Method and a system for semantic relation extraction

• US 20090019032A1
• Filed: 11/05/2007
• Published: 01/15/2009
• Est. Priority Date: 07/13/2007
• Status: Abandoned Application

• Current Assignee: Siemens AG
• Original Assignee: Siemens AG
• Inventors: Dejori, Mathaeus, Stetter, Martin, Bundschus, Markus, Tresp, Volker
• Application Number:
  US11/979,534
• Publication Number:
  US 20090019032A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

The invention provides a method for semantic relation extraction, wherein on the basis of an annotated training corpus having tokens with associated relational labels each indicating a relation between the respective token and a selectable key entity semantic relation between said key entity and other entities are directly extracted from unstructured text using a probabilistic extraction model.

Claims

1. A method for semantic relation extraction comprising:

extracting directly on the basis of an annotated training corpus having tokens with associated relational labels each indicating a relation between the respective token and a selectable key entity semantic relation between said key entity and other entities from unstructured text using a probabilistic extraction model.

2. The method according to claim 1,

wherein the probabilistic extraction model is a conditional random field.

3. The method according to claim 1,

wherein weighting factors (λ

) for each feature are calculated on the basis of a feature label distribution of said annotated training corpus by means of a maximum likelihood algorithm.

4. The method according to claim 1, wherein a query comprising said key entity is input by a user.

5. The method according to claim 4, wherein the input query is tokenized to generate a token sequence.

6. The method according to claim 5, wherein a most likely label sequence is calculated for the generated token sequence by means of a Viterbi algorithm using said calculated weighting factors.

7. The method according to claim 6, wherein a conditional probability (P) of the label sequence is calculated as follows:

$p<br><br>\left(y/x\right)=\frac{1}{Z_x}<br><br>\exp (\underset{i=1}{\overset{N}{\sum <br><br>}}<br><br>\underset{k=1}{\overset{K}{\sum <br><br>}}<br><br>{\mathrm{\lambda <br><br>}}_k<br><br>f_k<br><br>\left(y_{i-1},y_i,x,i\right)$

wherein Z_x is a normalization factor,

f_k(y<sub>i−

1</sub>, y_i,x, i) is an arbitrary feature function, λ

_K is a calculated weight factor for a feature function ranging between −

∞ and

+∞

.

8. The method according to claim 7, wherein the normalization factor Z_x is calculated as follows:

$Z_x=\underset{s\in <br><br>S^N}{\sum <br><br>}<br><br>\exp (\underset{i=1}{\overset{N}{\sum <br><br>}}<br><br>\underset{k=1}{\overset{K}{\sum <br><br>}}<br><br>{\mathrm{\lambda <br><br>}}_k<br><br>f_k<br><br>\left(y_{i-1},y_i,x,i\right)$

wherein N is the length of the input sequence.

9. The method according to claim 1, wherein the semantic relations are formed by biomedical relations.

10. The method according to claim 9, wherein the biomedical relations is

an altered expression,

a genetic variation,

a regulatory modification,

a general relation, and

a non-existing relation between two entities.

11. The method according to claim 1, wherein a set of recognition features is provided.

12. The method according to claim 11, wherein the set of recognition features comprises:

orthographic features

word shape features,

n-gram features,

dictionary features, and

context features.

13. The method according to claim 1, wherein a set of relation recognition features is provided.

14. The method according to claim 13, wherein the set of relation recognition features comprises:

a dictionary window feature,

a key entity neighbourhood feature

a start window feature, and

a negation feature.

15. Method according to claim 1, wherein the entities are formed by biomedical entities.

16. The method according to claim 15, wherein the entities comprise genes, diseases, drugs, compounds and proteins.

17. A computer program for performing the method for semantic relation extraction according to claim 1.

18. A data carrier for storing instructions of a computer program which performs the method for semantic relation extraction according to claim 1.

19. A semantic relation extraction system comprising:

(a) means for storing unstructured text;

(b) means for storing an annotated training corpus having tokens with associated relational labels each indicating a relation between the respective token and a selectable key entity; and

(c) means for extracting semantic relations between the key entity and other entities from said unstructured text on the basis of said training corpus using a probabilistic extraction model.

20. The semantic relation extraction system according to claim 19, wherein said probabilistic extraction model is a conditional random field.