In discussions hosted on discussion forums for MOOCs, references to online learning resources are often of central importance. They contextualize the discussion, anchoring the discussion participants' presentation of the issues and their understanding. However they are usually mentioned in free text, without appropriate hyperlinking to their associated resource. Automated learning resource mention hyperlinking and categorization will facilitate discussion and searching within MOOC forums, and also benefit the contextualization of such resources across disparate views. We propose the novel problem of learning resource mention identification in MOOC forums. As this is a novel task with no publicly available data, we first contribute a large-scale labeled dataset, dubbed the Forum Resource Mention (FoRM) dataset, to facilitate our current research and future research on this task. We then formulate this task as a sequence tagging problem and investigate solution architectures to address the problem. Importantly, we identify two major challenges that hinder the application of sequence tagging models to the task: (1) the diversity of resource mention expression, and (2) long-range contextual dependencies. We address these challenges by incorporating character-level and thread context information into a LSTM-CRF model. First, we incorporate a character encoder to address the out-of-vocabulary problem caused by the diversity of mention expressions. Second, to address the context dependency challenge, we encode thread contexts using an RNN-based context encoder, and apply the attention mechanism to selectively leverage useful context information during sequence tagging. Experiments on FoRM show that the proposed method improves the baseline deep sequence tagging models notably, significantly bettering performance on instances that exemplify the two challenges.
In computational linguistics, anaphora is typically defined as references to items mentioned earlier in the discourse or pointing back'' reference as described by @cite_36 . is then defined as resolving anaphora to its corresponding entities in a discourse. Resolving repeated references to an entity is similar to differentiating whether a mention is a valid resource mention within the course.
@cite_36
1657765689
This paper presents a multilingual robust, knowledge-poor approach to resolving pronouns in technical manuals. This approach is a modification of the practical approach (Mitkov 1998a) and operates on texts pre-processed by a part-of-speech tagger. Input is checked against agreement and a number of antecedent indicators. Candidates are assigned scores by each indicator and the candidate with the highest aggregate score is returned as the antecedent. We propose this approach as a platform for multilingual pronoun resolution. The robust approach was initially developed and tested for English, but we have also adapted and tested it for Polish and Arabic. For both languages, we found that adaptation required minimum modification and that further, even if used unmodified, the approach delivers acceptable success rates. Preliminary evaluation reports high success rates in the range of over 90 .