Noisy User-generated Text Research Articles

Knowledge Topic-Structure Exploration for Online Innovative Knowledge Acquisition

Researchers and practitioners have long realized the importance of acquiring innovative knowledge from online innovation communities. However, it has still been a challenge to identify valuable information of interest from unstructured and noisy user-generated text. The extant research trying to solve this problem is limited by the generalizability issue and the lack of topic-level investigation. In this article, we proposed an automatic hybrid model to simultaneously extract knowledge topics and knowledge structures from free text in online innovation communities. By employing a topic model, one important contribution of this article is the elimination of manual intervention and addressing of the generalizability issue faced by existing supervised approaches. Another major contribution of this article is the design of a novel solution for knowledge structure construction by combining a topic model with association analysis; this solution addresses the weakness of the traditional topic model. We evaluate the validity of the proposed hybrid model in experiments with a real-world dataset. The results show that by providing a new angle for innovative knowledge acquisition and management, this proposed method is effective in extracting meaningful innovative knowledge and in uncovering knowledge structures.

Learning Cross-Lingual Word Embeddings from Twitter via Distant Supervision

Cross-lingual embeddings represent the meaning of words from different languages in the same vector space. Recent work has shown that it is possible to construct such representations by aligning independently learned monolingual embedding spaces, and that accurate alignments can be obtained even without external bilingual data. In this paper we explore a research direction that has been surprisingly neglected in the literature: leveraging noisy user-generated text to learn cross-lingual embeddings particularly tailored towards social media applications. While the noisiness and informal nature of the social media genre poses additional challenges to cross-lingual embedding methods, we find that it also provides key opportunities due to the abundance of code-switching and the existence of a shared vocabulary of emoji and named entities. Our contribution consists of a very simple post-processing step that exploits these phenomena to significantly improve the performance of state-of-the-art alignment methods.

Improving named entity recognition in noisy user-generated text with local distance neighbor feature

Recognizing infrequent or emerging named entities in a user-generated text is a challenging task, especially when informal or slang text is used. Some recent works propose to use a gazetteer to solve this problem, but this solution is not general because the gazetteer is task-specific and its maintenance is costly. In this paper, we overcome this drawback by presenting Local Distance Neighbor (LDN), a novel feature that substitutes the gazetteer and makes that the model obtains state-of-the-art results. LDN captures an initial guess for each input token based on the categories of its neighboring tokens within an embedding space. We evaluated the proposed network on the W-NUT-2017 dataset, and we obtained the state-of-the-art F1 score for the Group, Person, and Product categories. We employed our new feature together with the model proposed by Aguilar et al. to recognize named entities in the Tor Darknet related to suspicious activities associated with weapons and drug selling. After increasing the samples of the W-NUT-2017 dataset with 851 manually annotated entries, we repeated our evaluation in this extended version of the dataset, achieving entity and surface F1 scores of 52.96% and 50.57%, respectively. Furthermore, we demonstrate that our proposal can be useful for Law Enforcement Agencies in mining the textual information in the Tor hidden services, being especially adequate for the Group, Person, and Product categories.