A Survey of Paraphrasing and Textual Entailment Methods

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of Informatics, Athens University of Economics and Business, Greece, 201

Unified model of phrasal and sentential evidence for information extraction

Journal ArticleInformation Extraction (IE) systems that extract role fillers for events typically look at the local context surrounding a phrase when deciding whether to extract it. Often, however, role fillers occur in clauses that are not directly linked to an event word. We present a new model for event extraction that jointly considers both the local context around a phrase along with the wider sentential context in a probabilistic framework. Our approach uses a sentential event recognizer and a plausible role-filler recognizer that is conditioned on event sentences. We evaluate our system on two IE data sets and show that our model performs well in comparison to existing IE systems that rely on local phrasal context

Quality of Word Vectors and Its Impact on Named Entity Recognition in Czech

Named Entity Recognition (NER) focuses on finding named entities in text and classifying them into one of the entity types. Modern state-of-the-art NER approaches avoid using hand-crafted features and rely on feature-inferring neural network systems based on word embeddings. The paper analyzes the impact of different aspects related to word embeddings on the process and results of the named entity recognition task in Czech, which has not been investigated so far. Various aspects of word vectors preparation were experimentally examined to draw useful conclusions. The suitable settings in different steps were determined, including the used corpus, number of word vectors dimensions, used text preprocessing techniques, context window size, number of training epochs, and word vectors inferring algorithms and their specific parameters. The paper demonstrates that focusing on the process of word vectors preparation can bring a significant improvement for NER in Czech even without using additional language independent and dependent resources.O

A Survey of Biological Entity Recognition Approaches

There has been growing interest in the task of Named Entity Recognition (NER) and a lot of research has been done in this direction in last two decades. Particularly, a lot of progress has been made in the biomedical domain with emphasis on identifying domain-specific entities and often the task being known as Biological Named Entity Recognition (BER). The task of biological entity recognition (BER) has been proved to be a challenging task due to several reasons as identified by many researchers. The recognition of biological entities in text and the extraction of relationships between them have paved the way for doing more complex text-mining tasks and building further applications. This paper looks at the challenges perceived by the researchers in BER task and investigates the works done in the domain of BER by using the multiple approaches available for the task

QCompere @ REPERE 2013

International audienceWe describe QCompere consortium submissions to the REPERE 2013 evaluation campaign. The REPERE challenge aims at gathering four communities (face recognition, speaker identification, optical character recognition and named entity detection) towards the same goal: multimodal person recognition in TV broadcast. First, four mono-modal components are introduced (one for each foregoing community) constituting the elementary building blocks of our various submissions. Then, depending on the target modality (speaker or face recognition) and on the task (supervised or unsupervised recognition), four different fusion techniques are introduced: they can be summarized as propagation-, classifier-, rule- or graph-based approaches. Finally, their performance is evaluated on REPERE 2013 test set and their advantages and limitations are discussed

Disambiguating the species of biomedical named entities using natural language parsers

Motivation: Text mining technologies have been shown to reduce the laborious work involved in organizing the vast amount of information hidden in the literature. One challenge in text mining is linking ambiguous word forms to unambiguous biological concepts. This article reports on a comprehensive study on resolving the ambiguity in mentions of biomedical named entities with respect to model organisms and presents an array of approaches, with focus on methods utilizing natural language parsers

Mining Social Science Publications for Survey Variables

Research in Social Science is usually based on survey data where individual research questions relate to observable concepts (variables). However, due to a lack of standards for data citations a reliable identification of the variables used is often difficult. In this paper, we present a work-in-progress study that seeks to provide a solution to the variable detection task based on supervised machine learning algorithms, using a linguistic analysis pipeline to extract a rich feature set, including terminological concepts and similarity metric scores. Further, we present preliminary results on a small dataset that has been specifically designed for this task, yielding modest improvements over the baseline