So, shall we eke out an incremental existence, parasitic on linguistic theories, lan- guage corpora, and machine learning algorithms developed by others? Are we content to tweak parameters and deliver results that are surpassed at next year’s meeting, while important sources of new data are falling silent? It’s time that we focused some of our efforts on a new kind of computational linguistics, one that accelerates the documenta- tion and description of the world’s endangered linguistic heritage, and delivers tangible and intangible value to future generations. Who knows, we may even postpone the day when these languages utter their last words.
In an earlier Last Words piece, Ken Church (Church 2006) pointed out how the ACL conference reviewing process can be derailed by the lack of positive endorsement by reviewers who are not well qualiﬁed to review a given paper. He went on to suggest that papers rejected by NAACL are “often strong contenders for the best-paper award at ACL.” An instance of this phenomenon was observed in 2009, when a paper rejected from NAACL 2009 with an average acceptance score of 2.3 out of 5 was given a best paper award at ACL 2009 (Branavan et al. 2009). 1
However, it exhibits all the problems to which we have always known MT is heir. Both pronouns “he” in the last paragraph will be understood as referring to Franz, whereas in the reference text it is Miles Osborne who does the commending and the pointing out. Moreover, the alarming rumor of the latter’s death has been greatly exag- gerated by the English language-model: The reference text says he “spent a sabbatical last year working on the Google project.” The human Arabic translation says much the same, but the Arabic words for spent and died are homographs, and the newswire-based model favors the latter.
To date, cleaning has been done in isolation (and it has not been seen as interesting enough to publish on). Resources have not been pooled, and it has been done cursorily, if at all. Thus, a paper which describes work with a vast Web corpus of 31 million pages devotes just one paragraph to the corpus development process, and mentions de-duplication and language-filtering but no other cleaning (Ravichandran, Pantel, and Hovy 2005, Section 4). A paper using that same corpus notes, in a footnote, ”as a preprocessing step we hand-edit the clusters to remove those containing non-English words, terms related to adult content, and other Webpage-specific clusters” (Snow, Jurafsky, and Ng 2006). The development of open-source tools that identify and filter out each of the many sorts of ‘dirt’ found in Web pages to give clean output will have many beneficiaries, and the CLEANEVAL project 3 has been set up to this end. There will, of course, be differences of opinion about what should be filtered out, and a full toolset will provide a range of options as well as provoke discussion on what we should include and exclude to develop a low-noise, general-language corpus that is suitable for linguistic and language technology research by a wide range of researchers. (In the following, I call the data that meet these criteria “running text.”)
Consider sentiment analysis, for instance, which is the automatic extraction of “opinion-oriented” information (e.g., whether an author feels positive or negative about a certain product) from text. This is a prime example of an emerging research area in computational linguistics which moves beyond factual information exchange (although the preferred approach to this problem very much ﬁts with the paradigm sketched by Halevy et al. : take a large set of data and apply machine learning to it). Pang and Lee (2008) offer an extensive overview of research related to sentiment analysis, but do not discuss any of the psychological studies mentioned herein (in fact, of the 332papers they cite, only one or two could conceivably be interpreted as psychological in the broadest interpretation). What is especially interesting is that their discussion of why sentiment analysis is difﬁcult echoes the discussion of Chung and Pennebaker (2007) on the problems of counting words (by sheer coincidence they even discuss essentially the same example: madden).
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Though machine learning applied to big data is producing remarkable results, a gap stands between capitalizing on human language content and the structured data needed by analytic tools. For instance, one can draw illuminating social network graphs merely from the metadata of human communications (e.g., who is talking to whom, when, and where), but analysis of what they are communicating is lacking. Though co-occurrence of words/phrases/topics/names in messages can suggest that a relation exists between them, the type of relation is lacking. Though the following statistics are estimates only, the magnitude of public human language communication is staggering:
In contrast to Jesus other voices in the scene challenge and mock, ironically bringing into relief the identity of Jesus and his power to save himself and others. The leaders mock him as Messiah noting that while he can save others he cannot save himself (Luke 23:35). Soon after the soldiers challenge him to save himself if he is the King of the Jews (Luke 23:37), and then one of the criminals derides him and challenges him to save himself, and them, if he is the Messiah (Luke 23:39). In spite of the challenges Jesus dies an exemplary and noble death, true to his mission unbowed and undaunted, compassionate and faithful. This much at least is clear -‐ violence and rejection do not have the last word here.
But there is a deeper reason. Linguistics, as a discipline, went astray: It focused mainly on syntax (and predominantly on English); and its theory became so obscure, so baroque, and so self-centered, that it became virtually impenetrable to researchers from other disciplines. To use the terminology of Evans and Levinson (in press), “the relevant literature is forbiddingly opaque to outsiders”; or, in the words of Tomasello (1995, page 136), linguistic theories are “described in linguistically speciﬁc terms such that it is very difﬁcult to relate them to cognition in other psychological domains.” Or to computational implementation, for that matter.
With over 900 separate submissions, one might wonder if all breakthroughs in our field are really made in late fall or winter, just in time for these deadlines. If they’re not, why is it that these deadlines seem to define when new results are announced? Is there no credit to be gained from really being the first to publish some new method or theory or some clever take on an old one? Or are there no places to publish that will guarantee catching the field’s immediate attention (our equivalent of Science, Nature, or YouTube)? In short, why the veritable flood of words crashing up against conference deadlines and the veritable trickle reaching the editorial offices of the significant (and still growing) number of CL/NLP-related journals. A choice is clearly being made by researchers in the field, but is it one that should be encouraged? Could change bring about some better situation?
Applying this principle to shared tasks in practice, we propose making the primary competition a “public track,” where participants can use any code, data, and pre-trained models they want, as long as others can then freely obtain them. In other words: All resources used to participate in the shared task should be subsequently shared with the community. Although this does not ensure equal access to resources for the current edition, it will still ensure a progressively more equal footing for the future. We believe this is the crucial step to move the field forward, as everyone will have access to the resources used in state-of-the-art systems. To keep participation possible for teams who cannot or will not make all resources available, a secondary, “proprietary track” can be established.
At the June 2015 opening of the Facebook AI Research Lab in Paris, its director Yann LeCun said: “The next big step for Deep Learning is natural language under- standing, which aims to give machines the power to understand not just individual words but entire sentences and paragraphs.” 1 In a November 2014 Reddit AMA (Ask Me Anything), Geoff Hinton said, “I think that the most exciting areas over the next five years will be really understanding text and videos. I will be disappointed if in five years’ time we do not have something that can watch a YouTube video and tell a story about what happened. In a few years time we will put [Deep Learning] on a chip that fits into someone’s ear and have an English-decoding chip that’s just like a real Babel fish.” 2 And Yoshua Bengio, the third giant of modern Deep Learning, has
If we follow these lines of thought to their logical conclusions, we inevitably converge on a mode of publication that is quite different from the one we are familiar with today, and one which is much faster and much slimmer. The slimness comes from the potential for the removal of the redundancy that current practices encourage. I am surely not the only researcher to become impatient when reading several articles by the same author, all subtly different but clearly involving reuse of large slabs of material. A better model is one where any given individual’s research on a particular topic is collected together, without redundancy, in one place. This might be a single Web page or a collection of pages, which for the lack of a better term I’ll call a ‘unipaper’. A unipaper is something that is added to and developed over time, with corrections and revisions incorporated; a single individual might produce only one unipaper in his or her lifetime, and few would create more than two or three, each dedicated to distinct research topics. No more tiny increments on last year’s paper wrapped up as eight pages of text, with 50–75% being recycled from earlier publications: If this year’s contribution can be summed up in a few (numbered!) paragraphs, then these individually citable elements get added to the unipaper.
I am also disappointed that Computational Linguistics and ACL conferences do not publish a broader range of papers on “problems involving natural language and computation” (especially as the ACL Web site states that ACL is “the international scientific and professional society for people working on problems involving nat- ural language and computation”). In Table 2, I list recent language-related articles published in Artificial Intelligence (which is the most prestigious general AI journal). These articles address topics that I rarely see in ACL venues these days (but which I did see 10–20 years ago), such as user modeling, knowledge representation, inte- gration of linguistic and visual information, and computational cognitive modeling. These articles, incidentally, have a very different citation pattern from Computational Linguistics papers (Table 1); only 11% of journal citations are to NLP and speech journals, whereas 31% are to AI journals, and 25% (!!) are to psychology journals. In other words, language-related papers published in Artificial Intelligence (unlike papers in Computational Linguistics) show considerable awareness of the broader language research community.
Polarity Tags: We define features that represent the sentiment of the words in the two spans. Each word’s polarity was assigned according to its en- try in the Multi-perspective Question Answering Opinion Corpus (Wilson et al., 2005). In this re- source, each sentiment word is annotated as posi- tive, negative, both, or neutral. We use the number of negated and non-negated positive, negative, and neutral sentiment words in the two text spans as features. If a writer refers to something as “nice” in Arg1, that counts towards the positive sentiment count (Arg1Positive); “not nice” would count to- wards Arg1NegatePositive. A sentiment word is negated if a word with a General Inquirer (Stone et al., 1966) Negate tag precedes it. We also have features for the cross products of these polarities between Arg1 and Arg2.
The echo words are discussed under the reduplication head of morphology. The echo words are formed by replacing a part of the base form. Both the languages, the replacement is occur in initial part, last part and some time phoneme is added to the reduplication form. Sometime, the base form of the echo words is occur as free base for and bound base form. The main objectives of this paper find out the feature and function on echo words. It also tries to drown on similarities and dissimilarities of Bodo and Garo languages.
Craik and Lockhart conclude that a set of processes ensures that information is understood and eventually remembered (1972). The more features a word, image or sound takes into account, the more information could be analyzed and stored. This is also known as “Depth of Processing”. Input that is known and has some kind of meaning is easier to analyze on a deeper level than abstract input. For this reason the first kind of input can be stored better. The first kind of input could also be images and sentences. The input in this theory is not specifically linguistic, but it can be applied to words and sentences. Danan (2004) concludes that subtitling increases comprehension and leads to additional cognitive benefits, such as the greater depth of processing.
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5 use of antifibrinolytic drugs . The aim of this review is to examine the available evidence on use of NSAIDs patients with haemophilia, risk of GI bleeding, CV risk and other risks associated with these drugs and to address the question of whether there are any NSAIDs that are more suitable for use in PWH. To achieve this a literature search was carried out on PubMed for publications in the last 30 years using the following words or phrases: h(a)emophilia, arthropathy, pain management of haemophilia, NSAIDs, COX-2 inhibitors, gastrointestinal bleeding, cardiovascular risk, stroke, renal disease, management of haemophilic arthropathy, Histamine receptor 2 blockers, proton pump inhibitors,
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On this research, the results show me that the best option for desalting any seawater is using the test No. 2013-3 because the chemical reactives are very cheap and I had good efficiency. The process may be excited a liter of seawater with chemical reactives with ultrasound and flotation cell and a last step using reverse osmosis.
In the Transformer model (Vaswani et al., 2017), the decoding is still autoregressive, but un- like the RNN decoder, the generation of each word conditions on the whole prefix sequence and not only on the last word. This makes it non-trivial to apply scheduled sampling directly for this model. Since the Transformer achieves state-of-the-art re- sults and has become a default choice for many natural language processing problems, it is inter- esting to adapt and explore the idea of scheduled sampling for it, and, to our knowledge, no way of doing this has been proposed so far.
This leaves the question of what is the appropriate modication to the machine-closure condition for progress properties. Recall that machine- closure was derived from the requirement that a complete program be imple- mentable in practice. Ignoring the initial predicate, machine-closure asserts that any nite execution satisfying the next-state relation can be completed to an execution satisfying the next-state relation and the progress property. We similarly require that the partial program be implementable in practice, except now we have the additional requirement that it be implementable without knowing its environment. In other words, the implementation must work regardless of what the environment does. We therefore require that given any nite behavior prex in which the program's actions satisfy the next-state relation, there is a strategy that the program can play from that point on and \win"|that is, produce a behavior satisfying the next-state relation and the progress property.
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