Triplet Extraction from Sentences Lorand Dali Blaž “Jožef Stefan” Institute, Ljubljana 17 th of October 2008.

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Presentation transcript:

Triplet Extraction from Sentences Lorand Dali Blaž “Jožef Stefan” Institute, Ljubljana 17 th of October 2008

My father carries around the picture of the kid who came with his wallet.

Motivation of Triplet Extraction Advantages ◦ compact and simple representation of the information contained in a sentence ◦ avoids the complexity of a full parse ◦ contains semantic information Applications ◦ building the semantic graph of a document ◦ summarization ◦ question answering

Triplet Extraction – 2 Approaches Extraction from the parse tree of the sentence using heuristic rules ◦ OpenNLP – Treebank Parsetree ◦ Link Parser – Link Grammar (a type of dependency grammar) Extraction using Machine Learning ◦ Support Vector Machines (SVM) are used ◦ The SVM model is trained on human annotated data

.. wheat milling and durum including wheat of kinds all cover will increase The The increase will cover all kinds of wheat including durum and milling wheat. SVM increase cover wheat durum wheat increase cover kinds increase cover including increase cover durum increase cover wheat durum kinds wheat increase cover milling... increase cover wheat increase cover milling increase cover including increase cover durum increase cover wheat increase cover kinds durum kinds wheat durum wheat increase...

Merging Triplets increase cover wheat increase cover milling increase cover including increase cover durum increase cover wheat increase cover kinds increase cover kinds wheat including durum milling wheat increase cover all kinds of wheat including durum and milling wheat Merge triplets with same or adjacent words Add stopwords

Features of the triplet candidates Over 300 features depending on: Sentence ◦ length of sentence, number of words, etc Candidate ◦ context of Subj, Verb and Obj; ◦ distance between Subj, Verb, Obj Linkage ◦ number of links, of link types, nr of links from S, V, O Minipar ◦ depth, diameter, siblings, uncles, cousins, categories, relations Treebank ◦ depth, diameter, siblings, uncles, cousins, path to root, POS

Evaluation and Testing Training set = 700 annotated sentences Test set = 100 annotated sentences Compare the extracted triplets from a sentence to the annotated triplets from that same sentence Comparison is done according to a similaritry measure  [0, 1] between two triplets extracted to annotated => precision annotated to extracted => recall

Triplet Similarity Measure SVO S’ V’O’ SubjSimVerbSimObjSim TrSim = (SubjSim + VerbSim + ObjSim) / 3 TrSim, SubjSim, VerbSim, ObjSim  [0, 1]

String Similarity Measure The way to success is under heavy construction The road to success is always under construction road successunderconstruction waysuccessunderheavyconstruction Sim = nMatch / maxLen = 3 / 5 = 0.6

Candidate Position Histogram

Search the extracted triplets