![]() The proliferation of questions and answers in such platforms motivates the ability to automatically find relevant questions to a new question, Question Retrieval, and relevant answers among existing answers, Answer Selection. In particular, Community Question Answering (cQA) forums, such as Quora and Stackoverflow contain millions of open-ended questions and answers. Bengio.Open-ended language communications introduce an enormous challenge in automatic understanding and modeling of human language. Proceedings of the 12th International Workshop on the Web and Databases, Providence, Rhode Island, 2009. Marian, “Beyond the stars: Improving rating predictions using review text content”. ACL, pages 440–447, Prague, Czech Republic, June 2007 Biographies, Bollywood, Boomboxes and Blenders: Domain Adaptation for Sentiment Classification. Maria Pontiki (“Athena” Research Center, Greece) John Pavlopoulos (Athens University of Economics and Business, Greece) Harris Papageorgiou ("Athena" Research Center, Greece) Suresh Manandhar (University of York, UK) Ion Androutsopoulos (Athens University of Economics and Business, Greece)ĭimitris Galanis (“Athena” Research Center, Greece) Information about the domain adaptation dataset of Subtask 2 (out-of-domain ABSA) will be provided in advance. Additional datasets will be provided to evaluate the participating systems in Subtask 1 (in-domain ABSA). Two datasets of ~550 reviews of laptops and restaurants annotated with opinion tuples (as shown in Fig. Unconstrained: using additional resources, such as lexicons or additional training data.Īll teams will be asked to report the data and resources they used for each submitted run. Similarly to previous SemEval Sentiment Analysis tasks, each team may submit two runs:Ĭonstrained: using ONLY the provided training data (of the corresponding domain). Details about the evaluation procedure will be provided shortly. The evaluation framework will be similar to that of the SE-ABSA14. Participants are free to decide the domain(s), subtask(s), and slot(s) they wish to participate in. The output files of the participating systems will be evaluated by comparing them to corresponding files based on human annotations of the test reviews. (1) It fires up in the morning in less than 30 seconds and I have never had any issues with it freezing. → įigure 2: ABSA system input/output example. Some examples highlighting these annotations are given below: The entity types and attribute labels are described in the respective annotation guidelines document. The E#A inventories for the laptops domain contains 22 Entity types (LAPTOP, DISPLAY, CPU, MOTHERBOARD, HARD DISC, MEMORY, BATTERY, etc.) and 9 Attribute labels (GENERAL, PRICE, QUALITY, OPERATION_PERFORMANCE, etc.). Each E#A pair defines an aspect category of the given text. ![]() performance, design, price, quality) per domain. laptop, keyboard, operating system, restaurant, food, drinks) and Attribute labels (e.g. ![]() E and A should be chosen from predefined inventories of Entity types (e.g. Identify every entity E and attribute A pair E#A towards which an opinion is expressed in the given text. Slot 1: Aspect Category (Entity and Attribute). Given a review text about a laptop or a restaurant, identify the following types of information: In particular, SE-ABSA15 consists of the following two subtasks. In addition, SE-ABSA15 will include an out-of-domain ABSA subtask, involving test data from a domain unknown to the participants, other than the domains that will be considered during training. SE-ABSA15 consolidates the four subtasks of SE-ABSA14 within a unified framework. However, unlike SE-ABSA14, the input datasets of SE-ABSA15 will contain entire reviews, not isolated (potentially out of context) sentences. SE-ABSA15 will focus on the same domains as SE-ABSA14 (restaurants and laptops). 1.įigure 1: Table summarizing the average sentiment for each aspect of an entity. The ultimate goal is to be able to generate summaries listing all the aspects and their overall polarity such as the example shown in Fig. In aspect-based sentiment analysis (ABSA) the aim is to identify the aspects of entities and the sentiment expressed for each aspect. The SemEval-2015 Aspect Based Sentiment Analysis (SE-ABSA15) task is a continuation of SemEval-2014 Task 4 (SE-ABSA14). The majority of current approaches, however, attempt to detect the overall polarity of a sentence, paragraph, or text span, irrespective of the entities mentioned (e.g., laptops, battery, screen) and their attributes (e.g. ![]() With the proliferation of user-generated content, interest in mining sentiment and opinions in text has grown rapidly, both in academia and business.
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