions about the nature of language and meaning referred to as the distributional hypothesis. This hypothesis is often stated in terms like “words which are similar in meaning occur in similar contexts” (Rubenstein & Goodenough, 1965); “words with similar meanings will occur with similar neighbors if enough text material is available” (Schutze & Pedersen, 1995); “a representation that captures much of how words are used in natural context will capture much of what we mean by meaning” (Landauer & Dumais, 1997); and “words that occur in the same contexts tend to have similar meanings” (Pantel, 2005), just to quote a few representative examples. The general idea behind the distributional hypothesis seems clear enough: there is a correlation between distributional similarity and meaning similarity, which allows us to utilize the former in order to estimate the latter. However, one can pose two very basic questions concerning the distributional hypothesis. The first is what kind of distributional properties we should look for, and what — if any — the differences are between different kinds of distributional properties. Looking at algorithms for distributional meaning acquisition we can discern two distinct approaches. The first is to build distributional profiles for words based on which other words surround them, as exemplified by Schutze (1992) and the Hyperspace Analogue to Language (HAL) model (Lund, Burgess, & Atchley, 1995). The second is to build distributional profiles based on in which text regions words occur, as exemplified by the Latent Semantic Analysis (LSA) model (Landauer & Dumais, 1997). These approaches are often treated as functionally equivalent when it comes to representing meaning similarities, despite the fact that they are based on different types of distributional raw materials. The second question is in what sense it is meaning that is conveyed by distributional patterns. Proponents of distributional methods often seem comfortable to ascribe meaning to distributional representations without explaining Extracting relations from text corpora is an important task with wide applications. However, it becomes particularly challenging when focusing on weakly-supervised relation extraction, that is, utilizing a few relation instances (i.e., a pair of entities and their relation) as seeds to extract from corpora more instances of the same relation. Existing distributional approaches leverage the corpus-level co-occurrence statistics of entities to predict their relations, and require large amount of labeled instances to learn effective relation classifiers. Alternatively, pattern-based approaches perform boostrapping or apply neural networks to model the local contexts, but still rely on large amount of labeled instances to build reliable models. In this paper, we study the integration of distributional and pattern-based methods in a weakly-supervised setting such that the two kinds of methods can provide complementary supervision for each other to build an effective, unified model. We propose a novel framework with a distributional module and a pattern module. During training, the distributional module helps the pattern module discriminate between the informative patterns and other patterns, and the pattern module generates some highly-confident instances to improve the distributional module. The whole framework can be effectively optimized by iterating between improving the pattern module and updating the distributional module. We conduct experiments on two tasks: knowledge base completion and corpus-level relation extraction. Experimental results prove the effectiveness of our framework over many competitive baselines. Research into corpus-based semantics has focused on the development of ad hoc models that treat single tasks, or sets of closely related tasks, as unrelated challenges to be tackled by extracting different kinds of distributional information from the corpus. As an alternative to this "one task, one model" approach, the Distributional Memory framework extracts distributional information once and for all from the corpus, in the form of a set of weighted word-link-word tuples arranged into a third-order tensor. Different matrices are then generated from the tensor, and their rows and columns constitute natural spaces to deal with different semantic problems. In this way, the same distributional information can be shared across tasks such as modeling word similarity judgments, discovering synonyms, concept categorization, predicting selectional preferences of verbs, solving analogy problems, classifying relations between word pairs, harvesting qualia structures with patterns or example pairs, predicting the typical properties of concepts, and classifying verbs into alternation classes. Extensive empirical testing in all these domains shows that a Distributional Memory implementation performs competitively against task-specific algorithms recently reported in the literature for the same tasks, and against our implementations of several state-of-the-art methods. The Distributional Memory approach is thus shown to be tenable despite the constraints imposed by its multi-purpose nature. Distributional semantic methods to approximate word meaning with context vectors have been very successful empirically, and the last years have seen a surge of interest in their compositional extension to phrases and sentences. We present here a new model that, like those of (2010) and Baroni and Zamparelli (2010), closely mimics the standard Montagovian semantic treatment of composition in distributional terms. However, our approach avoids a number of issues that have prevented the application of the earlier linguistically-motivated models to full-fledged, real-life sentences. We test the model on a variety of empirical tasks, showing that it consistently outperforms a set of competitive rivals. 1 Compositional distributional semantics The research of the last two decades has established empirically that distributional vectors for words obtained from corpus statistics can be used