Estimating Maximally Probable Constrained Relations by Mathematical Programming

Estimating a constrained relation is a fundamental problem in machine learning. Special cases are classification (the problem of estimating a map from a set of to-be-classified elements to a set of labels), clustering (the problem of estimating an equivalence relation on a set) and ranking (the problem of estimating a linear order on a set). We contribute a family of probability measures on the set of all relations between two finite, non-empty sets, which offers a joint abstraction of multi-label classification, correlation clustering and ranking by linear ordering. Estimating (learning) a maximally probable measure, given (a training set of) related and unrelated pairs, is a convex optimization problem. Estimating (inferring) a maximally probable relation, given a measure, is a 01-linear program. It is solved in linear time for maps. It is NP-hard for equivalence relations and linear orders. Practical solutions for all three cases are shown in experiments with real data. Finally, estimating a maximally probable measure and relation jointly is posed as a mixed-integer nonlinear program. This formulation suggests a mathematical programming approach to semi-supervised learning.Comment: 16 page

STransE: a novel embedding model of entities and relationships in knowledge bases

Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction or knowledge base completion, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a low-dimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task.Comment: V1: In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016. V2: Corrected citation to (Krompa{\ss} et al., 2015). V3: A revised version of our NAACL-HLT 2016 paper with additional experimental results and latest related wor

Automatic Generation of Grounded Visual Questions

In this paper, we propose the first model to be able to generate visually grounded questions with diverse types for a single image. Visual question generation is an emerging topic which aims to ask questions in natural language based on visual input. To the best of our knowledge, it lacks automatic methods to generate meaningful questions with various types for the same visual input. To circumvent the problem, we propose a model that automatically generates visually grounded questions with varying types. Our model takes as input both images and the captions generated by a dense caption model, samples the most probable question types, and generates the questions in sequel. The experimental results on two real world datasets show that our model outperforms the strongest baseline in terms of both correctness and diversity with a wide margin.Comment: VQ

Using Tag Semantic Network for Keyphrase Extraction in Blogs

Folksonomies provide a comfortable way to search and browse the blogosphere. As the tags in the blogosphere are sparse, ambiguous and too general, this paper proposes both a supervised and an unsupervised approach that extract tags from posts using a tag semantic network. We evaluate the two methods on a blog dataset and observe an improvement in F1-measure from 0.23 to 0.50 when compared to the baseline system

Sentiment analysis with limited training data

Sentiments are positive and negative emotions, evaluations and stances. This dissertation focuses on learning based systems for automatic analysis of sentiments and comparisons in natural language text. The proposed approach consists of three contributions: 1. Bag-of-opinions model: For predicting document-level polarity and intensity, we proposed the bag-of-opinions model by modeling each document as a bag of sentiments, which can explore the syntactic structures of sentiment-bearing phrases for improved rating prediction of online reviews. 2. Multi-experts model: Due to the sparsity of manually-labeled training data, we designed the multi-experts model for sentence-level analysis of sentiment polarity and intensity by fully exploiting any available sentiment indicators, such as phrase-level predictors and sentence similarity measures. 3. LSSVMrae model: To understand the sentiments regarding entities, we proposed LSSVMrae model for extracting sentiments and comparisons of entities at both sentence and subsentential level. Different granularity of analysis leads to different model complexity, the finer the more complex. All proposed models aim to minimize the use of hand-labeled data by maximizing the use of the freely available resources. These models explore also different feature representations to capture the compositional semantics inherent in sentiment-bearing expressions. Our experimental results on real-world data showed that all models significantly outperform the state-of-the-art methods on the respective tasks.Sentiments sind positive und negative Gefühle, Bewertungen und Einstellungen. Die Dissertation beschäftigt sich mit lernbasierten Systemen zur automatischen Analyse von Sentiments und Vergleichen in Texten in natürlicher Sprache. Die vorliegende Abeit leistet dazu drei Beiträge: 1. Bag-of-Opinions-Modell: Zur Vorhersage der Polarität und Intensität auf Dokumentenebene haben wir das Bag-of-Opinions-Modell vorgeschlagen, bei dem jedes Dokument als ein Beutel Sentiments dargestellt wird. Das Modell kann die syntaktischen Strukturen von subjektiven Ausdrücken untersuchen, um eine verbesserte Bewertungsvorhersage von Online-Rezensionen zu erzielen. 2. Multi-Experten-Modell: Wegen des Mangels an manuell annotierten Trainingsdaten haben wir das Multi-Experten-Modell entworfen, um die Sentimentpolarität und -intensität auf Satzebene zu analysieren. Das Modell kann alle möglichen Sentiment-Indikatoren verwenden, wie Prädiktoren auf Phrasenebene und Ähnlichkeitsmaße von Sätzen. 3. LSSVMrae-Modell: Um Sentiments von Entitäten zu verstehen, wir haben wir das LSSVMrae-Modell zur Extraktion von Sentiments und Vergleichen von Entitäten auf Satz- und Ausdrucksebene vorgeschlagen. Die unterschiedliche Granularität der Analyse führt zu unterschiedlicher Modellkomplexität; je feiner, desto komplexer. Alle vorgeschlagenen Modelle zielen darauf ab, möglichst wenige manuell annotierte Daten und möglichst viele frei verfügbare Ressourcen zu verwenden. Diese Modelle untersuchen auch verschiedene Merkmalsdarstellungen, um die Kompositionssemantik abzubilden, die subjektiven Ausdrücken inhärent ist. Die Ergebnisse unserer Experimente mit Realweltdaten haben gezeigt, dass alle Modelle für die jeweiligen Aufgaben deutlich bessere Leistungen erzielen als die modernsten Methoden