Chinese Named Entity Recognition using Lexicalized HMMs

1. Chinese Named Entity Recognition using Lexicalized HMMs Advisor : Dr. Hsu Presenter : Zih-Hui Lin Author :Guohong Fu, Kang-Kwong Luke

2. Outline • Motivation • Objective • Introduction • NER as Known Word Tagging • Known Word Segmentation • Lexicalized HMM Tager • Conclusions • Future Research

3. Motivation • A HMM-based tagger don’t provide contextual word information, which sometimes gives strong evidence for NER. • Some learning methods such as ME and SVMs usually need much more time in training and tagging.

4. Objective • Our system is able to integrate both the internal formation patterns and the surrounding contextual clues for NER under the framework of HMMs • As a result, the performance of the system can be improved without losing its efficiency in training and tagging.

5. Introduction • We develop a two-stage NERsystem for Chinese. • Given a sentence, a known word bigram model is first applied to segment it into a meaningful sequence of known words. • Then, a lexicalized HMM tagger is used to assign each known word a proper hybrid tag that indicates its pattern in forming an entity and the category of the formed entity.

6. Introduction(cont.) • Our system is able to explore three types of features • entity-internal formation patterns • contextual word evidence • contextual category information • As a consequence, the system’s performance can be improved without losing its efficiency in training and processing.

7. NER as Known Word Tagging- Categorization of Entities • We use the same named entity tag set as defined in the IEER-99 Mandarin named entity task, these entity categories are further encoded using twelve different abbreviated SGML tags. • Our system will also assign each common word in the input sentence a proper POS tag. (Peking University POS tag-set)

8. NER as Known Word Tagging- Patterns of known words in NER • A known word w may take one of the following four patterns to present itself during NER: • w is an independent named entity ISE • w is the beginning component of a named entity  BOE • w is at the middle of a named entity MOE • w is at the end of a named entity ISE • Example: • “温家宝/总理/” “<BOE>温</BOE><MOE>家</MOE><EOE>宝</EOE><ISE>总理</ISE>”.

9. NER as Known Word Tagging- NER as known word tagging • We define a hybrid tag set by merging the category tags and the pattern tags.

10. Known Word Segmentation • To find the most appropriate segmentation that maximizes the conditional probability P(W | C) , i.e. • P (w | wt-1) denotes the known word bigram probability, which can be estimated from a segmented corpus using maximum likelihood estimation (MLE).

11. Lexicalized HMM Tager- Lexicalized HMMs • We employ the uniformly lexicalized models to perform the tagging of known words for Chinese NER. • To find an appropriate sequence of hybrid tags that maximizes the conditional probability P(T |W) , namely

12. Lexicalized HMM Tager- Lexicalized HMMs (cont.) • Two types of approximations are employed to simplify the general model. • The firs approximation is based on the independent hypothesis used in standard HMMs: • The second pproximation follows the notion of the lexicalization technique,

13. Lexicalized HMM Tager- Lexicalized HMMs (cont.) • Because MLE will yield zero will yield zero probabilities for any cases that are not observed in the training data. To solve this problem, we employ the linear interpolation smoothing technique .

14. Lexicalized HMM Tager- Lattice-Based Tagging • In our implementation, we employ the classical Viterbi algorithm to find the most probable sequence of hybrid tags for a given sequence of known words, which works in three major steps as follows: • The generation of candidate tags • The decoding of the best tag sequence • The conversion of the results

15. Lexicalized HMM Tager- Inconsistent Tagging • Our system may yield two types of inconsistent tagging : • pattern inconsistency • when two adjacent known words are assigned inconsistent pattern tags such as “ISE:MOE” or “ISE:EOE”. • class inconsistency • two adjacent known words are labeled with different category-tags while at the same time • <CPN-BOE>张</CPN-BOE><Vg-MOW>晓</Vg-MOW><CPN-EOE>华</CPN-EOE>

16. Experiments - Experimental Measures • F-measure is a weighted harmonic mean of precision and recall. • R= • P= • β is the weighing coefficient correctly recognized NEs manually annotated corpus correctly recognized NEs recognized by the system

17. Experiments Data

18. Experiments - Experimental Results • Consequently, our first aim is to examine how the use of thelexicalization technique affects the performance of our system.

19. Experiments - Experimental Results（cont.) • To investigate whether the word-level mode or the character-level model is more effective for Chinese NER.

20. Experiments - Experimental Results（cont.) • to compare our system with other public systems for Chinese NER.

21. Conclusions • The experimental results indicate • on different public corpora show that the NER performance can be significantly enhanced using lexicalization techniques. • character-level tagging are comparable to and may even outperform known-word based tagging when a lexicalized method is applied.

22. Future Directions • Our current tagger is a purely statistical system; it will inevitably suffer from the problem of data sparseness, particularly in open-domain applications. • Our system usually fails to yield correct results for some complicated NEs such as nested organization names.