James Mayfield! The Johns Hopkins University Applied Physics Laboratory The Human Language Technology Center of Excellence!
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1 James Mayfield! The Johns Hopkins University Applied Physics Laboratory The Human Language Technology Center of Excellence! (301)
2 What is Information Retrieval? Evaluation Characterizing documents and queries Comparing documents against queries Implementation details
3 Information retrieval is the automatic identification of those documents in a large document collection that are relevant to an explicitly stated information need!
4
5 The document collection is static!
6 The document collection is static!
7 The document collection is static A document is relevant or it isn t
8 The document collection is static! A document is relevant or it isn t!
9 The document collection is static! A document is relevant or it isn t! All documents are in the same form! Corollary 1 all documents are text documents! Corollary 2 all documents are the same length! Bonus assumption: All documents are professionally edited
10 The document collection is static! A document is relevant or it isn t! All documents are in the same form! Corollary 1 all documents are text documents! Corollary 2 all documents are the same length! Bonus assumption: All documents are professionally edited
11 The document collection is static! A document is relevant or it isn t! All documents are in the same form! Corollary 1 all documents are text documents! Corollary 2 all documents are the same length! Bonus assumption: All documents are professionally edited There is no user
12 The document collection is static! A document is relevant or it isn t! All documents are in the same form! Corollary 1 all documents are text documents! Corollary 2 all documents are the same length! Bonus assumption: All documents are professionally edited There is no user!
13 The document collection is static! A document is relevant or it isn t! All documents are in the same form! Corollary 1 all documents are text documents! Corollary 2 all documents are the same length! Bonus assumption: All documents are professionally edited There is no user!
14 Field has bifurcated Research community Commercial search engines Key differentiators Scale Query logs
15 1. Characterize each document in collection! 2. Store characterizations on disk! 3. Characterize user s query! 4. Compare characterization of query against document characterizations! 5. Return rank ordered list of documents!
16 Routing and filtering direct documents to interested parties! Multimedia retrieval retrieve e.g. images or speech data! Cross language Retrieval find documents in one language that are relevant to an information need expressed in another language! Summarization capture the essence of a text in fewer words! Translation express in one language the meaning of a document written in another language! Question answering find text that answers a particular question! Topic detection identify stories that discuss the same topic! Classification assign documents to known classes! Clustering assign documents to previously unknown groupings! Novelty detection determine when a new topic is introduced!
17 How do you know that one approach to retrieval is better than another?! At least two requirements for a score based method! An answer key! A way to score a result set based on the answer key!
18 Type one errors Errors of commission False positives Type two errors Errors of omission False negatives precision = recall = A A + B A A + C
19
20 Break text into pieces Fiddle with the pieces to produce terms Throw the terms into a bag No inter-term ordering information Terms are assumed to be independent Terms can also come from outside the text metadata (e.g., author, LOC category) references (e.g., text associated with references (e.g., click here for a taste sensation )
21 Most traditional information retrieval systems index documents according to the words in those documents. Word-based retrieval is language-specific (e.g., a retrieval system for English will not necessarily work well for Japanese, Turkish, or even Spanish). Four score and seven... Words four score and seven... Stopped four score seven years... Stemmed four scor seven year... Word-based retrieval performs poorly when the documents to be retrieved are garbled or contain spelling mistakes (e.g., from OCR).
22 An n-gram is a sequence of n consecutive characters (not words) in a text N-grams can span words Advantages of n-grams: language-independent robust against errors in text capture information about phrases Disadvantages of n-grams: Larger postings files corollary: slower retrieval Don t mesh well with NLP techniques Four score and seven... 5-Grams four our s ur sc r sco... 6-Grams four s our sc ur sco r scor...
23 The efficacy of words and n-grams varies across natural languages Some languages (e.g., Mandarin) are difficult to automatically segment into words, and therefore fare significantly better under n-gram approaches English 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Recall (25.38%) 6-grams (24.81%) Words German 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Recall (28.29%) 6-grams (16.14%) Words French 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Recall (33.55%) 6-grams (35.74%) Words Italian 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Recall (23.91%) 6-grams (24.09%) Words
24 Term weights need not be binary (as they are in the to-be-covered-shortly Boolean model) The more frequently a term appears in a document, the better that term describes the document The more frequently a term appears in the collection as a whole, the worse that term is at discriminating relevant from non-relevant documents So, weight terms by their frequency within the document (TF), and inversely by their frequency in the collection (IDF) w t = tf t log N df t
25 Four basic models Boolean Vector Probabilistic Language Hybrid Retrieval Blind Relevance Feedback
26 Basic Boolean model combines terms with AND, OR, and NOT wild AND (cats OR jaguars) AND NOT football Pure Boolean models do not rank results Extended Boolean models add operators proximity operators wild cards stemming and/or document ranking Some systems provide the pseudo-boolean operators + and -. In a Boolean model, Precision and Recall are thought of as being manipulated separately Reference: Cooper, Getting beyond Boole Information Processing and Management 24(3): , 1988.
27 Reference: Salton, Wang & Yang, A vector space model for automatic indexing. CACM18(11): View documents and queries as points in a (high-dimensional) vector space Typically, each term receives its own dimension Latent Semantic Indexing reduces dimensionality using a principal components analysis of the vector space Similarity between a document and a query is calculated geometrically Euclidean distance is a poor measure Many systems use some variant of cosine sim(d, Q) = i i 2 d i d i q i + q i 2 i
28 Probability ranking principle: optimal retrieval performance is achieved when documents are ranked according to their probabilities of being judged relevant to a query Often, odds of relevance are used instead of probabilities: O(R) = P(R) 1 P(R) Many probabilistic methods boil down to different term weighting Okapi BM25 and variants are the field s stalwarts: w t = tf t N df log t df t dl + tf avdl t Reference: Robertson, The probability ranking principle in IR Journal of Documentation 33(4): , 1977.
29 A language model is a process that outputs strings in a language The.10 purple.20 green.20 frog.50 Build a language model for each document in collection Calculate probability that each language model would produce query: D P(Q D) = P(q D) = q D Rank documents according to these probabilities q Q Requires smoothing for rare or non-existent terms: P(Q D) = [ αp(q D) + (1 α)p(q C) ] = α D q D + (1 α) C q C q Q q Q q Q Reference: Ponte & Croft, A language modeling approach to information retrieval, SIGIR 98,
30 Queries are typically short They omit valuable search terms e.g., automobile in query about cars Blind relevance feedback draws terms from top retrieved documents, performs second retrieval 50 topics from TREC-7 show: 27.7% increase in mean average precision 18.8% more relevant documents identified Works well for queries that already have good performance 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Blind Relevance Feedback Recall (23.7%) Without Feedback (30.2%) With Feedback
31 Statistics about documents and terms must be stored on disk Most common data structure is an inverted index, which maps a term to a list of documents containing that term alfalfa "2" angst "6" arbuthnot "1" assiduous "3" " postings doc42 "3" doc76 "1" doc8 "2" doc19 "1" doc42 "1" " Other data structures (e.g., signature files, PAT trees) can also be used Compression of indexes is advisable
32 BRUTUS! + STABS! CAESAR! Information Retrieval (IR) is the automatic identification of those documents that are relevant to an explicitly-stated information need A broad range of related problems also fit under the scope of IR Precision and recall are the most commonly used evaluation metrics Documents and queries are typically characterized by words, stems or n-grams Common similarity metrics are Boolean, vector space, probabilistic and language model Performance (mean average precision) can be improved by results combination and by blind relevance feedback Because collections are large, care is needed when storing indexes to disk
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