In this paper, we present a novel algorithm that combines multi-context term embeddings using a neural classifier and we test this approach on the use case of corpus-based term set expansion. In addition, we present a novel and unique dataset for intrinsic evaluation of corpus-based term set expansion algorithms. We show that, over this dataset, our algorithm provides up to 5 mean average precision points over the best baseline.
Several works have addressed the term set expansion problem. We focus on corpus-based approaches based on the distributional similarity hypothesis @cite_5 . State-of-the-art techniques return the @math nearest neighbors around the seed terms as the expanded set, where terms are represented by their co-occurrence or embedding vectors in a training corpus according to different context types, such as linear window context @cite_12 @cite_18 @cite_17 @cite_14 @cite_3 @cite_19 , explicit lists @cite_23 @cite_0 @cite_4 , coordinational patterns @cite_0 and unary patterns @cite_17 @cite_7 . In this work, we generalize coordinational patterns, look at additional context types and combine multiple context-type embeddings.
@cite_18 @cite_14 @cite_4 @cite_7 @cite_3 @cite_0 @cite_19 @cite_23 @cite_5 @cite_12 @cite_17
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In this paper, we study the problem of expanding a set of given seed entities into a more complete set by discovering other entities that also belong to the same concept set. A typical example is to use "Canon" and "Nikon" as seed entities, and derive other entities (e.g., "Olympus") in the same concept set of camera brands. In order to discover such relevant entities, we exploit several web data sources, including lists extracted from web pages and user queries from a web search engine. While these web data are highly diverse with rich information that usually cover a wide range of the domains of interest, they tend to be very noisy. We observe that previously proposed random walk based approaches do not perform very well on these noisy data sources. Accordingly, we propose a new general framework based on iterative similarity aggregation, and present detailed experimental results to show that, when using general-purpose web data for set expansion, our approach outperforms previous techniques in terms of both precision and recall. Corpus-based set expansion (i.e., finding the “complete” set of entities belonging to the same semantic class, based on a given corpus and a tiny set of seeds) is a critical task in knowledge discovery. It may facilitate numerous downstream applications, such as information extraction, taxonomy induction, question answering, and web search. This paper is a short empirical study of the performance of centrality and classification based iterative term set expansion methods for distributional semantic models. Iterative term set expansion is an interactive process using distributional semantics models where a user labels terms as belonging to some sought after term set, and a system uses this labeling to supply the user with new, candidate, terms to label, trying to maximize the number of positive examples found. While centrality based methods have a long history in term set expansion, we compare them to classification methods based on the the Simple Margin method, an Active Learning approach to classification using Support Vector Machines. Examining the performance of various centrality and classification based methods for a variety of distributional models over five different term sets, we can show that active learning based methods consistently outperform centrality based methods. We present a corpus-based approach to the class expansion task. For a given set of seed entities we use co-occurrence statistics taken from a text collection to define a membership function that is used to rank candidate entities for inclusion in the set. We describe an evaluation framework that uses data from Wikipedia. The performance of our class extension method improves as the size of the text collection increases. Online social media yields a large-scale corpora which is fairly informative and sometimes includes many up-to-date entities. The challenging task of expanding entity sets on social media text is to extract more uncommon entities only using several seeds already in hand. In this paper, we present an approach which is able to find novel entities by expanding a small initial seed set on Twitter text. Our method first generates candidate sets on the basis of the semantic similarity feature. Then it jointly utilizes 2 text-based features and other 12 ones which carry social media specific information. With the scores on those features, a ranking model is learned by a supervised algorithm to synthetically score each candidate terms and then the final ranked list is taken as the target expanded set. We do experiments with 24 entity classes on the Twitter corpus and in the expanded sets there come many novel entities which have not been completely detected in previous researches. And the experimental results on the datasets of different years can perfectly consist with the objective law that fresh entities change as time goes on. Generating semantic lexicons semi-automatically could be a great time saver, relative to creating them by hand. In this paper, we present an algorithm for extracting potential entries for a category from an on-line corpus, based upon a small set of exemplars. Our algorithm finds more correct terms and fewer incorrect ones than previous work in this area. Additionally, the entries that are generated potentially provide broader coverage of the category than would occur to an individual coding them by hand. Our algorithm finds many terms not included within Wordnet (many more than previous algorithms), and could be viewed as an "enhancer" of existing broad-coverage resources. A key challenge of entity set expansion is that multifaceted input seeds can lead to significant incoherence in the result set. In this paper, we present a novel solution to handling multifaceted seeds by combining existing user-generated ontologies with a novel word-similarity metric based on skip-grams. By blending the two resources we are able to produce sparse word ego-networks that are centered on the seed terms and are able to capture semantic equivalence among words. We demonstrate that the resulting networks possess internally-coherent clusters, which can be exploited to provide non-overlapping expansions, in order to reflect different semantic classes of the seeds. Empirical evaluation against state-of-the-art baselines shows that our solution, EgoSet, is able to not only capture multiple facets in the input query, but also generate expansions for each facet with higher precision.