Tomomi Inada's resignation as defense minister ended a tenure that often made reporters wonder if her transgressions had more to do with ignorance than with incompetence. It would be wrong to associate her failures with her sex, though there were some in the media who harped on her fashion sense or supposed emotional instability as indications that she wasn't suitable for the job. Inada didn't actively discourage these indications. In June, she addressed the second plenary session of the International Institute of Strategic Studies' Shangri-La Dialogue in Singapore, where she expressed in English how privileged she felt to "share the podium" with other defense ministers, namely Marise Payne of Australia and Sylvie Goulard of France, saying that "We belong to the same gender … the same generation and, most importantly, we are all good looking." As mentioned in a June 14 article in the Huffington Post, Mayumi Mori, the Asahi Shimbun Singapore correspondent, noted that Inada was obviously making a joke "to relieve tension," and that there were a few chuckles in the hall.

Especially in the community of Digital Humanities, the automated processing of Latin texts has always been a popular research topic. In a variety of computational applications, such as text reuse detection [Franzini et al, 2015], it is desirable to annotate and augment Latin texts with useful morpho-syntactical or lexical information, such as lemmas. In this paper, we will focus on two sequence tagging tasks for medieval Latin: part-of-speech tagging and lemmatization. Given a piece of Latin text, the task of lemmatization involves assigning each word to a single dictionary headword or'lemma': a baseform label (preferably in a normalized orthography) grouping all word tokens which only differ in spelling and/or inflection [Knowles et al, 2004]. The task of lemmatization is closely related to that of part-of-speech (PoS) tagging [Jurafsky et al, 2000], in which each word in a running text should be assigned a tag indicating its part of speech or word class (e.g.

Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web. Structured prediction models are used in these tasks to take advantage of correlations between co-occurring labels and visual input but risk inadvertently encoding social biases found in web corpora. In this work, we study data and models associated with multilabel object classification and visual semantic role labeling. We find that (a) datasets for these tasks contain significant gender bias and (b) models trained on these datasets further amplify existing bias. For example, the activity cooking is over 33% more likely to involve females than males in a training set, and a trained model further amplifies the disparity to 68% at test time. We propose to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for collective inference. Our method results in almost no performance loss for the underlying recognition task but decreases the magnitude of bias amplification by 47.5% and 40.5% for multilabel classification and visual semantic role labeling, respectively.

Resolving abstract anaphora is an important, but difficult task for text understanding. Yet, with recent advances in representation learning this task becomes a more tangible aim. A central property of abstract anaphora is that it establishes a relation between the anaphor embedded in the anaphoric sentence and its (typically non-nominal) antecedent. We propose a mention-ranking model that learns how abstract anaphors relate to their antecedents with an LSTM-Siamese Net. We overcome the lack of training data by generating artificial anaphoric sentence--antecedent pairs. Our model outperforms state-of-the-art results on shell noun resolution. We also report first benchmark results on an abstract anaphora subset of the ARRAU corpus. This corpus presents a greater challenge due to a mixture of nominal and pronominal anaphors and a greater range of confounders. We found model variants that outperform the baselines for nominal anaphors, without training on individual anaphor data, but still lag behind for pronominal anaphors. Our model selects syntactically plausible candidates and -- if disregarding syntax -- discriminates candidates using deeper features.

Structured Prediction Energy Networks (SPENs) are a simple, yet expressive family of structured prediction models (Belanger and McCallum, 2016). An energy function over candidate structured outputs is given by a deep network, and predictions are formed by gradient-based optimization. This paper presents end-to-end learning for SPENs, where the energy function is discriminatively trained by back-propagating through gradient-based prediction. In our experience, the approach is substantially more accurate than the structured SVM method of Belanger and McCallum (2016), as it allows us to use more sophisticated non-convex energies. We provide a collection of techniques for improving the speed, accuracy, and memory requirements of end-to-end SPENs, and demonstrate the power of our method on 7-Scenes image denoising and CoNLL-2005 semantic role labeling tasks. In both, inexact minimization of non-convex SPEN energies is superior to baseline methods that use simplistic energy functions that can be minimized exactly.

SHRDLU is the first computer program which accepts natural language as input for moving toy blocks in a virtual world. It accepts commands like "put red pyramid on top of green square" and translate to physical actions inside the virtual world. NLP (Natural Language processing) is the techniques used for Natural Language Understanding (NLU) which plays a major role in Artificial Intelligence (AI). NLP helps the AI systems to process the data for knowledge representation,reasoning and Machine Learning.The objective of NLP is to make the machines as intelligent as human beings in understanding language. NLP fills the gap between human communication(natural langauge) and what the computer understands(machine learning).NLP helps in mapping the given input in natural language to useful representations The availability of large computational resources and data helped in the statistical revolution in computer applications like speech recognition, probabilistic models, data science, machine learning etc. Different Statistical methods are used in NLP applications like From the above mentioned linguistic properties phonetics and lexicons are mostly used for speech to text conversion systems.

This will be a very short tutorial on how to train a CoreNLP POS model for Swedish, as it does not exist one for CoreNLP "package" and I haven't found one open source out there just yet. From Wikipedia: "part-of-speech tagging (POS tagging or POST), also called grammatical tagging or word-category disambiguation, is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition, as well as its context" It is also sometimes called shallow parsing, since it is not creating a deeper structure of the different parts of the sentence. A sort of POS tagging is what you are learnt the first years in school, in the identification of words as nouns, verbs, adjectives, adverbs. First we need some training data for our Swedish POS-tagger, I've used the http://stp.lingfil.uu.se/ nivre/swedish_treebank/ for the Talbanken part, they also provide a conversion to Stanford dependencies. After we've downloaded it, we get two files, After you've downloaded POS-tagger part (use the -full, to get all the models, german and french etc) it's time to create your .props

The U.S. has a racial wealth gap problem. By one estimate, at current levels of wealth growth it would take 228 years for the average black family to catch up with levels of wealth among white families. Thomas Shapiro explains some of the surprising reasons parity remains so elusive in his book, Toxic Inequality: How America's Wealth Gap Destroys Mobility, Deepens the Racial Divide, and Threatens Our Future.

I'm happy to say that I've been able to create the first fully working demo here: I'm totally a noob but in the last 3 days I have learned a lot in creating this like SciKit, nltk, WSGI, Google Big Query, Google Datalabs, etc. Thanks everyone for the guidance in my last thread!

We introduce a simple and accurate neural model for dependency-based semantic role labeling. Our model predicts predicate-argument dependencies relying on states of a bidirectional LSTM encoder. The semantic role labeler achieves competitive performance on English, even without any kind of syntactic information and only using local inference. However, when automatically predicted part-of-speech tags are provided as input, it substantially outperforms all previous local models and approaches the best reported results on the English CoNLL-2009 dataset. We also consider Chinese, Czech and Spanish where our approach also achieves competitive results. Syntactic parsers are unreliable on out-of-domain data, so standard (i.e., syntactically-informed) SRL models are hindered when tested in this setting. Our syntax-agnostic model appears more robust, resulting in the best reported results on standard out-of-domain test sets.