Question: Why do they set the keyword discovery task?
Evidence: Given a set of labeled and unlabeled microposts, our goal is to extract informative keywords and estimate their expectations in order to train a machine learning model. To achieve this goal, our proposed human-AI loop approach comprises two crowdsourcing tasks, i.e., micropost classification followed by keyword discovery, and a unified probabilistic model for expectation inference and model training. Figure FIGREF6 presents an overview of our approach. Next, we describe our approach from a process-centric perspective.
Following previous studies BIBREF1, BIBREF17, BIBREF2, we collect a set of unlabeled microposts $\mathcal {U}$ from a microblogging platform and post-filter, using an initial (set of) keyword(s), those microposts that are potentially relevant to an event category. Then, we collect a set of event-related microposts (i.e., positively labeled microposts) $\mathcal {L}$, post-filtering with a list of seed events. $\mathcal {U}$ and $\mathcal {L}$ are used together to train a discriminative model (e.g., a deep neural network) for classifying the relevance of microposts to an event. We denote the target model as $p_\theta (y|x)$, where $\theta $ is the model parameter to be learned and $y$ is the label of an arbitrary micropost, represented by a bag-of-words vector $x$. Our approach iterates several times $t=\lbrace 1, 2, \ldots \rbrace $ until the performance of the target model converges. Each iteration starts from the initial keyword(s) or the new keyword(s) discovered in the previous iteration. Given such a keyword, denoted by $w^{(t)}$, the iteration starts by sampling microposts containing the keyword from $\mathcal {U}$, followed by dynamically creating micropost classification tasks and publishing them on a crowdsourcing platform.
Micropost Classification. The micropost classification task requires crowd workers to label the selected microposts into two classes: event-related and non event-related. In particular, workers are given instructions and examples to differentiate event-instance related microposts and general event-category related microposts. Consider, for example, the following microposts in the context of Cyber attack events, both containing the keyword `hack':
Credit firm Equifax says 143m Americans' social security numbers exposed in hack
This micropost describes an instance of a cyber attack event that the target model should identify. This is, therefore, an event-instance related micropost and should be considered as a positive example. Contrast this with the following example:
Companies need to step their cyber security up
This micropost, though related to cyber security in general, does not mention an instance of a cyber attack event, and is of no interest to us for event detection. This is an example of a general event-category related micropost and should be considered as a negative example.
In this task, each selected micropost is labeled by multiple crowd workers. The annotations are passed to our probabilistic model for expectation inference and model training.
Expectation Inference & Model Training. Our probabilistic model takes crowd-contributed labels and the model trained in the previous iteration as input. As output, it generates a keyword-specific expectation, denoted as $e^{(t)}$, and an improved version of the micropost classification model, denoted as $p_{\theta ^{(t)}}(y|x)$. The details of our probabilistic model are given in Section SECREF3.
Keyword Discovery. The keyword discovery task aims at discovering a new keyword (or a set of keywords) that is most informative for model training with respect to existing keywords. To this end, we first apply the current model $p_{\theta ^{(t)}}(y|x)$ on the unlabeled microposts $\mathcal {U}$. For those that contain the keyword $w^{(t)}$, we calculate the disagreement between the model predictions and the keyword-specific expectation $e^{(t)}$:
and select the ones with the highest disagreement for keyword discovery. These selected microposts are supposed to contain information that can explain the disagreement between the model prediction and keyword-specific expectation, and can thus provide information that is most different from the existing set of keywords for model training.
For instance, our study shows that the expectation for the keyword `hack' is 0.20, which means only 20% of the initial set of microposts retrieved with the keyword are event-related. A micropost selected with the highest disagreement (Eq. DISPLAY_FORM7), whose likelihood of being event-related as predicted by the model is $99.9\%$, is shown as an example below:
RT @xxx: Hong Kong securities brokers hit by cyber attacks, may face more: regulator #cyber #security #hacking https://t.co/rC1s9CB
This micropost contains keywords that can better indicate the relevance to a cyber security event than the initial keyword `hack', e.g., `securities', `hit', and `attack'.
Note that when the keyword-specific expectation $e^{(t)}$ in Equation DISPLAY_FORM7 is high, the selected microposts will be the ones that contain keywords indicating the irrelevance of the microposts to an event category. Such keywords are also useful for model training as they help improve the model's ability to identify irrelevant microposts.
To identify new keywords in the selected microposts, we again leverage crowdsourcing, as humans are typically better than machines at providing specific explanations BIBREF18, BIBREF19. In the crowdsourcing task, workers are first asked to find those microposts where the model predictions are deemed correct. Then, from those microposts, workers are asked to find the keyword that best indicates the class of the microposts as predicted by the model. The keyword most frequently identified by the workers is then used as the initial keyword for the following iteration. In case multiple keywords are selected, e.g., the top-$N$ frequent ones, workers will be asked to perform $N$ micropost classification tasks for each keyword in the next iteration, and the model training will be performed on multiple keyword-specific expectations.
The keyword discovery task aims at discovering a new keyword (or a set of keywords) that is most informative for model training with respect to existing keywords.
Answer: To discover new keword that is most informative for existing keywords model training with respect.