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A Survey on Unsupervised Graph-based Word
           Sense Disambiguation



                Elena-Oana Tabaranu
             elena.tabaranu@info.uaic.ro
                      UAIC, Iasi
Plan
1.Introduction
2.State of the Art
3.Experiments and Results
4.Conclusions
5.References




                     Elena-Oana Tabaranu   2
Introduction
●   WSD = assign automatically the most
    appropriate meaning to a polysemous word
    within a given context (Sinha et al, 2007)
●   Use Cases:
    ●   Machine translation
    ●   Speech processing
    ●   Boosting the performance of tasks like text retrieval, document
        classification and document clustering




                               Elena-Oana Tabaranu                        3
State of the Art
●   Supervised WSD vs Unsupervised WSD
●   GWSD and Semantic Graph Construction
●   SAN Method
●   Page-Rank Method
●   HITS Method
●   P-Rank Method



                    Elena-Oana Tabaranu    4
Supervised WSD vs Unsupervised WSD
●   Most approaches transform           ●   Identify the best sense
    the sense of the word into a            candidate for a model of the
    feature vector                          word sense dependency in
                                            text
●   Low execution time
                                        ●   Ranking algorithm to choose
●   Accuracy of 60%-70%
                                            their most likely combination
●   Major disadvantage:                 ●   Window, graph based
    knowledge aquisition
                                            representation of the model
    bottleneck (accuracy
    connected to the amount of          ●   Fast execution time
    manually anotated data)             ●   Accuracy of 40%-60%



                            Elena-Oana Tabaranu                             5
Graph-based WSD
●   GWSD = graph representation used to model
    word sense dependencies in text (WSD with
    graphs, not just word window)
●   Goal: identify the most probable sense (label)
    for each word
●   Advantage: takes into account information
    drawn from the entire graph



                      Elena-Oana Tabaranu            6
Semantic Graph Construction (I)
●   Example (Sinha et al, 2007)




                          Elena-Oana Tabaranu   7
Semantic Graph Construction (II)
●   Example (Tsatsaronis et al, 2010)




                          Elena-Oana Tabaranu   8
The Page-Rank Method (Brin and
             Page, 1998)
●   Ranking algorithm based on the idea of voting:
    when one node links to another it offers a vote
    to that other node
●   The higher the number of votes for a note, the
    higher the importance of the node
●   Recursively score the candidate nodes for a
    weighted undirected graph



                      Elena-Oana Tabaranu             9
The P-Rank Method (Zao et al,
                2009)
●   Check the structural similarity of nodes in an
    information network
●   Based on the idea that two nodes are similar if
    they reference and also reference similar nodes
●   Represents a generalization of other state of
    the art measures like CoCitation, Coupling,
    Amsler, SimLink



                       Elena-Oana Tabaranu           10
The HITS Method (Kleinberg,1999)
●   Identify authorities = the most important nodes
    in the graph
●   Identify hubs = the nodes which point to
    authorities
●   The sense with the highest authority is chosen
    as the most likely one for each word
●   Major disadvantage: densely connected nodes
    can attract the highest score (clique attack)

                      Elena-Oana Tabaranu             11
Experiments and Results (I)
●   Senseval 2 and 3 data sets often used for testing
●   Occurencies for Senseval 2 using WordNet 2




●   Occurencies for Senseval 3 using WordNet 2




                            Elena-Oana Tabaranu         12
Experiments and Results (II)
●   Accuracies on the Senseval 2 and 3 English All
    Words Task data sets (Tsatsaronis et al)




                      Elena-Oana Tabaranu        13
Conclusions
●   Recent systems minimise the gap between supervised
    and unsupervised approaches.
●   The graph-based methods make the most of the rich
    semantic model they employ.
●   Unsupervised approaches seek the optimal value for
    the parameters using as little training data as possible
    and testing on as large a dataset as possible.
●   Future work: implement P-Rank using a different
    representation, for example Sinha et al.


                          Elena-Oana Tabaranu              14
References
1. Tsatsaronis, G., Varlamis, I., Norvag, K. : An Experimental
   Study on Unsupervised Graph-based Word Sense
   Disambiguation. In Proc. of CICLing (2010).
2. Sinha, R., Mihalcea, R. :Unsupervised graph-based word
  sense disambiguation using measures of semantic similarity. In
  Proc. of ICSC (2007).
3. Mihalcea, R., Csomai, A. : Senselearner: Word sense
  disambiguation for all words in unrestricted text. In Proc. of
  ACL, pages 53-56 (2005).
4. Tsatsaronis, G., Vazirgiannis, M., Androutsopoulos, I. :Word
   Sense Disambiguation with Spreading Activation Networks
   Generated from Thesauri. In Proc. of IJCAI (2007).

                            Elena-Oana Tabaranu                    15
Questions?




  Elena-Oana Tabaranu   16

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A Survey on Unsupervised Graph-based Word Sense Disambiguation

  • 1. A Survey on Unsupervised Graph-based Word Sense Disambiguation Elena-Oana Tabaranu elena.tabaranu@info.uaic.ro UAIC, Iasi
  • 2. Plan 1.Introduction 2.State of the Art 3.Experiments and Results 4.Conclusions 5.References Elena-Oana Tabaranu 2
  • 3. Introduction ● WSD = assign automatically the most appropriate meaning to a polysemous word within a given context (Sinha et al, 2007) ● Use Cases: ● Machine translation ● Speech processing ● Boosting the performance of tasks like text retrieval, document classification and document clustering Elena-Oana Tabaranu 3
  • 4. State of the Art ● Supervised WSD vs Unsupervised WSD ● GWSD and Semantic Graph Construction ● SAN Method ● Page-Rank Method ● HITS Method ● P-Rank Method Elena-Oana Tabaranu 4
  • 5. Supervised WSD vs Unsupervised WSD ● Most approaches transform ● Identify the best sense the sense of the word into a candidate for a model of the feature vector word sense dependency in text ● Low execution time ● Ranking algorithm to choose ● Accuracy of 60%-70% their most likely combination ● Major disadvantage: ● Window, graph based knowledge aquisition representation of the model bottleneck (accuracy connected to the amount of ● Fast execution time manually anotated data) ● Accuracy of 40%-60% Elena-Oana Tabaranu 5
  • 6. Graph-based WSD ● GWSD = graph representation used to model word sense dependencies in text (WSD with graphs, not just word window) ● Goal: identify the most probable sense (label) for each word ● Advantage: takes into account information drawn from the entire graph Elena-Oana Tabaranu 6
  • 7. Semantic Graph Construction (I) ● Example (Sinha et al, 2007) Elena-Oana Tabaranu 7
  • 8. Semantic Graph Construction (II) ● Example (Tsatsaronis et al, 2010) Elena-Oana Tabaranu 8
  • 9. The Page-Rank Method (Brin and Page, 1998) ● Ranking algorithm based on the idea of voting: when one node links to another it offers a vote to that other node ● The higher the number of votes for a note, the higher the importance of the node ● Recursively score the candidate nodes for a weighted undirected graph Elena-Oana Tabaranu 9
  • 10. The P-Rank Method (Zao et al, 2009) ● Check the structural similarity of nodes in an information network ● Based on the idea that two nodes are similar if they reference and also reference similar nodes ● Represents a generalization of other state of the art measures like CoCitation, Coupling, Amsler, SimLink Elena-Oana Tabaranu 10
  • 11. The HITS Method (Kleinberg,1999) ● Identify authorities = the most important nodes in the graph ● Identify hubs = the nodes which point to authorities ● The sense with the highest authority is chosen as the most likely one for each word ● Major disadvantage: densely connected nodes can attract the highest score (clique attack) Elena-Oana Tabaranu 11
  • 12. Experiments and Results (I) ● Senseval 2 and 3 data sets often used for testing ● Occurencies for Senseval 2 using WordNet 2 ● Occurencies for Senseval 3 using WordNet 2 Elena-Oana Tabaranu 12
  • 13. Experiments and Results (II) ● Accuracies on the Senseval 2 and 3 English All Words Task data sets (Tsatsaronis et al) Elena-Oana Tabaranu 13
  • 14. Conclusions ● Recent systems minimise the gap between supervised and unsupervised approaches. ● The graph-based methods make the most of the rich semantic model they employ. ● Unsupervised approaches seek the optimal value for the parameters using as little training data as possible and testing on as large a dataset as possible. ● Future work: implement P-Rank using a different representation, for example Sinha et al. Elena-Oana Tabaranu 14
  • 15. References 1. Tsatsaronis, G., Varlamis, I., Norvag, K. : An Experimental Study on Unsupervised Graph-based Word Sense Disambiguation. In Proc. of CICLing (2010). 2. Sinha, R., Mihalcea, R. :Unsupervised graph-based word sense disambiguation using measures of semantic similarity. In Proc. of ICSC (2007). 3. Mihalcea, R., Csomai, A. : Senselearner: Word sense disambiguation for all words in unrestricted text. In Proc. of ACL, pages 53-56 (2005). 4. Tsatsaronis, G., Vazirgiannis, M., Androutsopoulos, I. :Word Sense Disambiguation with Spreading Activation Networks Generated from Thesauri. In Proc. of IJCAI (2007). Elena-Oana Tabaranu 15
  • 16. Questions? Elena-Oana Tabaranu 16