[PDF] Top 20 Aggregating Continuous Word Embeddings for Information Retrieval
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Aggregating Continuous Word Embeddings for Information Retrieval
... crete word occurrences such as PLSA/LDA and the proposed model for continuous word embed- ...each word for the particu- lar ...on word counts jointly perform the embedding of words and ... See full document
10
Specializing Word Embeddings (for Parsing) by Information Bottleneck
... Pre-trained word embeddings like ELMo and BERT contain rich syntactic and semantic in- formation, resulting in state-of-the-art perfor- mance on various ...variational information bottleneck (VIB) ... See full document
11
Improved Word Embeddings with Implicit Structure Information
... Distributed word representation is an efficient method for capturing semantic and syntactic word ...the continuous bag-of-words model for learning word representations efficiently by using ... See full document
10
Parameter Free Hierarchical Graph Based Clustering for Analyzing Continuous Word Embeddings
... adds information about the neighborhood relation between clusters, clusters of cluster and so ...important) word in the ...representative word to each of them. This word might for example be ... See full document
9
Mathematical Information Retrieval based on Type Embeddings and Query Expansion
... values for these query expansion models are also taken from Cummins et al. (2015). LMs with QE are used to determine how type-based QE performs in comparison to state-of-the-art term-based QE. Automatic QE using top ... See full document
12
Word embeddings and discourse information for Quality Estimation
... produces word embeddings using the Dis- tributed Skip-Gram or Continuous Bag-of-Words (CBOW) ...next word given the context of preceding words, a CBOW model predicts the word in the ... See full document
7
Urdu Word Embeddings
... a word by the distribution of other words around ...Mutual Information (PMI) measure which sought to quantify the degree of relatedness between two words by looking at how often they occurred together, ... See full document
5
Information Storage And Retrieval Systems Theory And Impl 2e Kowalski GJ (2002) pdf
... item. Retrieval ware uses a statistical algorithm but it does not include any corpora ...frequency information. It creates this information as it processes items, storing and modifying it for use as ... See full document
333
An Information Retrieval Model Based On Word Concept
... of word sense ambiguity. A word may have a lot of ...one word. Therefore whether we can determine the meaning of the word in a document will affect the accuracy of an IR ... See full document
8
Word Sense Disambiguation with Information Retrieval Technique
... on word sense disambiguation of Korean nouns with information retrieval ...target word is represented as ‘Static Sense Vector’ in word space, which is the centroid of the context ...use ... See full document
7
Better Word Embeddings by Disentangling Contextual n Gram Information
... Sentences are tokenized using the Stanford NLP library (Manning et al., 2014). All algorithms are implemented using a modified version of the fast- text (Bojanowski et al., 2017; Joulin et al., 2017) and sent2vec ... See full document
7
Word Sense Disambiguation Improves Information Retrieval
... sense information concerning the relative sense sim- ilarity ∆cos(t, q, d), where α is a positive parame- ter to control the impact of sense ...sense information is larger than 1; otherwise, it is less than ... See full document
10
Querying Databases Privately A New Approach To Private Information Retrieval Asanov D (2004) pdf
... Private Information Retrieval problem is due to a fun- damental constraint of conventional ...“Private Information Retrieval” problem (PIR), also alternatively called the “querying databases ... See full document
128
What do we need to know about an unknown word when parsing German
... Compound Embeddings In a neural parsing system, each word is repre- sented by a vector stored in a lookup ...the word lookup table by character- based embeddings (Ling et ...each word ... See full document
7
Relational Word Embeddings
... which word vectors capture semantic properties which has shown to strongly correlate with performance in downstream tasks such as text categorization and sentiment analy- ...of word vectors, and in ... See full document
11
Morphological Word Embeddings
... each word is to its neighbors, where distance is measured in the Hamming distance between morphological ...Morph-LBL embeddings generally encode mor- phology better than the ... See full document
6
Derivational Morphological Relations in Word Embeddings
... which embeddings of the source and the target words are shared in a common vector space, we use two sepa- rated dictionaries (each containing 25,000 word ...the word vectors are not influenced by any ... See full document
8
Multimedia Information Storage And Retrieval Techniques And Technologies Tse PKC (2008) pdf
... The region based constraint allocation method is efficient. It increases the efficiency in accessing multimedia objects. It provides an upper bound on the maximum seek distance of disk requests to limit the ... See full document
421
Interactive Information Retrieval In Digital Environments Iris Xie (2008) pdf
... the information-seeking ...solicit information about the cognitive processes of a user’s internal ...insightful information about human problem-solving processes (Yang, ...providing ... See full document
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