For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, deep learning technologies have gained significant popularity among various computer vision tasks and also been introduced in iris biometrics, especially iris segmentation. To investigate recent developments and attract more interest of researchers in the iris segmentation method, we organized the 2021 NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021) at the 2021 International Joint Conference on Biometrics (IJCB 2021). The challenge was used as a public platform to assess the performance of iris segmentation and localization methods on Asian and African NIR iris images captured in non-cooperative environments. The three best-performing entries achieved solid and satisfactory iris segmentation and localization results in most cases, and their code and models have been made publicly available for reproducibility research.

As natural language understanding research advances towards deeper knowledge modeling, the tasks become more and more complex: we are interested in more nuanced word characteristics, more linguistic properties, deeper semantic and syntactic features. One such example, explored in this article, is the mention detection and recognition task in the Automatic Content Extraction project, with the goal of identifying named, nominal or pronominal references to real-world entities---mentions---and labeling them with three types of information: entity type, entity subtype and mention type. In this article, we investigate three methods of assigning these related tags and compare them on several data sets. A system based on the methods presented in this article participated and ranked very competitively in the ACE'04 evaluation.

This paper proposes a new approach for coreference resolution which uses the Bell tree to represent the search space and casts the coreference resolution problem as finding the best path from the root of the Bell tree to the leaf nodes. A Maximum Entropy model is used to rank these paths. The coreference performance on the 2002 and 2003 Automatic Content Extraction (ACE) data will be reported. We also train a coreference system using the MUC6 data and competitive results are obtained.

We present a domain-independent topic segmentation algorithm for multi-party speech. Our feature-based algorithm combines knowledge about content using a text-based algorithm as a feature and about form using linguistic and acoustic cues about topic shifts extracted from speech. This segmentation algorithm uses automatically induced decision rules to combine the different features. The embedded text-based algorithm builds on lexical cohesion and has performance comparable to state-of-the-art algorithms based on lexical information. A significant error reduction is obtained by combining the two knowledge sources.

Part-of-speech tagging is needed for French Text-to-Speech (TTS) synthesis to disambiguate the pronunciation of homograph heterophones, liaison instances, and eventually to model intonational contours. A core problem in the part-of-speech tagging in French TTS is to decide on the tagset used for the tagger and the tagset needed by TTS. We carried out a number of experiments on several sizes of tagsets as well as on several algorithms to investigate this problem. Our experiment results suggest that there may be an optimal tagset to be used for the part-of-speech disambiguation in French TTS. This optimal tagset contains a slightly larger number of tags than the tagset that is needed by TTS for pronunciation disambiguation and intonational modeling purposes. In our experiments, the optimal tagset gives a 98.4 % tagging accuracy for TTS, when a trigram Hidden Markov Model tagger is used. 1.

Professional summarizers often reuse original documents to generate summaries. The task of summary sentence decomposition is to deduce whether a summary sentence is constructed by reusing the original text and to identify reused phrases. Specifically, the decomposition program needs to answer three questions for a given summary sentence: (1) Is this summary sentence constructed by reusing the text in the original document? (2) If so, what phrases in the sentence come from the original document? and (3) From where in the document do the phrases come? Solving the decomposition problem can lead to better text generation techniques for summarization. Decomposition can also provide large training and testing corpora for extraction-based summarizers. We propose a hidden Markov model solution to the decomposition problem. Evaluations show that the proposed algorithm performs well.

This paper presents the integration of a large-scale, reusable lexicon for generation with the FUF/SURGE unification-based syntactic realizer. The lexicon was combined from multiple existing resources in a semi-automatic process. The integration is a multi-step unification process. This integration allows the reuse of lexical, syntactic, and semantic knowledge encoded in the lexicon in the development of lexical chooser module in a generation system. The lexicon also brings other benefits to a generation system: for example, the ability to generate many lexical and syntactic paraphrases and the ability to avoid non-grammatical output.

We define the problem of decomposing human-written summary sentences and propose a novel Hidden Markov Model solution to the problem.Human summarizers often rely on cutting and pasting of the full document to generate summaries.Decomposing a human-written summary sentence requires determining: (1) whether it is constructed by cutting and pasting, (2) what components in the sentence come from the original document, and (3) where in the document the components come from.Solving the decomposition problem can potentially lead to the automatic acquisition of large corpora for summarization.It also sheds light on the generation of summary text by cutting and pasting.The evaluation shows that the proposed decomposition algorithm performs well.

We present an approach to information retrieval based on context distance and morphology. Context distance is a measure we use to assess the closeness of word meanings. This context distance model measures semantic distances between words using the local contexts of words within a single document as well as the lexical co-occurrence information in the set of documents to be retrieved. We also propose to integrate the context distance model with morphological analysis in determining word similarity so that the two can enhance each other. Using the standard vector-space model, we evaluated the proposed method on a subset of TREC-4 corpus (AP88 and AP90 collection, 158,240 documents, 49 queries). Results show that this method improves the 11-point average precision by 8.6%.