1 Using Semantic Dependencies to Mine Depressive Symptoms from Consultation Records Chung-Hsien Wu and Liang-Chih, Yu National Cheng Kung University Fong-Lin Jang Chi-Mei Medical Center IEEE INTELLIGENT SYSTEMS NOVEMBER/DECEMBER 2005 D a t a M i n i n g i n B i o i n f o r m a t i c s

2 A Example: Consultation Records During past few months, I always felt upset. My life is full of sadness. This caused me to attempt to kill myself several times. Now, I often worry about some minor matters. What can I do?

3 A Example: Consultation Records During past few months, I always felt upset. My life is full of sadness. This caused me to attempt to kill myself several times. Now, I often worry about some minor matters. What can I do? …. is a discourse segment- that is, successive sentences describing the same depressive symptom.

4 A Example: Consultation Records During past few months, I always felt upset. My life is full of sadness. This caused me to attempt to kill myself several times. Now, I often worry about some minor matters. What can I do? Cause-Effect Temporal Sequence

5 a Good Problem? During past few months, I always felt upset. My life is full of sadness. This caused me to attempt to kill myself several times. Now, I often worry about some minor matters. What can I do? Marked semantic label Identify discourse segments Discover semantic relations

6 Domain Knowledge HDRS (1960) is the most prominent rating scale to assess symptoms of depression. is to handle the out-of-domain symptoms.

7 Framework for mining depressive symptoms Marked semantic label Identify discourse segments Discover semantic relations

8 Semantic dependency graph (SDG)

9 Semantic dependency model f is the function that maps a word to a concept in HowNet. For example : “ worry” and “concern”, f(worry)=f(concern)

10 Inferring semantic labels “worry about some minor matters” ( ) “worry about many health problems” ( ). H=(l 1,l 2,…l k-1 ) is the semantic label’s history.

11 Inferring semantic labels Bigram assumption to approximate P(l 1,l 2,…l k-1 )

12 Inferring semantic labels P(l k |H, D) as a semantic label’s confidence score. The best hypothesis is accepted if its confidence score is over the threshold T label. Otherwise, the sentence will be labeled, which means the sentence is out-of-domain or ambiguous.

13 Identifying discourse segments Discourse segments: group of successive sentences with the same semantic label. First use a reestimation process to resolve the labels. For each, the process computes the strength of lexical cohesion (LC) between and its surrounding. i.e., The previous and following n semantic labels. The n is a window size.

14 Identifying discourse segments The LC (lexical cohesion) between two semantic labels is measured by the similarity between their corresponding SDGs. Each semantic dependency in an SDG has the format ( modifer, head, rel modifer,head ) SimM: modifer node score. simH: head node score. simR: relation score. 1, 2, 3, are the weightings of simM,simH and simR.

15 Identifying discourse segments r is concept hierarchy in HowNet. Z is normalization factors.

16 Identifying discourse segments

17 Discovering semantic relations Cause-effect—because, therefore Contrast—however, but Joint—and, also Temporal sequence—before, after A huge knowledge base like WordNet can solve part of the problem. But contrastive relations are not easy collected.

18 Discovering semantic relations Consider positive and negative (depressive) symptoms (P-N pairs). HowNet doesn’t include many such pairs. Affective Norms for English words (ANEW) list provides a set of normative emotional ratings for a large number of words in the English language. Using an automated method to align ANEW with HowNet and then extract the P-N pairs.

19 Discovering semantic relations

20 Experiment Consultation records from PsychPark ( Total data set included 1,514 consultation records. 1,208 for training. 306 for testing. Three experienced psychiatric physicians to help annotate those records. (golden standard) Using majority-vote mechanism to handle disagreements among the physicians. The experiments compare with noisy-channel (NC) models (i.e., non building SDG structure)

21 Evaluation of semantic label inference Expert: A physician wasn’t involved in creating the golden standard.

22 Evaluation of discourse segment identification

23 Evaluation of semantic relation discovery

24 Conclusion The semantic-dependency structure (SDG) captures the intra-sentential information, The lexical cohesion captures the inter-sentential information to identifying discourse segments. The domain ontology (HowNet,WordNet and so on) models the domain knowledge and discovers the semantic relations. Integrating these knowledge sources is a promising approach to the mining task.

25 Conclusion During past few months, I always felt upset. My life is full of sadness. This caused me to attempt to kill myself several times. Now, I often worry about some minor matters. What can I do?