Linguistic Graph Similarity for News Sentence Searching
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1 Lingutic Graph Similarity for News Sentence Searching Kim Schouten & Flavius Frasincar Web News Sentence Searching Using Lingutic Graph Similarity, Kim Schouten and Flavius Frasincar. In Proceedings of the 12th International Baltic Conference on Databases and Information Systems (DB&IS 2016), pages , Springer, 2016
2 Problem Most text search methods are word-based Often, the context lost for the sake of simplicity However, the meaning of a word defined by both word and context How can we include context information of words into the search algorithm? Can we not search by sentence instead of words, and retrieve sentences with similar meaning?
3 Graph-based Approach Grammatically parsing a sentence yields a graph Words are the nodes Grammatical relations between words are the edges Set of relations of a word can then be used as context NLP pipeline transforms both query and news sentences into graphs
4 Graph representation of sentence
5 Graph comparon Problem similar to graph omorphm But partial similarity makes it much harder Nodes may be msing on either side Nodes may be only partially similar (pc <> workstation) Relation labels may be different for similar nodes Hence, output not binary but a real-valued similarity score
6 Graph comparon Nodes are compared on: Basic and full part-of-speech (POS) label Stem, lemma, and fully inflected word If POS the same, but word not then check for: Synonymy Hypernymy (1 / steps in hypernym tree) Correct for word frequency
7 Graph comparon We can recursively go through both graphs Compare nodes and edges to assign score However, a starting position within both graphs needed: Using all possibilities inefficient Always starting at root inaccurate Use index of stemmed words (s/s) Only the best scoring starting position kept
8 In Gartner s rankings, Lenovo the PC maker. Hewlett-Packard still workstation manufacturer according to new ranking by IDC. the determiner Hewlett-Packard proper legend: word part-of-speech (lemma) (stem) PC Search algorithm compound in possession Lenovo proper maker (maker) (make) rankings plural Gartner proper Case difference (plural vs. singular) and different relation type workstation still ad manufacturer (manufacturer) (manufactur) to ranking new compound according object by IDC proper
9 In Gartner s rankings, Lenovo the PC maker. Hewlett-Packard still workstation manufacturer according to new ranking by IDC. the determiner Hewlett-Packard proper legend: word part-of-speech (lemma) (stem) PC Search algorithm compound in possession Lenovo proper maker (maker) (make) rankings plural Gartner proper Case difference (plural vs. singular) and different relation type Part-of-Speech identical but different relation type workstation still ad manufacturer (manufacturer) (manufactur) to ranking new compound according object by IDC proper
10 In Gartner s rankings, Lenovo the PC maker. Hewlett-Packard still workstation manufacturer according to new ranking by IDC. the determiner Hewlett-Packard proper legend: word part-of-speech (lemma) (stem) PC Search algorithm compound in possession Lenovo proper maker (maker) (make) rankings plural Gartner proper Synonym and different relation type Case difference (plural vs. singular) Part-of-Speech identical but different relation type workstation still ad manufacturer (manufacturer) (manufactur) to ranking new compound according object by IDC proper
11 In Gartner s rankings, Lenovo the PC maker. Hewlett-Packard still workstation manufacturer according to new ranking by IDC. the determiner Hewlett-Packard proper legend: word part-of-speech (lemma) (stem) PC Search algorithm compound in possession Lenovo proper maker (maker) (make) rankings plural Gartner proper Identical words and relation type Synonym and different relation type Case difference (plural vs. singular) Part-of-Speech identical but different relation type workstation still ad manufacturer (manufacturer) (manufactur) to ranking new compound according object by IDC proper
12 In Gartner s rankings, Lenovo the PC maker. Hewlett-Packard still workstation manufacturer according to new ranking by IDC. the determiner PC Search algorithm compound in possession Lenovo proper maker (maker) (make) rankings plural Gartner proper Part-of-Speech and relation type identical Identical words and relation type Synonym and different relation type Case difference (plural vs. singular) Part-of-Speech identical but different relation type Hewlett-Packard proper workstation still ad manufacturer (manufacturer) (manufactur) to ranking new compound according object by legend: word part-of-speech (lemma) (stem) IDC proper
13 In Gartner s rankings, Lenovo the PC maker. Hewlett-Packard still workstation manufacturer according to new ranking by IDC. the determiner PC Search algorithm Hypernym compound in possession Lenovo proper maker (maker) (make) rankings plural Gartner proper Part-of-Speech and relation type identical Identical words and relation type Synonym and different relation type Case difference (plural vs. singular) Part-of-Speech identical but different relation type Hewlett-Packard proper workstation still ad manufacturer (manufacturer) (manufactur) to ranking new compound according object by legend: word part-of-speech (lemma) (stem) IDC proper
14 In Gartner s rankings, Lenovo the PC maker. Hewlett-Packard still workstation manufacturer according to new ranking by IDC. the determiner PC Search algorithm Hypernym compound in possession Lenovo proper maker (maker) (make) rankings plural Gartner proper Identical words and relation type Part-of-Speech and relation type identical Identical words and relation type Synonym and different relation type Case difference (plural vs. singular) Part-of-Speech identical but different relation type Hewlett-Packard proper workstation still ad manufacturer (manufacturer) (manufactur) to ranking new compound according object by legend: word part-of-speech (lemma) (stem) IDC proper
15 Data set A set of ~1000 sentences Extracted from news items News items are on roughly the same ic 10 sentences are designated as queries Three human annotators annotated the similarity between each of the queries and each of the news sentences Similarity score of 0,1,2, or 3 Inter-annotator agreement: std.dev. in score
16 Score optimization Each comparon of two nodes or two edges contributes to total similarity score The exact score that each feature can yield optimized using genetic optimization 5 queries and related data are used for training Other 5 queries and related data are used for testing Th repeated 32 times, with different splits For each query a ranked lt of sentences produced according to similarity
17 Performance Results are averages over all 32 splits t-stattics are computed over the 32 results for each metric
18 Conclusions Our proposed method has several improvements over traditional text searching: By representing text as a graph, the original semantics are preserved, which can be used to leverage search results Words are not only compared lexically, but also semantically, by looking for synonyms and hypernyms
19 Thank you for your attention! Questions?
20 Backup slides
21 Pipeline
22 Evaluating ranked lts Three metrics: MAP, Spearman s Rho, and ndcg MAP measures to what extent the of the ranking contains only similar/relevant items MAP assumes binary similarity System outputs real-valued similarity scores Converted to binary using cut-off value(s) Cut-off values from 0 to 3 with stepsize 0.1 Reported MAP score average of these
23 Evaluating ranked lts Spearman s Rho measures correlation of whole lt Only part of results used in practice ndcg measures whether the most similar items are in the of the ranking Every result contributes its similarity value to final score, dcounted by position in ranking Most appropriate Focuses on part of the ranking Uses real-valued similarity values
24 Scalable? Linear in the number of sentences Graph comparon a large constant factor Depends on: # nodes in query # edges in query Average # nodes in sentences Average # edges in sentences
25 Performance TF-IDF on average twice as fast D T
26 Open Issues More intelligent way to find start positions Co-reference resolution Non-literal expressions Mitigate problems with varying graph sizes Microsoft expanding its online corporate offerings to include a full version of Office Microsoft includes Office into its online corporate offerings
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