Abstract: Entity population, a task of collecting entities that belong to a particular category, has attracted attention from vertical domains. There is still a high demand for creating entity dictionaries in vertical domains, which are not covered by existing knowledge bases. We develop a lightweight front-end tool for facilitating interactive entity population. We implement key components necessary for effective interactive entity population: 1) GUI-based dashboards to quickly modify an entity dictionary, and 2) entity highlighting on documents for quickly viewing the current progress. We aim to reduce user cost from beginning to end, including package installation and maintenance. The implementation enables users to use this tool on their web browsers without any additional packages -users can focus on their missions to create entity dictionaries. Moreover, an entity expansion module is implemented as external APIs. This design makes it easy to continuously improve interactive entity population pipelines. We are making our demo publicly available (http://bit.ly/luwak-demo).

Introduction: Entity extraction is one of the most major NLP components. Most NLP tools (e.g., NLTK, Stanford CoreNLP, etc.), including commercial services (e.g., Google Cloud API, Alchemy API, etc.), provide entity extraction functions to recognize named entities (e.g., PERSON, LOCATION, ORGANIZATION, etc.) from texts. Some studies have defined fine-grained entity types and developed extraction methods BIBREF0 based on these types. However, these methods cannot comprehensively cover domain-specific entities. For instance, a real estate search engine needs housing equipment names to index these terms for providing fine-grained search conditions. There is a significant demand for constructing user-specific entity dictionaries, such as the case of cuisine and ingredient names for restaurant services. A straightforward solution is to prepare a set of these entity names as a domain-specific dictionary. Therefore, this paper focuses on the entity population task, which is a task of collecting entities that belong to an entity type required by a user. We develop LUWAK, a lightweight tool for effective interactive entity population. The key features are four-fold: We think these features are key components for effective interactive entity population. We choose an interactive user feedback strategy for entity population for LUWAK. A major approach to entity population is bootstrapping, which uses several entities that have been prepared as a seed set for finding new entities. Then, these new entities are integrated into the initial seed set to create a new seed set. The bootstrapping approach usually repeats the procedure until it has collected a sufficient number of entities. The framework cannot prevent the incorporation of incorrect entities that do not belong to the entity type unless user interaction between iterations. The problem is commonly called semantic drift BIBREF1 . Therefore, we consider user interaction, in which feedback is given to expanded candidates, as essential to maintaining the quality of an entity set. LUWAK implements fundamental functions for entity population, including (a) importing an initial entity set, (b) generating entity candidates, (c) obtaining user feedback, and (d) publishing populated entity dictionary. We aim to reduce the user’s total workload as a key metric of an entity population tool. That is, an entity population tool should provide the easiest and fastest solution to collecting entities of a particular entity type. User interaction cost is a dominant factor in the entire workload of an interactive tool. Thus, we carefully design the user interface for users to give feedbacks to the tool intuitively. Furthermore, we also consider the end-to-end user cost reduction. We adhere to the concept of developing installation-free software to distribute the tool among a wide variety of users, including nontechnical clusters. This lightweight design of LUWAK might speed up the procedure of the whole interactive entity population workflow. Furthermore, this advantage might be beneficial to continuously improve the whole pipeline of interactive entity population system.

Question: Which sentiment analysis data set has a larger performance drop when a 10% error is introduced?
Answer: SST-2 dataset