Learning deep structured semantic models for web search using clickthrough data

Huang, Po-Sen; He, Xiaodong; Gao, Jianfeng; Deng, Li; Acero, Alex; Heck, Larry

doi:10.1145/2505515.2505665

Cited by 1,562 publications

(1,205 citation statements)

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“…In a separate line of research, deep learning based techniques have been proposed for semantic understanding (Mesnil et al, 2013;Huang et al, 2013;Shen et al, 2014b;Salakhutdinov and Hinton, 2009;Tur et al, 2012). We adapt the work of (Huang et al, 2013;Shen et al, 2014b) for measuring the semantic distance between a question and relational triples in the KB as the core component of our semantic parsing approach.…”

Section: Related Workmentioning

confidence: 99%

“…In our model, we leverage the word hashing technique proposed in (Huang et al, 2013) where we first represent a word by a lettertrigram count vector. For example, given a word (e.g., cat), after adding word boundary symbols (e.g., #cat#), the word is segmented into a sequence of letter-n-grams (e.g., letter-trigrams: #-c-a, c-a-t, a-t-#).…”

Section: Convolutional Neural Network Based Semantic Modelmentioning

confidence: 99%

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Semantic Parsing for Single-Relation Question Answering

Yih

Meek

2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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118

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We develop a semantic parsing framework based on semantic similarity for open domain question answering (QA). We focus on single-relation questions and decompose each question into an entity mention and a relation pattern. Using convolutional neural network models, we measure the similarity of entity mentions with entities in the knowledge base (KB) and the similarity of relation patterns and relations in the KB. We score relational triples in the KB using these measures and select the top scoring relational triple to answer the question. When evaluated on an open-domain QA task, our method achieves higher precision across different recall points compared to the previous approach, and can improve F 1 by 7 points.

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Section: Related Workmentioning

confidence: 99%

Section: Convolutional Neural Network Based Semantic Modelmentioning

confidence: 99%

Semantic Parsing for Single-Relation Question Answering

Yih

Meek

2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Self Cite

321

118

View full text Add to dashboard Cite

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“…In vision, abstractions can include object detection (Girshick et al, 2014;Girshick, 2015;Ren et al, 2015), classification (Krizhevsky et al, 2012;Simonyan and Zisserman, 2014;Szegedy et al, 2015), and semantic understanding (Huang et al, 2013) using convolution neural networks (LeCun and Bengio, 1995). Inspired by the hierarchical architecture of the human visual cortex (Hubel and Wiesel, 1962), architectures for multiple convolution-pooling layers have been proposed and are being used in different machine learning tasks.…”

Section: Trends In the Development Of Ai Technology Applications For mentioning

confidence: 99%

Current trends in the development of intelligent unmanned autonomous systems

Zhang

et al. 2017

Frontiers Inf Technol Electronic Eng

111

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Intelligent unmanned autonomous systems are some of the most important applications of artificial intelligence (AI). The development of such systems can significantly promote innovation in AI technologies. This paper introduces the trends in the development of intelligent unmanned autonomous systems by summarizing the main achievements in each technological platform. Furthermore, we classify the relevant technologies into seven areas, including AI technologies, unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned workshops/intelligent plants. Current trends and developments in each area are introduced.

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“…We train a regression forest model (Meinshausen, 2006) and use the following features: monolingual word aligner (Sultan et al, 2014), DSSM model (Huang et al, 2013), word embedding composition (max, sum, idf-sum) with GloVe , n-gram overlap, subsequence matching, PairingWords (Han et al, 2013), word mover's distance (Kusner et al, 2015).…”

Section: Faq Search For Customer Qa Pairsmentioning

confidence: 99%

Proceedings of ACL 2017, System Demonstrations

Zhang

2017

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We present the first open-source tool for annotating morphosyntactic tense, mood and voice for English, French and German verbal complexes. The annotation is based on a set of language-specific rules, which are applied on dependency trees and leverage information about lemmas, morphological properties and POS-tags of the verbs. Our tool has an average accuracy of about 76%. The tense, mood and voice features are useful both as features in computational modeling and for corpuslinguistic research. IntroductionNatural language employs, among other devices such as temporal adverbials, tense and aspect to locate situations in time and to describe their temporal structure (Deo, 2012). The tool presented here addresses the automatic annotation of morphosyntactic tense, i.e., the tense-aspect combinations, expressed in the morphology and syntax of verbal complexes (VC). VCs are sequences of verbal tokens within a verbal phrase. We address German, French and English, in which the morphology and syntax also includes information on mood and voice. Morphosyntactic tenses do not always correspond to semantic tense (Deo, 2012). For example, the morphosyntactic tense of the English sentence "He is leaving at noon." is present progressive, while the semantic tense is future. In the remainder of this paper, we use the term tense to refer to the morphological tense and aspect information encoded in finite verbal complexes.Corpus-linguistic research, as well as automatic modeling of mono-and cross-lingual use of tense, mood and voice will strongly profit from a reliable automatic method for identifying these clausal features. They may, for instance, be used to classify texts with respect to the epoch or region in which they have been produced, or for assigning texts to a specific author. Moreover, in crosslingual research, tense, mood, and voice have been used to model the translation of tense between different language pairs (Santos, 2004; Loáiciga et al., 2014; Ramm and Fraser, 2016)). Identifying the morphosyntactic tense is also a necessary prerequisite for identifying the semantic tense in synthetic languages such as English, French or German (Reichart and Rappoport, 2010). The extracted tense-mood-voice (TMV) features may also be useful for training models in computational linguistics, e.g., for modeling of temporal relations (Costa and Branco, 2012; UzZaman et al., 2013).As illustrated by the examples in Figure 1, relevant information for determining TMV is given by syntactic dependencies and partially by partof-speech (POS) tags output by analyzers such as Mate (Bohnet and Nivre, 2012). However, the parser's output is not sufficient for determining TMV features; morphological features and lexical information needs to be taken into account as well. Learning TMV features from an annotated corpus would be an alternative; however, to the best of our knowledge, no such large-scale corpora exist.A sentence may contain more than one VC, and the tokens belonging to a VC are not always contiguous in the sentence (see VCs A...

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Learning deep structured semantic models for web search using clickthrough data

Cited by 1,562 publications

References 20 publications

Semantic Parsing for Single-Relation Question Answering

Semantic Parsing for Single-Relation Question Answering

Current trends in the development of intelligent unmanned autonomous systems

Proceedings of ACL 2017, System Demonstrations

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