World Standards Day 2011—14 October 2011 International standards – Creating confidence globally Message from IEC, ISO and ITU Dr. Klaus WUCHERER ， IEC President Dr. Boris ALESHIN ， ISO President Dr. Hamadoun TOURé ， ITU Secretary-General In today’s world we need to have a high level of expectation that things will work the way we expect them to work. We expect that when we pick up the phone we will be able to instantly connect to any other phone on the planet. We expect to be able to connect to the Internet and be provided with news and information… instantly. When we fall ill, we rely on the healthcare equipment used to treat us. When we drive our cars, we have confidence that the engine management, steering and braking, and child safety systems are reliable. We expect to be protected against electrical power failure and the harmful effects of pollution. International standards give us this confidence globally. Indeed one of the key objectives of standardization is to provide this confidence. Systems, products and services perform as we expect them to because of the essential features specified in international standards. International standards for products and services underpin quality, ecology, safety, reliability, interoperability, efficiency and effectiveness. They do all of this while giving manufacturers confidence in their ability to reach out to global markets safe in the knowledge that their product will perform globally. Interoperability creates economies of scale and ensures users can obtain equal service wherever they travel. So international standards benefit consumers, manufacturers and service providers alike. Importantly, in developing countries this accelerates the deployment of new products and services and encourages economic development. International standards create this confidence by being developed in an environment of openness and transparency, where every stakeholder can contribute. It is the stated aim of the WSC partners – IEC, ISO and ITU – to facilitate and augment this confidence globally, so as to connect the world with international standards.
曲津華 睡眠是個好東西——無論怎樣強調和讚美，都不過分！ 世界睡眠日據說是由 World Association of Sleep Medicine (WASM) 發起的（我翻譯為“世界睡療協會”）。但每年都要慶祝的世界睡眠日，具體是哪一天？有 3 月 21 日版本，也有如下維基百科的多種版本。 YEAR DATE SLOGAN World Sleep Day 2008 14 March Sleep well, live fully awake 睡得好，生活好 World Sleep Day 2009 20 March Drive alert, arrive safe 睡眠質量高，駕車更安全 World Sleep Day 2010 19 March Sleep Well, Stay Healthy 好睡好健康 World Sleep Day 2011 18 March Sleep Well, Grow Healthy 睡得好，更健康 不 管它了，今晚就好好睡一下先——默念口號“睡得好，更健康”，美美地睡…… P.S. 睡療，比水療簡便、綠色、低碳。咱，值得擁有！ 早前有一篇歌頌睡眠的小文 SLEEPING IS NEVER TIME WASTING 供參考。 http://bbs.sciencenet.cn/home.php?mod=spaceuid=247430do=blogid=290123 2011-03-21
转 哈工大信息检索中心论坛上的一个帖子，是Yorick Wilks在本世纪之初写的对WSD的疑惑。帖子原地址： http://bbs.langtech.org.cn/viewthread.php?tid=2046 全文如下： Is Word Sense Disambiguation just one more NLP task? Is Word Sense Disambiguation just one more NLP task? Yorick Wilks Abstract: The paper compares the tasks of part-of-speech (POS) tagging and word-sense-tagging or disambiguation (WSD), and argues that the tasks are not related by fineness of grain or anything like that, but are quite different kinds of task, particularly because there is nothing in POS corresponding to sense novelty. The paper also argues for the reintegration of sub-tasks that are being separated for evaluation. Introduction I want to make clear right away that I am not writing as a sceptic about word-sense disambiguation (WSD) let alone as a recent convert: on the contrary, since my PhD thesis was on the topic thirty years ago. That (Wilks, 1968) was what we would now call a classic AI toy system approach, one that used techniques later called Preference Semantics, but applied to real newspaper texts, as controls on the philosophical texts that were my real interest at the time. But it did attach single sense representations to words drawn from a polysemous lexicon of 800 or so. If Boguraev was right, in his informal survey twelve years ago, that the average NLP lexicon was under fifty words, then that work was ahead of its time and I do therefore have a longer commitment to, and perspective on, the topic than most, for whatever that may be worth!. I want to raise some general questions about WSD as a task, aside from all the busy work in SENSEVAL: questions that should make us worried and wary about what we are doing here, but definitely NOT stop doing it. I can start by reminding us all of the obvious ways in which WSD is not like part-of-speech (POS) tagging, even though the two tasks are plainly connected in information terms, as Stevenson and I pointed out in (Wilks and Stevenson, 1998a), and were widely misunderstood for doing so. From these differences, of POS and WSD, I will conclude that WSD is not just one more partial task to be hacked off the body of NLP and solved. What follows acknowledges that Resnik and Yarowsky made a similar comparison in 1997 (Resnik and Yarowsky, 1997) though this list is a little different from theirs: There is broad agreement about POS tags in that, even for those committed to differing sets, there is little or no dispute that they can be put into one-many correspondence. That is not generally accepted for the sets of senses for the same words from different lexicons. There is little dispute that humans can POS tag to a high degree of consistency, but again this is not universally agreed for WS tagging, as various email discussions leading up to this workshop have shown. I'll come back to this issue below, but its importance cannot be exaggerated -- if humans cannot do it then we are wasting our time trying to automate it. I assume that fact is clear to everyone: whatever maybe the case in robotics or fast arithmetic, in the NL parts of AI there is no point modelling or training for skills that humans do not have! I do not know the genesis of the phrase `` lexical tuning, but the phenomenon has been remarked, and worked on, for thirty years and everyone seems agreed that it happens, in the sense that human generators create, and human analysers understand, words in quite new senses, ungenerated before or, at least, not contained in the point-of-reference lexicon, whether that be thought of as in the head or in the computer. Only this view is consistent with the evident expansion of sense lists in dictionaries with time; these new additions cannot plausibly be taken as established usages not noticed before. If this is the case, it seems to mark an absolute difference from POS tagging (where novelty does not occur in the same way), and that should radically alter our view of what we are doing here, because we cannot apply the standard empirical modelling method to that kind of novelty. The now standard empirical paradigm of assumes prior markup, in the sense of a positive answer to the question (2) above. But we cannot, by definition, mark up for new senses, those not in the list we were initially given, because the text analysed creates them, or they were left out of the source from which the mark up list came. If this phenomenon is real, and I assume it is, it sets a limit on phenomenon (2), the human ability to pre-tag with senses, and therefore sets an upper bound on the percentage results we can expect from WSD, a fact that marks WSD out quite clearly from POS tagging. The contrast here is in fact quite subtle as can be seen from the interesting intermediate case of semantic tagging: which is the task of attaching semantic, rather than POS, tags to words automatically, a task which can then be used to do more of the WSD task (as in Dini et al., 1998) than POS tagging can, since the ANIMAL or BIRD versus MACHINE tags can then separate the main senses of `` crane. In this case, as with POS, one need not assume novelty in the tag set, but must allow for novel assignments from it to corpus words e.g. when a word like `` dog or `` pig was first used in a human sense. It is just this sense of novelty that POS tagging does also have, of course, since a POS tag like VERB can be applied to what was once only a noun, as with `` ticket. This kind of novelty, in POS and semantic tagging, can be pre-marked up with a fixed tag inventory, hence both these techniques differ from genuine sense novelty which cannot be premarked. As I said earlier, the thrust of these remarks is not intended sceptically, either about WSD in particular, or about the empirical linguistic agenda of the last ten years more generally. I assume the latter has done a great deal of good to NLP/CL: it has freed us from toy systems and fatuous example mongering, and shown that more could be done with superficial knowledge-free methods than the whole AI knowledge-based-NLP tradition ever conceded: the tradition in which every example, every sentence, had in principle to be subjected to the deepest methods. Minsky and McCarthy always argued for that, but it seemed to some even then an implausible route for any least-effort-driven theory of evolution to have taken. The caveman would have stood paralysed in the path of the dinosaur as he downloaded deeper analysis modules, trying to disprove he was only having a nightmare. However, with that said, it may be time for some corrective: time to ask not only how we can continue to slice off more fragments of partial NLP as tasks to model and evaluate, but also how to reintegrate them for real tasks that humans undoubtedly can evaluate reliably, like MT and IE, and which are therefore unlike some of the partial tasks we have grown used to (like syntactic parsing) but on which normal language users have no views at all, for they are expert-created tasks, of dubious significance outside a wider framework. It is easy to forget this because it is easier to keep busy, always moving on. But there are few places left to go after WSD:-empirical pragmatics has surely started but may turn out to be the final leg of the journey. Given the successes of empirical NLP at such a wide range of tasks, it is not to soon to ask what it is all for, and to remember that, just because machine translation (MT) researchers complained long ago that WSD was one of their main problems, it does not follow that high level percentage success at WSD will advance MT. It may do so, and it is worth a try, but we should remember that Martin Kay warned years ago that no set of individual solutions to computational semantics, syntax, morphology etc. would necessarily advance MT. However, unless we put more thought into reintegrating the new techniques developed in the last decade we shall never find out. Can humans sense tag? I wish now to return to two of the topics raised above: first, the human task: itself. It seems obvious to me that, aside from the problems of tuning and other phenomena that go under names like vagueness, humans, after training, can sense-tag texts at reasonably high levels and reasonable inter-annotator consistency. They can do this with alternative sets of senses for words for the same text, although it may be a task where some degree of training and prior literacy are essential, since some senses in such a list are usually not widely known to the public. This should not be shocking: teams of lexicographers in major publishing houses constitute literate, trained teams and they can normally achieve agreement sufficient for a large printed dictionary for publication (about sense sets, that is, a closely related skill to sense-tagging). Those averse to claims about training and expertise here should remember that most native speakers cannot POS tag either, though there seems substantial and uncontentious consistency among the trained. There is strong evidence for this position on tagging ability, which includes (Green, 1989 see also Jorgensen, 1990) and indeed the high figures obtained for small word sets by the techniques pioneered by Yarowsky (Yarowsky, 1995). Many of those figures rest on forms of annotation (e.g. assignment of words to thesaurus head sets in Roget), and the general plausibility of the methodology serves to confirm the reality of human annotation (as a consistent task) as a side effect. The counterarguments to this have come explicitly from the writings of Kilgarriff (1993), and sometimes implicitly from the work of those who argue from the primacy of lexical rules or of notions like vagueness in regard to WSD. In Kilgarriff's case I have argued elsewhere (Wilks, 1997) that the figures he produced on human annotation are actually consistent with very high levels of human ability to sense-tag and are not counter-arguments at all, even though he seems to remain sceptical about the task in his papers. He showed only that for most words there are some contexts for which humans cannot assign a sense, which is of course not an argument against the human skill being generally successful. On a personal note, I would hope very much to be clearer when I see his published reaction to the SENSEVAL workshop what his attitude to WSD really is. In writing he is a widely published sceptic, in the flesh he is the prime organiser of this excellent event (SENSEVAL Workshop) to test a skill he may, or may not, believe in. There need be no contradiction there, but a fascinating question about motive lingers in the air. Has he set all this up so that WSD can destroy itself when rigourously tested? One does not have to be a student of double-blind tests, and the role of intention in experimental design, to take these questions seriously, particularly as he has designed the SENSEVAL methodology and the use of the data himself. The motive question here is not mere ad hominem argument but a serious question needing an answer. These are not idle questions, in my view, but go to the heart of what the SENSEVAL workshop is for: is it to show how to do better at WSD, or is to say something about wordsense itself (which might involve saying that you cannot do WSD by computer at all, or cannot do it well enough to be of interest?). In all this discussion we should remember that, if we take the improvement of (assessable) real tasks as paramount, those like MT, Information Retrieval and Information Extraction (IE), then it may not in the end matter whether humans are ever shown psycholinguistically to need POS tagging or WSD for their own language performance;-there is much evidence they do not. But that issue is wholly separate from what concerns us here; it may still be useful to advance MT/IE via partial tasks like WSD, if they can be shown performable, assessable, and modelable by computers, no matter how humans turn out to work. The implicit critique of the broadly positive position above (i.e. that WSD can be done by people and machines and we should keep at it) sometimes seems to come as well from those who argue (a) for the inadequacy of lexical sense sets over productive lexical rules and (b) for the inherently vague quality of the difference between senses of a given word. I believe both these approaches are muddled if their proponents conclude that WSD is therefore fatally flawed as a task;- and clearly not all do since some of them are represented here as participants. Lexical Rules Lexical rules go back at least to Givon's (1967) thirty-year old sense-extension rules and they are in no way incompatible with a sense-set approach, like that found in a classic dictionary. Such sense sets are normally structured (often by part of speech and by general and specific senses) and the rules are, in some sense, no more than a compression device for predicting that structuring. But the set produced by any set of lexical rules is still a set, just as a dictionary list of senses is a set, albeit structured. It is mere confusion to think one is a set and one not: Nirenburg and Raskin (1997) have pointed out that those who argue against lists of senses (in favour of rules, e.g. Pustejovsky 1995) still produce and use such lists. What else could they do? I myself cannot get sufficient clarity at all on what the lexical rule approach, whatever its faults or virtues, has to do with WSD? The email discussion preceding this workshop showed there were people who think the issues are connected, but I cannot see it, but would like to be better informed before I go home from here. If their case is that rules can predict or generate new senses then their position is no different (with regard to WSD) from that of anyone else who thinks new senses important, however modelled or described. The rule/compression issue itself has nothing essential to do with WSD: it is simply one variant of the novelty/tuning/new-sense/metonymy problem, however that is described. The vagueness issue is again an old observation, one that, if taken seriously, must surely result in a statistical or fuzzy-logic approach to sense discrimination, since only probabilistic (or at least quantitative) methods can capture real vagueness. That, surely, is the point of the Sorites paradox: there can be no plausible or rational qualitatively-based criterion (which would include any quantitative system with clear limits: e.g. tall = over 6 feet) for demarcating `` tall, `` green or any inherently vague concept. If, however, sense sets/lists/inventories are to continue to play a role, vagueness can mean no more than highlighting what all systems of WSD must have, namely some parameter or threshold for the assignment to one of a list of senses versus another, or setting up a new sense in the list. Talk of vagueness adds nothing specific to help that process for those who want to assign on some quantitative basis to one sense rather than another; algorithms will capture the usual issue of tuning to see what works and fits our intuitions. Vagueness would be a serious concept only if the whole sense list for a word (in rule form or not) was abandoned in favour of statistically-based unsupervised clusters of usages or contexts. There have been just such approaches to WSD in recent years (e.g. Bruce and Wiebe, 1994, Pedersen and Bruce, 1997, Schuetze Pederson, 1995) and the essence of the idea goes back to Sparck Jones 1964/1986) but such an approach would find it impossible to take part in any competition like SENSEVAL because it would inevitably deal in nameless entities which cannot be marked up for. Vague and Lexical Rule based approaches also have the consequence that all lexicographic practice is, in some sense, misguided: dictionaries according to such theories are fraudulent documents that could not help users, whom they systematically mislead by listing senses. Fortunately, the market decides this issue, and it is a false claim. Vagueness in WSD is either false (the last position) or trivial, and known and utilised within all methodologies. This issue owes something to the systematic ignorance of its own history so often noted in AI. A discussion email preceding this workshop referred to the purported benefits of underspecification in lexical entries, and how recent formalisms had made that possible. How could anyone write such a thing in ignorance of the 1970s and 80s work on incremental semantic interpretation of Hirst, Mellish and Small (Hirst, 1987; Mellish, 1983; Small et al., 1988) among others? None of this is a surprise to those with AI memories more than a few weeks long: in our field people read little outside their own notational clique, and constantly `` rediscover old work with a new notation. This leads me to my final point which has to do, as I noted above, with the need for a fresh look at technique integration for real tasks. We all pay lip service to this while we spend years on fragmentary activity, arguing that that is the method of science. Well, yes and no, and anyway WSD is not science: what we are doing is engineering and the scientific method does not generally work there, since engineering is essentially integrative, not analytical. We often write or read of `` hybrid systems in NLP, which is certainly an integrative notion, but we have little clear idea of what it means. If statistical or knowledge-free methods are to solve some or most cases of any linguistic phenomenon, like WSD, how do we then locate that subclass of the phenomena that other, deeper, techniques like AI and knowledge-based reasoning are then to deal with? Conversely, how can we know which cases the deeper techniques cannot or need not deal with? If there is an upper bound to empirical methods, and I have argued that that will be lower for WSD than for some other NLP tasks for the reasons set out above, then how can we pull in other techniques smoothly and seamlessly for the `` hard examples? The experience of POS tagging, to return to where we started, suggests that rule-driven taggers can do as well as purely machine learning-based taggers, which, if true, suggests that symbolic methods, in a broad sense, might still be the right approach for the whole task. Are we yet sure this is not the case for WSD? I simply raise the question. Ten years ago, it was taken for granted in most of the AI/NLP community that knowledge-based methods were essential for serious NLP. Some of the successes of the empirical program (and especially the TIPSTER program) have caused many to reevaluate that assumption. But where are we now, if a real ceiling to such methods is already in sight? Information Retrieval languished for years, and maybe still does, as a technique with a practical use but an obvious ceiling, and no way of breaking through it; there was really nowhere for its researchers to go. But that is not quite true for us, because the claims of AI/NLP to offer high quality at NLP tasks have never been really tested. They have certainly not failed, just got left behind in the rush towards what could be easily tested! Large or Small-scale WSD? Which brings me to my final point: general versus small-scale WSD. Our group is one of the few that has insisted on continuing with general WSD: the tagging and test of all content words in a text, a group that includes CUP, XERC-Grenoble and CRL-NMSU. We currently claim about 90% correct sense assignment (Wilks and Stevenson, 1998b) and do not expect to be able to improve much on that for the reasons set out above; we believe the rest is AI or lexical tuning! The general argument for continuing with the all-word paradigm, rather than the highly successful paradigm of Yarowsky et al., is that that is the real task, and there is no firm evidence that the small scale will scale up to the large because much of sense-disambiguation is mutual between the words of the text, which cannot be used by the small set approach. I am not sure this argument is watertight but it seems plausible to me. Logically, if you claim to do all the content words you ought, in principle, to be able to enter a contest like SENSEVAL that does only some of the words with an unmodified system. This is true, but you will also expect to do worse, as you have not have had as much training data for the chosen word set. Moreover you will have to do far more preparation to enter if you insist, as we would, on bringing the engines and data into play for all the training and test set words; the effort is that much greater and it makes such an entry self-penalising in terms of both effort and likely outcome, which is why we decided not to enter in the first round, regretfully, but just to mope and wail at the sidelines. The methodology chosen for SENSEVAL was a natural reaction to the lack of training and test data for the WSD task, as we all know, and that is where I would personally like to see effort put in the future, so that everyone can enter all the words; I assume that would be universally agreed to if the data were there. It is a pity, surely, to base the whole structure of a competition on the paucity of the data. Conclusion What we would like to suggest positively is that we cooperate to produce more data, and use existing all-word systems, like Grenoble, CUP, our own and others willing to join, possibly in combination, so as to create large-scale tagged data quasi-automatically, rather in the way that the Penn tree bank was produced with the aid of parsers, not just people. We have some concrete suggestions as to how this can be done, and done consistently, using not only multiple WSD systems but also by cross comparing the lexical resources available, e.g. WordNet (or EuroWordNet) and a major monolingual dictionary. We developed our own reasonably large test/training set with the WordNet-LDOCE sense translation table (SENSUS, Knight and Luk, 1994) from ISI. Some sort of organised effort along those lines, before the next SENSEVAL, would enable us all to play on a field not only level, but much larger. Bibliography 1 Bruce, R. and Wiebe, J. (1994) Word-sense disambiguation using decomposable models, Proc. ACL-94. 2 Dini, L., di Tommaso, V. and Segond, F. (1998) Error-driven word sense disambiguation. In Proc. COLING-ACL98, Montreal. 3 Givon, T. (1967) Transformations of Ellipsis, Sense Development and Rules of Lexical Derivation. SP-2896, Systems Development Corp., Sta. Monica, CA. 4 Green, G. (1989) Pragmatics and Natural Language Understanding. Erlbaum: Hillsdale, NJ. 5 Hirst, G. (1987) Semantic Interpretation and the Resolution of Ambiguity, CUP: Cambridge, England. 6 Jorgensen, J. (1990) The psychological reality of word senses, Journal of Psycholinguistic Research, vol 19. 7 Kilgarriff, A. (1993) Dictionary word-sense distinctions: an enquiry into their nature, Computers and the Humanities, vol 26. 8 Knight, K. and Luk, S. (1994) Building a Large Knowledge Base for Machine Translation, Proceedings of the American Association for Artificial Intelligence Conference AAAI-94, pp. 185-109, Seattle, WA. 9 Mellish, C. (1983) Incremental semantic interpretation in a modular parsing system. In K. Sparck-Jones and Y. Wilks (eds.) Automatic Natural Language Parsing, Ellis Horwood/Wiley: Chichester/NYC. 10 Nirenburg, S. and Raskin., V. (1997) Ten choices for lexical semantics. Research Memorandum, Computing Research Laboratory, Las Cruces, NM. 11 Pedersen, T. and Bruce, R. (1997) Distinguishing Word Senses in Untagged Text, Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, pp. 197-207, Providence, RI. 12 Pustejovsky, J. (1995) The Generative Lexicon, MIT Press: Cambridge, MA. 13 Resnik, P. and Yarowsky, D. (1997) A Perspective on Word Sense Disambiguation Techniques and their Evaluation, Proceedings of the SIGLEX Workshop ``Tagging Text with Lexical Semantics: What, why and how?'', pp. 79-86, Washington, D.C. 14 Schutze, H. (1992) Dimensions of Meaning, Proceedings of Supercomputing '92, pp. 787-796, Minneapolis, MN. 15 Schutze, H. and Pederson, J. (1995) Information Retrieval based on Word Sense, Proc. Fourth Annual Symposium on Document Analysis and Information Retrieval. Las Vegas, NV. 16 Small, S., Cottrell, G., and Tanenhaus, M. (Eds.) (1988) Lexical Ambiguity Resolution, Morgan Kaufmann: San Mateo, CA. 17 Sparck Jones, K. (1964/1986) Synonymy and Semantic Classification. Edinburgh UP: Edinburgh. 18 Wilks, Y. (1968) Argument and Proof. Cambridge University PhD thesis. 19 Wilks, Y. (1997) Senses and Texts. Computers and the Humanities. 20 Wilks, Y. and Stevenson, M. (1998a) The Grammar of Sense: Using part-of-speech tags as a first step in semantic disambiguation, Journal of Natural Language Engineering, 4(1), pp. 1-9. 21 Wilks, Y. and Stevenson, M. (1998b) Optimising Combinations of Knowledge Sources for Word Sense Disambiguation, Proceedings of the 36th Meeting of the Association for Computational Linguistics (COLING-ACL-98), Montreal, Canada. 22 Yarowsky, D. (1995) Unsupervised Word-Sense Disambiguation Rivaling Supervised Methods, Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (ACL-95), pp. 189-196, Cambridge, MA. About this document ... Is Word Sense Disambiguation just one more NLP task? This document was generated using the LaTeX2HTML translator Version 99.2beta6 (1.42) Copyright 1993, 1994, 1995, 1996, Nikos Drakos, Computer Based Learning Unit, University of Leeds. Copyright 1997, 1998, 1999, Ross Moore, Mathematics Department, Macquarie University, Sydney. The command line arguments were: latex2html cs9812.tex The translation was initiated by Gillian Callaghan on 2000-03-29
Well, this is the very first writing for my blog. I plan to use it to record my thinking and to exchange with fellows up here. I am currently reading word sense disambiguation: algorithm and applications. In the first two ariticles, one topic is focused, namely the invertory of word senses. Adam Kilgarriff, Nancy Ide and Yorick Wilks are proposing the the work of lexicographer may not be suitable for WSD and other NLP tasks such as information retrieval and machine translation. Thus they think it's high time to reexamine the theories of word sense and do the word sense inventory once again. I find this kind of idea interesting, as I was annotating some chinese polyseme words such as 大, 小 using HowNet as reference semantic system. What is always puzzling me is that there are always cases where I can not make a decision as to what sense option to apply. Sometimes more then one sense seems acceptable by intuition and some other times none seems to fit. The idea of homograph as is proposed in IdeWilks surely can avoid this problem. But when it comes to tasks where more precise sense division is needed, for example, metaphor recognition, homograph way not work. But one thing is for sure, the topic of sense inventory needs to be furthur considered.
标签: WSD
