The task of Named Entity Recognition (NER) has been well studied under high-resource conditions (e.g., extracting named mentions of PERSON, ORGANIZATION and LOCATION from news articles). However, there are very few studies of the NER task for open-domain collections and in low-resource settings. We focus on NER for low-resource collections, in which any entity types of practical interest to the users of the system must be supported. We try to achieve this with a low cost of annotation of data from the target domain/collection. We propose an entity recognition framework that combines active learning and conditional random fields (CRF), and which provides the flexibility to define new entity types as needed by the users. Our experiments on a help & support corpus show that the system can achieve F1 measure of 0.77 by relying on only 100 manually-annotated sentences.

Speaker identification experiments typically focus on acoustic signals, but conversational speech often occurs in settings where additional useful side information may be available. This paper introduces a new distributable speaker identification test collection based on recorded telephone calls of Enron energy traders. Experiments with these recordings demonstrate that social network features and recording channel metadata can be used to reduce error rates in speaker identification below that achieved using acoustic evidence alone. Social network features from the parallel Enron email collection (37 of the 41 speakers in the telephone recordings sent or received emails in the collection) improve speaker identification, as do social network features computed using lightly supervised techniques to estimate a social network from more than one thousand unlabeled recordings.

Searching conversational speech poses several new challenges, among which is how the searcher will make sense of what they find. This paper describes our initial experiments with a freely available collection of Enron telephone conversations. Our goal is to help the user make sense of search results by finding information about mentioned people, places and organizations. Because automated entity recognition is not yet sufficiently accurate on conversational telephone speech, we ask the user to transcribe just the name, and to indicate where in the recording it was heard. We then seek to link that mention to other mentions of the same entity in a variety of sources (in our experiments, in email and in Wikipedia). We cast this as an entity linking problem, and achieve promising results by utilizing social network features to help compensate for the limited accuracy of automatic transcription for this challenging content.

For each specific mention of an entity found in a text, the goal of entity linking is to determine whether the referenced entity is present in an existing knowledge base, and if so to determine which KB entity is the correct referent. Entity linking has been well explored for dissemination‐oriented sources such as news stories, blogs, and microblog posts, but the limited work to date on “conversational” sources such as email or text chat has not yet attempted to determine when the referent entity is not in the knowledge base (a task known as “NIL detection”). This article presents a supervised machine learning system for linking named mentions of people in email messages to a collection‐specific knowledge base, and that is also capable of NIL detection. This system learns from manually annotated training examples to leverage a rich set of features. The entity linking accuracy for entities present in the knowledge base is substantially and significantly better than the best previously reported results on the Enron email collection, comparable accuracy is reported for the challenging NIL detection task, and these results are for the first time replicated on a second email collection from a different source with comparable results.

One way of evaluating the reusability of a test collection is to determine whether removing the unique contributions of some system would alter the preference order between that system and others. Rank correlation measures such as Kendall's tau are often used for this purpose. Rank correlation measures are appropriate for ordinal measures in which only preference order is important, but many evaluation measures produce system scores in which both the preference order and the magnitude of the score difference are important. Such measures are referred to as interval. Pearson's rho offers one way in which correlation can be computed over results from an interval measure such that smaller errors in the gap size are preferred. When seeking to improve over existing systems, we care the most about comparisons among the best systems. For that purpose we prefer head-weighed measures such as tau_AP, which is designed for ordinal data. No present head weighted measure fully leverages the information present in interval effectiveness measures. This paper introduces such a measure, referred to as Pearson Rank.

Information retrieval systems rank documents, and shared-task evaluations yield results that can be used to rank information retrieval systems. Comparing rankings in ways that can yield useful insights is thus an important capability. When making such comparisons, it is often useful to give greater weight to comparisons near the head of a ranked list than to what happens further down. This is the focus of the widely used τAP measure. When scores are available, gap-sensitive measures give greater weight to larger differences than to smaller ones. This is the focus of the widely used Pearson correlation measure (ρ). This paper introduces a new measure, τGAP, which combines both features. System comparisons from the TREC 5 Ad Hoc track are used to illustrate the differences in emphasis achieved by τAP, ρ, and the proposed τGAP.

ABSTRACT As a large number of corpuses are represented, stored and published in XML format, how to find useful information from XML databases has become an increasingly important issue. Keyword search enables web users to easily access XML data without the need to learn a structured query language or to study complex data schemas. Most existing indexing strategies for XML keyword search are based upon Dewey encoding. In this paper, we proposed a new encoding method called Level Order and Father (LAF) for XML documents. With LAF encoding, we devised a new index structure, called two‐layer LAF inverted index, which can greatly decrease the space complexity compared with Dewey encoding‐based inverted index. Furthermore, with two‐layer LAF inverted index, we proposed a new keyword query algorithm called Algorithm based on Binary Search (ABS) that can quickly find all Smallest Lowest Common Ancestor. We experimentally evaluate two‐layer LAF inverted index and ABS algorithm on four real XML data sets selected from Wikipedia. The experimental results prove the advantages of our index method and querying algorithm. The space consumed by two‐layer LAF index is less than half of that consumed by Dewey inverted index. Moreover, ABS is about one to two orders of magnitude faster than the classic Stack algorithm. Concurrency and Computation: Practice and Experience, 2012.© 2012 Wiley Periodicals, Inc.

Mining frequent patterns in database has emerged as an important task in knowledge discovery and data mining. In this paper, we present an efficient algorithm called Mop for fast frequent pattern discovery. Mop utilizes a new kind of data structure called OP_tree (ordered pattern tree) and some particular properties of frequent patterns to facilitate the process of mining frequent patterns. An OP_tree is a special frequent pattern tree, where the children of any node are sorted according to the supports of corresponding items. Efficiency of Mop is achieved with three techniques: (1) it adopts OP_tree to store a large database to avoid repetitive database scans, (2) it finds all frequent 2-patterns in the construction of OP_tree to avoid the costly generation of a large number of candidate 2-patterns, (3) the supports of candidate k-patterns (k>2) can be obtained by traversing a few of specific subtrees of the OP_tree, which greatly reduces the search space and avoid multi-scans of a database. We experimentally compare our algorithm with the Apriori algorithm and the FP-growth algorithm on one real database and one synthetical database. The experimental results show that Mop is about an order of magnitude faster than the Apriori algorithm. Mop also outperforms the FP-growth algorithm, especially when support threshold is very low and databases are quite large.

Computing top-k results matching XML queries is gaining importance due to the increasing of large XML repositories. In this paper, we propose a novel two-layer-based index construction and associated algorithms for efficiently computing top-k results for SLCA-based XML keyword search. We have conducted expensive experiments and the results show great advantage on efficiency compared with existing approaches.