II-SDV 2017: Semantic Search Jargon - A short Guide
âą
2 gostaramâą1,098 visualizaçÔes
In the early 1990s, the term 'semantic' appeared in the context of text retrieval tools. However, from the very beginning of Information Retrieval as a research field (i.e. as computer-assisted identification of relevant documents), looking at the articles of Vannevar Bush (How we may think) or Luhn (The automatic creation of literature abstracts) in the 1940s and '50s, the idea of semantics was already there. So where are we now in terms of semantics? The `latent semantic indexing` of the 1990s faded away, and the first decade of the millennium enthusiastically studied semantic web technologies. Now, in the second decade, `deep learning` is the new star. In this talk I will give a high-level overview of what has been done already, particularly in the context of the patent domain, what the main techniques are, and in which directions is the scientific community looking today. Ultimately, there will be no one answer to the question of 'What is semantic search?'. Instead, my aim is to empower the audience to ask the right questions next time somebody mentions the term.
Recomendados
Recomendados
Mais conteĂșdo relacionado
Mais procurados
Mais procurados (18)
Semelhante a II-SDV 2017: Semantic Search Jargon - A short Guide
Semelhante a II-SDV 2017: Semantic Search Jargon - A short Guide (20)
Mais de Dr. Haxel Consult
Mais de Dr. Haxel Consult (20)
Ăltimo
Ăltimo (20)
II-SDV 2017: Semantic Search Jargon - A short Guide
- 1. Semantic Search Jargon â a short guide Mihai Lupu TU Wien / RSA Data Science mihai.lupu@researchstudio.at
- 2. âSemanticâ âȘ adjective â dictionary.com: of, relating to, or arising from the different meanings of words or other symbols â Merriam-Webster: of or relating to the meanings of words and phrases â Cambridge: connected with the meanings of words â Oxford: connected with the meaning of words and sentences
- 3. They are among us
- 5. Counting words (aka Statistics)
- 7. The geometric metaphor of meaning âMeanings are locations in a semantic space, and semantic similarity is proximity between the locationsâ (Sahlgren, 2006)
- 9. and others pure counting term frequency position in sentence SMART IDF cosine similarity and many more 195 196 197 198 199 200 201 202 from counting to predicting Latent Semantic Analysis Random Indexing WWW appears Semantic Web appears Deep Learning Speech Vision NLP IR The Golden Age of Artificial Intelligence Expert Systems, Knowledge bases (e.g. Cyc) Inference on billions of tuples on trillions Probabilistic models for IR Language Models
- 10. where are we now? âȘ Inference directly from text âȘ [Bowman et al. 2016] A man rides a bike on a snow covered road A man is outside 2 female babies eating chips Two female babies are enjoying chips A man in an apron shopping at a market A man in an apron is preparing dinner Model % Accur acy Feature-based classifier 78.2 Previous SOTA sentence encoder [Mou et al. 2016] 82.1 LSTN RNN sequence model 80.6 Tree LSTM 80.9 SPINN 83.2 SOTA (sentence pair alignment model) [Parikh et al. 2016] 86.8
- 11. where are we now? âȘ Inference directly from text âȘ [Bowman et al. 2016] A man rides a bike on a snow covered road A man is outside 2 female babies eating chips Two female babies are enjoying chips A man in an apron shopping at a market A man in an apron is preparing dinner Model % Accur acy Feature-based classifier 78.2 Previous SOTA sentence encoder [Mou et al. 2016] 82.1 LSTN RNN sequence model 80.6 Tree LSTM 80.9 SPINN 83.2 SOTA (sentence pair alignment model) [Parikh et al. 2016] 86.8 Particular success cases: Negation: - The rhythmic gymnast completes her floor exercise at the competition - The gymnast cannot finish her exercise Long examples (>20 words): - A man wearing glasses and a ragged costume is playing a Jaguar electric guitar and singing with the accompaniment of a drummer - A man with glasses and a disheveled outfit is playing a guitar and singing along with a drummer.
- 12. Where are we for patents? âȘ Latent Semantic Indexing â Some commercial systems claim to use it âȘ âLatent semantic analysis uses sophisticated statistical analysis of language to search on concepts, not just words, to help you find those documents - even if they don't contain any of the words you used in your searchâ â Minimal improvements found in experiments âȘ [Moldovan:2005]
- 13. Random Indexing âȘ Initial experiments using the Semantic Vectors package â Unsatisfactory results for document similarity â Noticeably good results for term similarity Term vectors Document vectors [Lupu et al.:2013]
- 14. Random Indexing âȘ Initial experiments using the Semantic Vectors package â Unsatisfactory results for document similarity â Noticeably good results for term similarity Term vectors Document vectors 1.0:coatings 0.9999339:rubs 0.9999338:coating 0.9999328:acrylics 0.9999271:vinyls 0.9999268:cratering 0.9999251:distinctness 0.9999246:blistering 0.9999235:pompano 0.9999234:cyanamid 1.0:crystal 0.9999378:cyrstal 0.9999305:crytal 0.9999022:nicol // a type of prism 0.9999014:jjap 0.9999006:nicols 0.9998996:nematic // a type of liquid crystal 0.9998943:uniaxial //minerals that form crystals used in optics 0.9998894:cb15 //a particular liquid crystal 0.9998887:anisotropy 1.0:crystals 0.9998632:supersaturation 0.9998519:crystallizing 0.9998281:supersaturated 0.9998213:crys 0.9998193:purer 0.9998166:soda 0.9998120:crystallize 0.9998105:crystallizers 0.9998081:tals [Lupu et al.:2013]
- 17. looks like we have a problem
- 18. Why words are too simple and documents are too large
- 19. documents are too large Particular success cases: Negation: - The rhythmic gymnast completes her floor exercise at the competition - The gymnast cannot finish her exercise Long examples (>20 words): - A man wearing glasses and a ragged costume is playing a Jaguar electric guitar and singing with the accompaniment of a drummer - A man with glasses and a disheveled outfit is playing a guitar and singing along with a drummer.
- 20. words are too simple âIn a railroad car truck, a windowed side frame, a bolster extending through the window, a wedge pocket in said bolster having an upwardly and outwardly inclined floor in opposition to a vertical wear surface on the side frame, a stabilizing wedge in the pocket having a vertical friction surface in contact with the wear surface on the side frame and an inclined wedging surface in opposition to the floor of the pocket, a removable wear plate inset in a recess In said inclined floor, said recess having a horizontal lower edge, said wear plate having an inclined lower edge formed and adapted to engage and be supported on said horizontal lower edge of said recess, said wear plate being held in said recess by a weldment located between the upper edge of said recess and the lower edge of said wear plate, and, a spring biasing the wedge upwardly against the removable wear plate to cam the wedge laterally against the wear surface on the side frame.â How much is the patent corpus covered by the CELEX lexical database? [Verberne et al., 2010] Patent data COBUILD corpus Tokens 96% 92% Types 55% (?)
- 21. What to do?Research Evaluation
- 22. words are too simple Query Generation [Andersson:2016] â Baseline, NLP:(word, phrases) and Statistically:(unigram, bigram) â Section Claims or entire document â Termhood âȘ Experiment to learn termhoodness, two sample sets: â 637 with C-value and 4,400 without C-value âȘ upper boundary (manual list) versus machine learning âȘ Skip-gram versus exact phrase, âȘ Technical terms versus or non-technical
- 23. Continuous and objective evaluation Search Engine Effectiveness Test
- 24. Artificial Intelligence - Will it ever come? a machine will pass the Turing test by 2029 (Kurzweil 1999, pp. 189-235.) * The Turing Test does not specify the use of patents in the conversation
- 25. Thank you
- 26. Glossary âȘ CBOW Continuous Bag-of-Words âȘ DBPedia Automatically extracted knowledge resource from Wikipedia âȘ dimensionality reduction Any procedure that takes as input a vector of size N and outputs a vector of size M<N âȘ feed-forward a particular type of neural network, which does not contain cycles between its neurons âȘ hypernym a term denoting a broader category than another âȘ hyponym a term denoting a narrower category than another âȘ LOD Linked Open Data âȘ LSA Latent Semantic Analysis âȘ LSI Latent Semantic Indexing âȘ LSTM Long Short Term Memory âȘ matrix decomposition a mathematical procedure to represent a matrix as the product of two or more matrices âȘ matrix factorization matrix decomposition âȘ neural networks an algorithmic model (loosely) simulating brain structures âȘ ontology (here) a knowledge representation resource âȘ OWL Web Ontology Language âȘ PCA Principal Component Analysis âȘ PMI Pointwise Mutual Information âȘ RDF Resource Description Framework âȘ recurrent nn a particular type of neural network, which contains cycles between its neurons âȘ RI Random Indexing âȘ skip-grams method to predict a context from a word âȘ SVD Singular Value Decomposition âȘ WordNet a large lexical database of English