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
2163794943 1547478246 1905555481 2335367650
Most research in the field of anaphora or coreference detection has been limited to noun phrase coreference, usually on a restricted set of entities, such as ACE entities. In part, this has been due to the lack of corpus resources tagged with general anaphoric coreference. The OntoNotes project is creating a large-scale, accurate corpus for general anaphoric coreference that covers entities and events not limited to noun phrases or a limited set of entity types. The coreference layer in OntoNotes constitutes one part of a multi-layer, integrated annotation of shallow semantic structure in text. This paper presents an initial model for unrestricted coreference based on this data that uses a machine learning architecture with state-of-the-art features. Significant improvements can be expected from using such cross-layer information for training predictive models. This paper describes the coreference annotation in OntoNotes, presents the baseline model, and provides an analysis of the contribution of this new resource in the context of recent MUC and ACE results. In this paper, we argue that the resolution of anaphoric expressions in an utterance is essentially an abductive task following [12] who use a weighted abduction scheme on horn clauses to deal with reference. We give a semantic representation for utterances containing anaphora that enables us to compute possible antecedents by abductive inference. We extend the disjunctive model construction procedure of hyper tableaux [3, 14] with a clause transformation turning the abductive task into a model generation problem and show the completeness of this transformation with respect to the computation of abductive explanations. This abductive inference is applied to the resolution of anaphoric expressions in our general model constructing framework for incremental discourse representation which we argue to be useful for computing information updates from natural language utterances. I compare several types of knowledge-based and knowledge-poor approaches to anaphora and ellipsis resolution. The former are able to capture fine-grained distinctions that depend on lexical meaning and real world knowledge, but they are generally not robust. The latter show considerable promise for yielding wide coverage systems. However, they consistently miss a small but significant subset of cases that are not accessible to rough-grained techniques of intepretation. I propose a sequenced model which first applies the most computationally efficient and inexpensive methods to resolution and then progresses successively to more costly techniques to deal with cases not handled by previous modules. Confidence measures evaluate the judgements of each component in order to determine which instances of anaphora or ellipsis are to be passed on to the next, more fine-grained subsystem.1 We present a discriminative model for single-document summarization that integrally combines compression and anaphoricity constraints. Our model selects textual units to include in the summary based on a rich set of sparse features whose weights are learned on a large corpus. We allow for the deletion of content within a sentence when that deletion is licensed by compression rules; in our framework, these are implemented as dependencies between subsentential units of text. Anaphoricity constraints then improve cross-sentence coherence by guaranteeing that, for each pronoun included in the summary, the pronoun's antecedent is included as well or the pronoun is rewritten as a full mention. When trained end-to-end, our final system outperforms prior work on both ROUGE as well as on human judgments of linguistic quality.