Titlebook: Information Retrieval Technology; 12th Asia Informatio Shaoping Ma,Ji-Rong Wen,Xin Zhao Conference proceedings 2016 Springer International

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書目名稱Information Retrieval Technology影響因子(影響力)




書目名稱Information Retrieval Technology影響因子(影響力)學(xué)科排名




書目名稱Information Retrieval Technology網(wǎng)絡(luò)公開度




書目名稱Information Retrieval Technology網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Information Retrieval Technology被引頻次




書目名稱Information Retrieval Technology被引頻次學(xué)科排名




書目名稱Information Retrieval Technology年度引用




書目名稱Information Retrieval Technology年度引用學(xué)科排名




書目名稱Information Retrieval Technology讀者反饋




書目名稱Information Retrieval Technology讀者反饋學(xué)科排名

exorbitant

Detecting Synonymous Predicates from Online Encyclopedia with Rich Featuresure selection and their effectiveness analysis. We not only leverage different resources such as Wikipedia, Freebase, but also use different word embeddings to represent predicates. The experimental results indicate that wikitext defined by Wikipedia and predicate surface form are most useful features.

BAIL

原始

nostrum

harrow

Learning a Semantic Space of Web Search via Session Datassion data, where a search session is regarded as an instantiation of an information need and keeps the interaction information of queries and URLs. Vector learning is done on a large query log from a search engine, and the efficacy of learnt vectors is examined in a few tasks.

ABYSS

TLINE: Scalable Transductive Network Embeddingormance in node classification task, we test our methods on two real world network datasets, which are Citeseer and DBLP. The experimental result indicates that TLINE outperforms state-of-the-art baselines and is suitable for large-scale networks.

dissolution

debase

Constraining Word Embeddings by Prior Knowledge – Application to Medical Information Retrievale). In this paper, we leverage the existing knowledge (word relations) in the medical domain to constrain word embeddings using the principle that related words should have similar embeddings. The resulting constrained word embeddings are used to rerank documents, showing superior effectiveness to unsupervised word embeddings.

Lipoprotein