This paper presents a generative model to event schema induction. Previous methods in the literature only use head words to represent entities. However, elements other than head words contain useful information. For instance, an armed man is more discriminative than man. Our model takes into account this information and precisely represents it using probabilistic topic distributions. We illustrate that such information plays an important role in parameter estimation. Mostly, it makes topic distributions more coherent and more discriminative. Experimental results on benchmark dataset empirically confirm this enhancement.

In the general framework of knowledge discovery, Data Mining techniques are usually dedicated to information extraction from structured databases. Text Mining techniques, on the other hand, are dedicated to information extraction from unstructured textual data and Natural Language Processing (NLP) can then be seen as an interesting tool for the enhancement of information extraction procedures. In this paper, we present two examples of Text Mining tasks, association extraction and prototypical document extraction, along with several related NLP techniques.

In many information extraction applications, entity linking (EL) has emerged as a crucial task that allows leveraging information about named entities from a knowledge base. In this paper, we address the task of multimodal entity linking (MEL), an emerging research field in which textual and visual information is used to map an ambiguous mention to an entity in a knowledge base (KB). First, we propose a method for building a fully annotated Twitter dataset for MEL, where entities are defined in a Twitter KB. Then, we propose a model for jointly learning a representation of both mentions and entities from their textual and visual contexts. We demonstrate the effectiveness of the proposed model by evaluating it on the proposed dataset and highlight the importance of leveraging visual information when it is available.

The correct identification of the link between an entity mention in a text and a known entity in a large knowledge base is important in information retrieval or information extraction. The general approach for this task is to generate, for a given mention, a set of candidate entities from the base and, in a second step, determine which is the best one. This paper proposes a novel method for the second step which is based on the joint learning of embeddings for the words in the text and the entities in the knowledge base. By learning these embeddings in the same space we arrive at a more conceptually grounded model that can be used for candidate selection based on the surrounding context. The relative improvement of this approach is experimentally validated on a recent benchmark corpus from the TAC-EDL 2015 evaluation campaign.