Errors in formulation of queries made by users can lead to poor search results pages. We performed a living lab study using online A/B testing to measure the degree of improvement achieved with a query amendment technique when applied to a commercial job search engine. Of particular interest in this case study is a clear 'success' signal, namely, the number of job applications lodged by a user as a result of querying the service. A set of 276 queries was identified for amendment in four different categories through the use of word embeddings, with large gains in conversion rates being attained in all four of those categories. Our analysis of query reformulations also provides a better understanding of user satisfaction in the case of problematic queries (ones with fewer results than fill a single page) by observing that users tend to reformulate rewritten queries less.

We describe an investigation into the identification and extraction of unrecorded potential lexical items in Japanese text by detecting text passages containing selected language patterns typically associated with such items.We identified a set of suitable patterns, then tested them with two large collections of text drawn from the WWW and Twitter.Samples of the extracted items were evaluated, and it was demonstrated that the approach has considerable potential for identifying terms for later lexicographic analysis.

Topic models have been shown to be a useful way of representing the content of large document collections, for example, via visualization interfaces (topic browsers). These systems enable users to explore collections by way of latent topics. A standard way to represent a topic is using a term list; that is the top‐n words with highest conditional probability within the topic. Other topic representations such as textual and image labels also have been proposed. However, there has been no comparison of these alternative representations. In this article, we compare 3 different topic representations in a document retrieval task. Participants were asked to retrieve relevant documents based on predefined queries within a fixed time limit, presenting topics in one of the following modalities: (a) lists of terms, (b) textual phrase labels, and (c) image labels. Results show that textual labels are easier for users to interpret than are term lists and image labels. Moreover, the precision of retrieved documents for textual and image labels is comparable to the precision achieved by representing topics using term lists, demonstrating that labeling methods are an effective alternative topic representation.

Many types of search tasks are answered through the computation of a ranked list of suggested answers. We re-examine the usual assumption that answer lists should be as long as possible, and suggest that when the number of matching items is potentially small -- perhaps even zero -- it may be more helpful to "quit while ahead", that is, to truncate the answer ranking earlier rather than later. To capture this effect, metrics are required which are attuned to the length of the ranking, and can handle cases in which there are no relevant documents. In this work we explore a generalized approach for representing truncated result sets, and propose modifications to a number of popular evaluation metrics.

The main objective of the workshop is to bring together researchers who are interested in applications of topic models and improving their output. Our goal is to create a broad platform for researchers to share ideas that could improve the usability and interpretation of topic models. We expect this will promote topic model applications in other research areas, making their use more effective.

User profiling is a key component of personalized recommender systems, and is used to generate user profiles that describe individual user interests and preferences. The increasing availability of big data is driving the urgent need for user profiling algorithms that are able to generate accurate user profiles from large-scale user behavior data. In this paper, we propose a probabilistic rating auto-encoder to perform unsupervised feature learning and generate latent user feature profiles from large-scale user rating data. Based on the generated user profiles, neighbourhood based collaborative filtering approaches have been adopted to make personalized rating predictions. The effectiveness of the proposed approach is demonstrated in experiments conducted on a real-world rating dataset from yelp.com.

Twitter provides access to large volumes of data in real time, but is notoriously noisy, hampering its utility for NLP. In this article, we target out-of-vocabulary words in short text messages and propose a method for identifying and normalizing lexical variants. Our method uses a classifier to detect lexical variants, and generates correction candidates based on morphophonemic similarity. Both word similarity and context are then exploited to select the most probable correction candidate for the word. The proposed method doesn't require any annotations, and achieves state-of-the-art performance over an SMS corpus and a novel dataset based on Twitter.

Relation extraction is the task of extracting semantic relations - such as synonymy or hypernymy - between word pairs from corpus data. Past work in relation extraction has concentrated on manually creating templates to use in directly extracting word pairs for a given semantic relation from corpus text. Recently, there has been a move towards using machine learning to automatically learn these patterns. We build on this research by running experiments investigating the impact of corpus type, corpus size and different parameter settings on learning a range of lexical relations.