tag:blogger.com,1999:blog-28300250970908557462023-11-16T00:47:09.985+05:30Research Notesgtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.comBlogger57125tag:blogger.com,1999:blog-2830025097090855746.post-41013883361406734382014-10-01T14:50:00.000+05:302014-10-01T15:31:36.536+05:30Relation between precision and recall in binary classification
Let $tp, fp, fn,$ and $tn$ be the number of true positives, false positives, false negatives and true negatives obtained on some set by a binary classifier. Let $P$ and $R$ be the precision and recall given by $P=\frac{tp}{tp+fp}, R = \frac{tp}{tp+fn}$. Let $N=tp+fp+fn+tn$ be the total size of the set. The precision and recall for the negative class are $P'=\frac{tn}{tn+fn}, R'=\frac{tn}{tn+fp}$gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-52296920632279230612013-07-16T17:14:00.000+05:302013-07-16T17:14:42.550+05:30
Language Models for Keyword Search over Data Graphs. Yosi Mass, Yehoshua Sagiv. WSDM 2012
Problem
given a keyword query, find entities in a graph of entities
the graph is probably derived from a database; and it is presumed that the user will find SQL difficult to use.
examples of such databases include Wikipedia, IMDB, and Mondial.
gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-85167905379202650522013-07-09T15:25:00.000+05:302013-07-10T14:09:21.723+05:30
Characterizing the Influence of Domain Expertise on Web Search Behavior. Ryen W. White, Susan T. Dumais, Jaime Teevan. WSDM 2009
Look up
maximum-margin averaged perceptron (Collins, M. Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. EMNLP 2002)
Disorder Inequality: A Combinatorial Approach to Nearest Neighbor Search. gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-5500178193884685512013-06-30T22:25:00.001+05:302013-06-30T22:25:03.922+05:30
Cross-Lingual Latent Topic Extraction. Duo Zhang, Qiaozhu Mei, ChengXiang Zhai. ACL 2010
Key ideas
Input: unaligned document sets in two languages, a bilingual dictionary
Output:
a set of aligned topics (word distributions) in the two languages, that can characterize the shared topics
a topic coverage distribution for each language (coverage of each topic in that language)
Method:gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-75443602705229701522013-04-26T02:16:00.002+05:302013-04-26T02:20:02.768+05:30
Accurate Methods for the Statistics of Surprise and Coincidence. Ted Dunning. Computational Linguistics 1993.
Ideas
"ordinary words are 'rare', any statistical work with texts must deal with the reality of rare events ... Unfortunately, the foundational assumption of most common statistical analyses used in computational linguistics is that the events being analyzed are relatively common."
gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-69990421815696806892013-04-25T14:36:00.000+05:302013-04-25T22:11:04.436+05:30
Statistical Extraction and Comparison of Pivot Words for Bilingual Lexicon Extension. Daniel Andrade, Takuya Matsuzaki, Jun'ichi Tsuji. TALIP 2012
Ideas
Use only statistically significant context---determined using Bayesian estimate of PMI
"calculate a similarity score ... using the probability that the same pivots [(words from the seed lexicon)] will be extracted for both the query word and gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-17899877257464859682013-04-25T14:27:00.002+05:302013-04-25T16:49:44.769+05:30
Attended a literature review on Question Answering by Akihiro Katsura. Some interesting references.
Green, Chomsky, et al. 1961. The BASEBALL system.
rule-based
Isozaki et al. 2009.
machine learning-based
Methodology of QA
Question analysis
Xue et al. SIGIR 2008. Retrieval models for QA archives.
Text retrieval
Jones et al. IPM 2000. ---Okapi/BM25
Berger et al. SIGIR 2000.--gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-59027628184444940992013-04-24T22:06:00.000+05:302013-07-24T15:42:05.138+05:30
Identifying Word Translations from Comparable Documents Without a Seed Lexicon. Reinhard Rapp, Serge Sharoff, Bogdan Babych. LREC 2012
Idea
Assume only document-aligned comparable corpora (and no seed lexicon---"typically comprising at least 10,000 words")
Characterize each article by a set of keywords
"Formulate translation identification as a variant of the word alignment problem in a noisygtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-38091809973309738292013-04-23T01:46:00.000+05:302013-04-25T16:49:44.771+05:30
Addressing polysemy in bilingual lexicon extraction from comparable corpora. Darja Fiser, Nikola Ljubesic, Ozren Kubelka. LREC 2012
Idea
Get source word senses (using sense tagger), construct context vectors for each sense, and then find target translation.
To compute sense-specific vectors: split occurrences of source word into groups, and build context vectors separately for each group.
>holpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-15616686292752082152013-03-14T15:55:00.000+05:302013-04-03T14:36:30.312+05:30
A Wikipedia-Based Multilingual Retrieval Model. Martin Potthast, Benno Stein, and Maik Anderka. ECIR 2008
Key idea
Use aligned Wiki articles (concepts) in two languages to map words/documents in different languages into a common concept space.
Comments
"A reasonable trade-off between retrieval quality and runtime is achieved for a concept space dimensionality between 1000 and 10000."
gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-79932837738013553452013-03-13T18:07:00.000+05:302013-04-03T14:33:24.354+05:30
Wikipedia-based Semantic Interpretation for Natural Language Processing. Evgeniy Gabrilovich, Shaul Markovitch. JAIR 2009
(Noting here the details not mentioned in the entry for the IJCAI paper...)
Corpus preprocessing
Discard articles that have fewer than 100 non stop words or fewer than 5 incoming and outgoing links; discard articles that describe specific dates, as well as Wikipedia gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-24971394994887116192013-03-07T12:41:00.000+05:302013-04-03T14:36:44.218+05:30
Recent Advances in Methods of Lexical Semantic Relatedness – a Survey. Ziqi Zhang, Anna Lisa Gentile, Fabio Ciravegna. NLE 2012
Corpora: Wikipedia, Wiktionary, Wordnet, various biomedical corpora
Methods:
based on Path, Information Content, Gloss, Vector
all methods use structure, mainly from Wordnet/Wikipedia
Some methods that treat Wiki articles as concepts (and use no other gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-74152543981614073032013-03-04T19:48:00.003+05:302013-04-25T18:23:36.222+05:30
Information about Krippendorff's alpha: http://cswww.essex.ac.uk/Research/nle/arrau/alpha.html
gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-4054596870866317492013-02-11T14:06:00.000+05:302013-04-03T14:36:56.386+05:30
Comparison of Semantic Similarity for Different Languages Using the Google n-gram Corpus and Second-Order Co-occurrence Measures. Colette Joubarne, Diana Inkpen. Advances in AI 2011
Claims
many languages without sufficient corpora to achieve valid measures of semantic similarity.
manually-assigned similarity scores from one language can be transferred to another language,
gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-69816139295908165502013-02-08T16:42:00.004+05:302013-04-03T14:37:08.950+05:30
A Graph-Theoretic Framework for Semantic Distance. Vivian Tsang, Suzanne Stevenson. CL 2010
Problem: similarity of texts (not single words)
Claims
"[we do] integration of distributional and ontological factors in measuring semantic distance between two sets of concepts (mapped from two texts) [within a network flow formalism]"
Key ideas
"Our goal is to measure the distance between two gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-88343413573421926752013-02-08T11:49:00.001+05:302013-04-03T14:37:37.224+05:30
Disambiguating Identity Web References using Web 2.0 Data and Semantics. Matthew Rowe, Fabio Ciravegna. Journal of Web Semantics 2010
Comments
Use ideas such as "Average First-Passage Time" of a graph
Interesting papers
L. Lovasz, Random walks on graphs: A survey. Combinatorics 1993
M. Saerens, F. Fouss, L. Yen, P. Dupont, The principal components analysis of a graph, and its relationships gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-52715781439348786932013-02-07T14:15:00.000+05:302013-04-03T14:38:51.805+05:30
A Random Graph Walk based Approach to Computing Semantic Relatedness Using Knowledge from Wikipedia. Ziqi Zhang, Anna Lisa Gentile, Lei Xia, José Iria, Sam Chapman. LREC 2010
Key ideas
Model many kinds of features on a graph
Convert edge weights into probabilities; use p(t)(i|j) to model relatedness (where t is the number of steps in the walk)
Interesting papers
Hughes, T., Ramage, D. gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-12281560731948282232013-02-06T14:40:00.000+05:302013-04-03T15:22:51.041+05:30
A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches. Eneko Agirre, Enrique Alfonseca, Keith Hall, Jana Kravalova, Marius Pasca, Aitor Soroa. NAACL-HLT 2009
Claims
a supervised combination of [our methods] yields the best published results on all datasets
we pioneer cross-lingual similarity
A discussion on the differences between learning similarity and gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-49733233269487613952013-02-05T19:08:00.002+05:302013-04-03T14:39:37.848+05:30
Cross-lingual Semantic Relatedness Using Encyclopedic Knowledge. Samer Hassan and Rada Mihalcea. EMNLP 2009
Key Ideas
Introduce the problem of cross-lingual semantic relatedness.
Map words in different languages to their concept vectors (concepts are Wikipedia articles, similar to Gabrilovich and Markovitch, AAAI 2007). Map concepts using Wikipedia langlinks. The vectors are now comparable.
gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-59212194688291945522013-02-04T16:02:00.001+05:302013-04-03T14:40:09.698+05:30
Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis. Evgeniy Gabrilovich and Shaul Markovitch. IJCAI 2007
Comments
Classifies work in the field into three main directions:
text fragments as bags of words in vector space (distributional similarity)
text fragments as bags of concepts (using Latent Semantic Analysis)
using lexical resources (Wordnet etc.) (also use gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-59980609608880167282013-02-04T15:00:00.000+05:302013-04-03T14:40:38.062+05:30
Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language. Philip Resnik. JAIR 1999
Key Ideas
Comments
Semantic similarity as a special case of semantic relatedness (relation is IS-A)
For example, car-gasoline are related, but car-bicycle are similar.
"... measures of similarity ... are seldom accompanied by an gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-71835278922632978532013-02-01T12:03:00.004+05:302013-04-03T14:41:05.421+05:30
Learning Discriminative Projections for Text Similarity Measures. Wen-tau Yih, Kristina Toutanova, John C. Platt, Christopher Meek. CoNLL 2011
Claims:
We propose a new projection learning framework, Similarity Learning via Siamese Neural Network (S2Net), to discriminatively learn the concept vector representations of input text objects.
Comment:
Input is pairs of words that are known to gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-29387311042999183772013-01-31T17:23:00.000+05:302013-04-03T14:41:32.935+05:30
Fast Large-Scale Approximate Graph Construction for NLP. Amit Goyal, Hal Daum´e III, Raul Guerra. EMNLP 2012
Claims:
In FLAG, we first propose a novel distributed online-PMI algorithm
We propose novel variants of PLEB to address the issue of reducing the pre-processing time for PLEB.
Finally, we show the applicability of large-scale graphs built from FLAG on two applications: the gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-50069164136530270792013-01-30T17:43:00.000+05:302013-04-03T14:42:16.412+05:30
A Relational Model of Semantic Similarity between Words using Automatically Extracted Lexical Pattern Clusters from the Web. Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka. EMNLP 2009
Key ideas
Past work modelled similarity between two words in terms of context overlap, where context consisted of other words known to be closely related to the word (derived either from a corpus or an gtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0tag:blogger.com,1999:blog-2830025097090855746.post-83296791509539781172013-01-30T11:56:00.000+05:302013-04-03T14:42:22.993+05:30
Machine Learning that Matters. Kiri L. Wagstaff. ICML 2012
Key message: An analysis of what ails ML research today, especially w.r.t. its impact to real life problems
Comments on empirical analysis
Needed: domain interpretation of reported results
Which classes were well-classified; which were not
What are the common error types
Why particular data sets were chosen
Metrics
Instead ofgtholpadihttp://www.blogger.com/profile/00817283539149247363noreply@blogger.com0