News portals, such as Yahoo News or Google News, collect large amounts of news articles from a variety of sources on a daily basis. Only a small portion of these documents can be selected and displayed on the homepage. Thus, there is a strong preference for major, recent events. In this work, we propose a scalable First Story Detection (FSD) pipeline that identifies fresh news. This pipeline is used in order to instantiate a variety of FSD approaches. In addition we suggest a novel FSD technique that in comparison to existing systems, relies on relation extraction algorithms and exploits the named entities and their relations in order to decide about the freshness of an article. We evaluate our technique by instantiating existing state of art FSD techniques within our generic pipeline. As ground truth we use multiple datasets that cover different categories. Experimental results demonstrate that our FSD method in many cases provides an improvement over state-of-the-art techniques. In addition, we show using a large synthetic dataset that our general FSD pipeline has constant space and time requirements and is suitable for very high volume streams.

Multi-Label Hierarchical Text Classification (MLHTC) is the task of categorizing documents into one or more topics organized in an hierarchical taxonomy.MLHTC can be formulated by combining multiple binary classification problems with an independent classifier for each category.We propose a novel transfer learning based strategy, HTrans, where binary classifiers at lower levels in the hierarchy are initialized using parameters of the parent classifier and fine-tuned on the child category classification task.In HTrans, we use a Gated Recurrent Unit (GRU)-based deep learning architecture coupled with attention.Compared to binary classifiers trained from scratch, our HTrans approach results in significant improvements of 1% on micro-F1 and 3% on macro-F1 on the RCV1 dataset.Our experiments also show that binary classifiers trained from scratch are significantly better than single multi-label models.

One important problem in task-based conversations is that of effectively updating the belief estimates of user-mentioned slot-value pairs.Given a user utterance, the intent of a slot-value pair is captured using dialog acts (DA) expressed in that utterance.However, in certain cases, DA's fail to capture the actual update intent of the user.In this paper, we describe such cases and propose a new type of semantic class for user intents.This new type, Update Intents (UI), is directly related to the type of update a user intends to perform for a slot-value pair.We define five types of UI's, which are independent of the domain of the conversation.We build a multi-class classification model using LSTM's to identify the type of UI in user utterances in the Restaurant and Shopping domains.Experimental results show that our models achieve strong classification performance in terms of F-1 score.

News portals, such as Yahoo News or Google News, collect large amounts of documents from a variety of sources on a daily basis. Only a small portion of these documents can be selected and displayed on the homepage. Thus, there is a strong preference for major, recent events. In this work, we propose a scalable and accurate First Story Detection (FSD) pipeline that identifies fresh news. In comparison to other FSD systems, our method relies on relation extraction methods exploiting entities and their relations. We evaluate our pipeline using two distinct datasets from Yahoo News and Google News. Experimental results demonstrate that our method improves over the state-of-the-art systems on both datasets with constant space and time requirements.

Subjectivity word sense disambiguation (SWSD) is a supervised and application-specific word sense disambiguation task disambiguating between subjective and objective senses of a word. Not sur-prisingly, SWSD suffers from the knowl-edge acquisition bottleneck. In this work, we use a “cluster and label ” strategy to generate labeled data for SWSD semi-automatically. We define a new algo-rithm called Iterative Constrained Cluster-ing (ICC) to improve the clustering purity and, as a result, the quality of the gener-ated data. Our experiments show that the