DCU at the TREC 2008 Blog Track

In this paper we describe our system, experiments and re- sults from our participation in the Blog Track at TREC 2008. Dublin City University participated in the adhoc re- trieval, opinion finding and polarised opinion finding tasks. For opinion finding, we used a fusion of approaches based on lexicon features, surface features and syntactic features. Our experiments evaluated the relative usefulness of each of the feature sets and achieved a significant improvement on the baseline

English Conversational Telephone Speech Recognition by Humans and Machines

One of the most difficult speech recognition tasks is accurate recognition of human to human communication. Advances in deep learning over the last few years have produced major speech recognition improvements on the representative Switchboard conversational corpus. Word error rates that just a few years ago were 14% have dropped to 8.0%, then 6.6% and most recently 5.8%, and are now believed to be within striking range of human performance. This then raises two issues - what IS human performance, and how far down can we still drive speech recognition error rates? A recent paper by Microsoft suggests that we have already achieved human performance. In trying to verify this statement, we performed an independent set of human performance measurements on two conversational tasks and found that human performance may be considerably better than what was earlier reported, giving the community a significantly harder goal to achieve. We also report on our own efforts in this area, presenting a set of acoustic and language modeling techniques that lowered the word error rate of our own English conversational telephone LVCSR system to the level of 5.5%/10.3% on the Switchboard/CallHome subsets of the Hub5 2000 evaluation, which - at least at the writing of this paper - is a new performance milestone (albeit not at what we measure to be human performance!). On the acoustic side, we use a score fusion of three models: one LSTM with multiple feature inputs, a second LSTM trained with speaker-adversarial multi-task learning and a third residual net (ResNet) with 25 convolutional layers and time-dilated convolutions. On the language modeling side, we use word and character LSTMs and convolutional WaveNet-style language models

Grammaticalization and grammar

This paper is concerned with developing Joan Bybee's proposals regarding the nature of grammatical meaning and synthesizing them with Paul Hopper's concept of grammar as emergent. The basic question is this: How much of grammar may be modeled in terms of grammaticalization? In contradistinction to Heine, Claudi & Hünnemeyer (1991), who propose a fairly broad and unconstrained framework for grammaticalization, we try to present a fairly specific and constrained theory of grammaticalization in order to get a more precise idea of the potential and the problems of this approach. Thus, while Heine et al. (1991:25) expand – without discussion – the traditional notion of grammaticalization to the clause level, and even include non-segmental structure (such as word order), we will here adhere to a strictly 'element-bound' view of grammaticalization: where no grammaticalized element exists, there is no grammaticalization. Despite this fairly restricted concept of grammaticalization, we will attempt to corroborate the claim that essential aspects of grammar may be understood and modeled in terms of grammaticalization. The approach is essentially theoretical (practical applications will, hopefully, follow soon) and many issues are just mentioned and not discussed in detail. The paper presupposes a familiarity with the basic facts of grammaticalization and it does not present any new facts

A Likelihood Ratio Based Forensic Text Comparison with Multiple Types of Features

This study aims at further improving forensic text comparison (FTC) under the likelihood ratio (LR) framework. While the use of the LR framework to conclude the strength of evidence is well recognised in forensic science, studies on forensic text evidence within the LR framework are limited, and this study is an attempt of alleviating this situation. There have already been initiatives to obtain LRs for textual evidence by adopting various approaches and using different sets of stylometric features. (Carne & Ishihara, 2020; Ishihara, 2014, 2017a, 2017b, 2021). However, only few features have been tested in the similarity-only score-based approach (Ishihara, 2021), and there are many features left to be further investigated. To achieve the aim of the study, we will investigate some of the features in LR-based FTC and demonstrate how they contribute to the further improvement of the LR-based FTC system. Statistic, word n-gram (n=1,2,3), character n-gram (n=1,2,3,4), and part of speech (POS) n-gram (n=1,2,3) features were separately tested first in this study, and then the separately estimated LRs were fused for overall LRs. The databased used was prepared by Ishihara (2021), and the documents of comparison were modelled into feature vectors using a bag-of-words model. Two groups of documents, which both contained documents of 700, 1,400, and 2,100 words, were concatenated for each author, resulting in the total of 719 same-author comparisons and 516,242 different-author comparisons. The Cosine similarity was used to measure the similarity of texts, and the similarity-only score-based approach was used to estimate the LRs from the scores of similarity (Helper et al., 2012; Bolck et al., 2015). Log-likelihood ratio cost (Cllr) and their composites—Cllrmin and Cllrcal—were used as assessment metrics. Findings indicate that (a) when the LRs of all the feature types are fused, the fused Cllr values are 0.56, 0.30, and 0.19 for 700, 1,400, and 2,100 words, respectively, and (b) feature selection depending on the nature of an FTC task matters to the performance of the FTC system and can contribute to the improvement of LR-based FTC

Automatic Discovery of Non-Compositional Compounds in Parallel Data

Automatic segmentation of text into minimal content-bearing units is an unsolved problem even for languages like English. Spaces between words offer an easy first approximation, but this approximation is not good enough for machine translation (MT), where many word sequences are not translated word-for-word. This paper presents an efficient automatic method for discovering sequences of words that are translated as a unit. The method proceeds by comparing pairs of statistical translation models induced from parallel texts in two languages. It can discover hundreds of non-compositional compounds on each iteration, and constructs longer compounds out of shorter ones. Objective evaluation on a simple machine translation task has shown the method's potential to improve the quality of MT output. The method makes few assumptions about the data, so it can be applied to parallel data other than parallel texts, such as word spellings and pronunciations.Comment: 12 pages; uses natbib.sty, here.st