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Because each twisted pair is connected to a balun transformer and common mode noise rejection circuitry at both ends of the circuit-the network interface card NIC and the network equipment-differences in the turn ratios and common mode ground impedances can result in common mode noise. The magnitude of the induced noise on the twisted pairs can be reduced, but not eliminated, through the use of common mode terminations, chokes, and filters within the equipment.

Note that it is not mandatory for equipment manufacturers to provide a low-impedance building ground path from the shielded 8-pin modular RJ jack through the equipment chassis. Sometimes, the chassis is isolated from the building ground with a protective RC circuit and, in other cases, the shielded RJ jack is completely isolated from the chassis ground. TIA and ISO standards identify the threshold when an excessive ground loop develops as when the difference in potential between the voltage measured at the shield at the work-area end of the cabling, and the voltage measured at the ground wire of the electrical outlet used to supply power to the workstation, exceeds 1.

This difference in potential should be measured and corrected in the field to ensure proper network equipment operation, but values in excess of 1. Furthermore, because the common mode voltage induced by ground loops is low frequency 50 or 60 Hz and their respective harmonics , the balance performance of the cabling plant itself is sufficient to ensure immunity regardless of the actual voltage magnitude. In fact, screens and shields, and the balanced twisted pairs in a UTP cable, are affected by differences in voltage potential at the end of the channel.

The difference in the transformer common mode termination impedance at the NIC and the network equipment naturally results in common mode noise current being induced on each twisted pair. The ANSI-J-STDA standard defines the building telecommunications grounding and bonding infrastructure that originates at the service equipment power ground and extends throughout the building. TIA and ISO standards provide one additional step for the grounding of screened and shielded cabling systems. Specifically, clause 4.

This procedure is intended to support the optimum configuration of one ground connection to minimize the appearance of ground loops, but recognizes that multiple ground connections may be present along the cabling. Part of the function of the screen or shield is to provide a low-impedance ground path for noise currents that are induced on the shielded material. For optimum alien crosstalk and noise immunity performance, shield continuity should be maintained throughout the end-to-end cabling system.

Building end users should perform a validation to ensure that screened and shielded cabling systems are properly grounded to the TGB or TMGB. While this difference in potential should be measured and corrected in the field to ensure proper network equipment operation, values in excess of 1. Furthermore, because the common mode voltage induced by ground loops is low frequency, the balance performance of the cabling plant is sufficient to ensure immunity regardless of the actual voltage magnitude. Shielding offers the benefits of significantly improved pair-to-pair crosstalk performance, alien crosstalk performance, and noise immunity that cannot be matched by any other cabling design strategy.

Optional drain wires are sometimes provided. Shielding materials are selected for their ability to maximize immunity to electric field disturbance, capability to reflect the incoming wave, absorption properties, and ability to provide a low-impedance signal path. As a rule, more conductive shielding materials yield greater amounts of incoming signal reflection. The thickness of the foil shield is influenced by the skin effect of the interfering noise currents.

Typical foil thicknesses are 1. This design approach ensures that higher frequency signals will not interfere with the twisted pairs as a result of their good balance performance. Braids and drain wires add strength to cable assemblies and further decrease the end-to-end electrical resistance of the shield when the cabling system is properly connected to ground.

A common myth says that screens and shields can behave as antennas because they are long lengths of metal. The fact is that both screens and shields, and the copper balanced twisted pairs in a UTP cable, will behave as an antenna to some degree. The difference is the noise that couples onto the screen or shield is actually to 1, times smaller in magnitude than the noise that is coupled onto an unshielded twisted pair in the same environment.

Here is an analysis of the two types of signal disturbers that can affect the noise immunity performance of balanced twisted-pair cabling:. Unfortunately, balance performance is no longer sufficient to ensure adequate noise immunity for UTP cabling at these higher frequencies. The potential for a cable to behave as an antenna can be experimentally verified by arranging two balanced cables in series, injecting a signal into one cable to emulate a transmit antenna across a swept frequency range, and measuring the interference on an adjacent cable to emulate a receiving antenna.

As a rule of thumb, the higher the frequency of the noise source, the greater the potential for interference. It should be noted that 40 dB of margin corresponds to times less voltage coupling, thus confirming the modeled predictions. A second antenna myth is that common mode signals appearing on a screen or shield can only be dissipated through a low-impedance ground path. The effects of leaving both ends of a foil twisted-pair cable ungrounded can also be verified by using the abovementioned experimental method.

Note that 20 dB of margin corresponds to 10 times less voltage coupling. Modeled and experimental results clearly dispel these antenna myth. Screens and shields offer substantially improved noise immunity compared to unshielded constructions above 30 MHz, even when improperly grounded. Achievable SNR margin is dependent upon the combined properties of cabling balance and the common mode and differential mode noise immunity provided by screens and shields. With the emergence of 10GBase-T, it has become clear that the noise isolation provided by good balance alone is just barely sufficient to support transmission objectives.

It is often said that the telecommunications industry has come full circle in the specification of its preferred media type. Correct: Avoid commas that are not necessary. At that point, nothing looks correct. Instead, review the comma rules covered in Chapter Use these rules as you write to help you correctly punctuate your documents. Incorrect: Of all U. Correct: Of all U. Never overuse exclamation marks. Instead of using exclamation marks, convey emphasis through careful, vivid word choice. Exclamation marks create an overwrought tone that often undercuts your point.

Incorrect: Use the semicolon correctly always use it where it is appropriate; and never where it is not suitable.

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Correct: Use the semicolon correctly; always use it where it is appropriate, and never where it is not suitable. This is covered in detail in Chapter Incorrect: louisa adams, Wife of john quincy Adams, was the first and only foreignborn First Lady. These include names, geographical places, specific historical events, eras, and documents, languages, nationalities, countries, and races. Capitalize the major words in titles of books, plays, movies, newspapers, and magazines. Incorrect: Proofread carefully to see if you have any words out.

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Correct: Proofread carefully to see if you have left any words out. This is a simple rule, but many people run out of time before they can proofread a document. Always make the time to proofread your writing. The errors will become much more obvious and easier to isolate. How can you use the previous 25 guidelines to improve your writing?

Try these ideas:. Instead, listen to the comments your readers mention when they discuss your writing. Keep track of the writing errors you make by checking your own work against the guidelines. Review this checklist every time you write an important document. To isolate your most common writing errors, select several pieces of your writing, such as memos, letters, or reports.

Just do the best you can. Implications for influenza forecasting are discussed in this report. Background: Public health officials and policy makers in the United States expend significant resources at the national, state, county, and city levels to measure the rate of influenza infection. Lee Westmaas and John W.

Ayers and Mark Dredze and Benjamin M. These tasks aimed to provide apples-to-apples comparisons of various approaches to modeling language relevant to mental health from social media. The data used for these tasks is from Twitter users who state a diagnosis of depression or post traumatic stress disorder PTSD and demographically-matched community controls. The unshared task was a hackathon held at Johns Hopkins University in November to explore the data, and the shared task was conducted remotely, with each participating team submitted scores for a held-back test set of users.

The shared task consisted of three binary classification experiments: 1 depression versus control, 2 PTSD versus control, and 3 depression versus PTSD. Classifiers were compared primarily via their average precision, though a number of other metrics are used along with this to allow a more nuanced interpretation of the performance measures. While separate embeddings are learned for each word, this is infeasible for every phrase. We construct phrase embeddings by learning how to compose word embeddings using features that capture phrase structure and context. We propose efficient unsupervised and task-specific learning objectives that scale our model to large datasets.

We demonstrate improvements on both language modeling and several phrase semantic similarity tasks with various phrase lengths. We make the implementation of our model and the datasets available for general use.

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Lexical embeddings can serve as useful representations for words for a variety of NLP tasks, but learning embeddings for phrases can be challenging. Language provides a natural lens for studying mental health -- much existing work and therapy have strong linguistic components, so the creation of a large, varied, language-centric dataset could provide significant grist for the field of mental health research.

We examine a broad range of mental health conditions in Twitter data by identifying self-reported statements of diagnosis. We systematically explore language differences between ten conditions with respect to the general population, and to each other. Our aim is to provide guidance and a roadmap for where deeper exploration is likely to be fruitful. Many significant challenges exist for the mental health field, but one in particular is a lack of data available to guide research. Yet integrating output from multiple analytics into a single framework can be time consuming and slow research progress.

Our pipeline includes data ingest, word segmentation, part of speech tagging, parsing, named entity recognition, relation extraction and cross document coreference resolution. Additionally, we integrate a tool for visualizing these annotations as well as allowing for the manual annotation of new data. We release our pipeline to the research community to facilitate work on Chinese language tasks that require rich linguistic annotations.

Natural language processing research increasingly relies on the output of a variety of syntactic and semantic analytics. However, the problem of entity linking for spoken language remains unexplored. Spoken language obtained from automatic speech recognition systems poses different types of challenges for entity linking; transcription errors can distort the context, and named entities tend to have high error rates. We propose features to mitigate these errors and evaluate the impact of ASR errors on entity linking using a new corpus of entity linked broadcast news transcripts.

Research on entity linking has considered a broad range of text, including newswire, blogs and web documents in multiple languages. We leverage multiple sources of semantic information, including temporal ordering constraints between events. These are combined in a max-margin framework to find a globally consistent view of entities and events across multiple documents, which leads to improvements over a very strong local baseline.

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We present a joint model for predicate argument alignment. While recent work has combined these word embeddings with hand crafted features for improved performance, it was restricted to a small number of features due to model complexity, thus limiting its applicability. We propose a new model that conjoins features and word embeddings while maintaining a small number of parameters by learning feature embeddings jointly with the parameters of a compositional model.

The result is a method that can scale to more features and more labels, while avoiding overfitting. Compositional embedding models build a representation for a linguistic structure based on its component word embeddings. The structured priors can be constrained to model topic hierarchies, factorizations, correlations, and supervision, allowing SPRITE to be tailored to particular settings. We demonstrate this flexibility by constructing a SPRITE-based model to jointly infer topic hierarchies and author perspective, which we apply to corpora of political debates and online reviews.

We show that the model learns intuitive topics, outperforming several other topic models at predictive tasks. We introduce SPRITE, a family of topic models that incorporates structure into model priors as a function of underlying components. Most such research has focused on English-language social media for the task of disease surveillance.

Objective: We investigated the value of Chinese social media for monitoring air quality trends and related public perceptions and response. The goal was to determine if this data is suitable for learning actionable information about pollution levels and public response. Methods: We mined a collection of 93 million messages from Sina Weibo, China's largest microblogging service. We experimented with different filters to identify messages relevant to air quality, based on keyword matching and topic modeling.

We evaluated the reliability of the data filters by comparing message volume per city to air particle pollution rates obtained from the Chinese government for 74 cities. Additionally, we performed a qualitative study of the content of pollution-related messages by coding a sample of messages for relevance to air quality, and whether the message included details such as a reactive behavior or a health concern.

Results: The volume of pollution-related messages is highly correlated with particle pollution levels, with Pearson correlation values up to. Our qualitative results found that Additionally, 3 messages of requested that action be taken to improve quality. Conclusions: We have found quantitatively that message volume in Sina Weibo is indicative of true particle pollution levels, and we have found qualitatively that messages contain rich details including perceptions, behaviors, and self-reported health effects.

Social media data can augment existing air pollution surveillance data, especially perception and health-related data that traditionally requires expensive surveys or interviews. Background: Recent studies have demonstrated the utility of social media data sources for a wide range of public health goals including disease surveillance, mental health trends, and health perceptions and sentiment. Both in terms of preparedness and response, public health officials and first responders have turned to automated tools to assist with organizing and visualizing large streams of social media.

In turn, this has spurred new research into algorithms for information extraction, event detection and organization, and information visualization. One challenge of these efforts has been the lack of a common corpus for disaster response on which researchers can compare and contrast their work.

This paper describes the Hurricane Sandy Twitter Corpus: 6. The growing use of social media has made it a critical component of disaster response and recovery efforts. A restricted form of domain-specific entity linking has, however, been tried with email, linking mentions of people to specific email addresses. This paper introduces a new test collection for the task of linking mentions of people, organizations, and locations to Wikipedia.

Furthermore, experiments with an existing entity linking system indicate that the absence of a suitable referent in Wikipedia can easily be recognized by automated systems, with NIL precision i. Most prior work on entity linking has focused on linking name mentions found in third-person communication e.

Many systems rely on prediction cascades to efficiently rank candidates. However, the design of these cascades often requires manual decisions about pruning and feature use, limiting the effectiveness of cascades. We present Slinky, a modular, flexible, fast and accurate entity linker based on prediction cascades.

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We adapt the web-ranking prediction cascade learning algorithm, Cronus, in order to learn cascades that are both accurate and fast. We show that by balancing between accurate and fast linking, this algorithm can produce Slinky configurations that are significantly faster and more accurate than a baseline configuration and an alternate cascade learning method with a fixed introduction of features. Entity linking requires ranking thousands of candidates for each query, a time consuming process and a challenge for large scale linking.

In this paper, we show that data from the microblogging community Twitter significantly improves influenza forecasting. Most prior influenza forecast models are tested against historical influenza-like illness ILI data from the U. These data are released with a one-week lag and are often initially inaccurate until the CDC revises them weeks later. Since previous studies utilize the final, revised data in evaluation, their evaluations do not properly determine the effectiveness of forecasting.

For a given level of accuracy, using Twitter data produces forecasts that are two to four weeks ahead of baseline models. Additionally, we find that models using Twitter data are, on average, better predictors of influenza prevalence than are models using data from Google Flu Trends, the leading web data source. Accurate disease forecasts are imperative when preparing for influenza epidemic outbreaks; nevertheless, these forecasts are often limited by the time required to collect new, accurate data.

We describe a topic modeling framework for discovering health topics in Twitter, a social media website. This is an exploratory approach with the goal of understanding what health topics are commonly discussed in social media. This paper describes in detail a statistical topic model created for this purpose, the Ailment Topic Aspect Model ATAM , as well as our system for filtering general Twitter data based on health keywords and supervised classification.

We show how ATAM and other topic models can automatically infer health topics in million Twitter messages from to These results demonstrate that it is possible to automatically discover topics that attain statistically significant correlations with ground truth data, despite using minimal human supervision and no historical data to train the model, in contrast to prior work.

Additionally, these results demonstrate that a single general-purpose model can identify many different health topics in social media. By aggregating self-reported health statuses across millions of users, we seek to characterize the variety of health information discussed in Twitter. Wallace and Michael J. Paul and Urmimala Sarkar and Thomas A.

In this demo paper, we describe data collection, processing, and features of the site. The goal of this service is to transition results from research to practice. We present HealthTweets. We identified nearly 1 million messages containing health-related keywords, filtered from a dataset of 93 million messages spanning five years. We applied probabilistic topic models to this dataset and identified the prominent health topics. We show that a variety of health topics are discussed in Sina Weibo, and that four flu-related topics are correlated with monthly influenza case rates in China.

This paper seeks to identify and characterize health-related topics discussed on the Chinese microblogging website, Sina Weibo. We propose a new learning objective that incorporates both a neural language model objective and prior knowledge from semantic resources to learn improved lexical semantic embeddings. We demonstrate that our embeddings improve over those learned solely on raw text in three settings: language modeling, measuring semantic similarity, and predicting human judgements.

Word embeddings learned on unlabeled data are a popular tool in semantics, but may not capture the desired semantics. We present Code-Switched LDA csLDA , which infers language specific topic distributions based on code-switched documents to facilitate multi-lingual corpus analysis. We experiment on two code-switching corpora English-Spanish Twitter data and English-Chinese Weibo data and show that csLDA improves perplexity over LDA, and learns semantically coherent aligned topics as judged by human annotators. Code-switched documents are common in social media, providing evidence for polylingual topic models to infer aligned topics across languages.

We present analysis of mental health phenomena in publicly available Twitter data, demonstrating how rigorous application of simple natural language processing methods can yield insight into specific disorders as well as mental health writ large, along with evidence that as-of-yet undiscovered linguistic signals relevant to mental health exist in social media. We present a novel method for gathering data for a range of mental illnesses quickly and cheaply, then focus on analysis of four in particular: post-traumatic stress disorder PTSD , major depressive disorder, bipolar disorder, and seasonal affective disorder.

We intend for these proof-of-concept results to inform the necessary ethical discussion regarding the balance between the utility of such data and the privacy of mental health related information. The ubiquity of social media provides a rich opportunity to enhance the data available to mental health clinicians and researchers, enabling a better-informed and better-equipped mental health field. Recent work has shown the utility of social media data for studying depression, but there have been limited evaluations of other mental health conditions.

We consider post traumatic stress disorder PTSD , a serious condition that affects millions worldwide, with especially high rates in military veterans. We show how to obtain a PTSD classifier for social media using simple searches of available Twitter data, a significant reduction in training data cost compared to previous work on mental health.

Traditional mental health studies rely on information primarily collected and analyzed through personal contact with a health care professional. Recently however, competing Social Media have begun to carry news.

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Here we examine how Facebook, Google Plus and Twitter report on breaking news. We consider coverage whether news events are reported and latency the time when they are reported. Using data drawn from three weeks in December , we identify 29 major news events, ranging from celebrity deaths, plague outbreaks to sports events.

We find that all media carry the same major events, but Twitter continues to be the preferred medium for breaking news, almost consistently leading Facebook or Google Plus. Facebook and Google Plus largely repost newswire stories and their main research value is that they conveniently package multitple sources of information together.

Twitter is widely seen as being the go to place for breaking news. We examine how performance changes without syntactic supervision, comparing both joint and pipelined methods to induce latent syntax. This work highlights a new application of unsupervised grammar induction and demonstrates several approaches to SRL in the absence of supervised syntax.

Our best models obtain competitive results in the high-resource setting and state-of-the-art results in the low resource setting, reaching We release our code for this work along with a larger toolkit for specifying arbitrary graphical structure. We explore the extent to which high-resource manual annotations such as treebanks are necessary for the task of semantic role labeling SRL. Typical approaches use a pipeline architecture that clusters the mentions using fixed or learned measures of name and context similarity.

In this paper, we propose a model for cross-document coreference resolution that achieves robustness by learning similarity from unlabeled data. The generative process assumes that each entity mention arises from copying and optionally mutating an earlier name from a similar context. Clustering the mentions into entities depends on recovering this copying tree jointly with estimating models of the mutation process and parent selection process.

We present a block Gibbs sampler for posterior inference and an empirical evalution on several datasets. On a challenging Twitter corpus, our method outperforms the best baseline by Entity clustering must determine when two named-entity mentions refer to the same entity. This paper summarizes our recently developed influenza infection detection algorithm that automatically distinguishes relevant tweets from other chatter, and we describe our current influenza surveillance system which was actively deployed during the full influenza season.

Our objective was to analyze the performance of this system during the most recent influenza season and to analyze the performance at multiple levels of geographic granularity, unlike past studies that focused on national or regional surveillance. Social media have been proposed as a data source for influenza surveillance because they have the potential to offer real-time access to millions of short, geographically localized messages containing information regarding personal well-being. Our system integrates popular lexical semantic resources into a simple discriminative model. PARMA achieves state of the art results.

We present a novel probabilistic model to learn the sub-word lexicon optimized for a given task. We consider the task of Out Of vocabulary OOV word detection, which relies on output from a hybrid system. We combine the proposed hybrid system with confidence based metrics to improve OOV detection performance. Previous work address OOV detection as a binary classification task, where each region is independently classified using local information.

We propose to treat OOV detection as a sequence labeling problem, and we show that 1 jointly predicting out-of-vocabulary regions, 2 including contextual information from each region, and 3 learning sub-lexical units optimized for this task, leads to substantial improvements with respect to state-of-the-art on an English Broadcast News and MIT Lectures task.

Large vocabulary speech recognition systems fail to recognize words beyond their vocabulary, many of which are information rich terms, like named entities or foreign words. However, the standard information provided by social media APIs, such as Twitter, cover a limited number of messages. This paper presents Carmen, a geolocation system that can determine structured location information for messages provided by the Twitter API. Our system utilizes geocoding tools and a combination of automatic and manual alias resolution methods to infer location structures from GPS positions and user-provided profile data.

We show that our system is accurate and covers many locations, and we demonstrate its utility for improving influenza surveillance. Public health applications using social media often require accurate, broad-coverage location information. We leverage this model to exploit a small set of previously annotated reviews to automatically analyze the topics and sentiment latent in over 50, online reviews of physicians and we make this dataset publicly available.

The proposed model outperforms baseline models for this task with respect to model perplexity and sentiment classification. We report the most representative words with respect to positive and negative sentiment along three clinical aspects, thus complementing existing qualitative work exploring patient reviews of physicians. We analyze patient reviews of doctors using a novel probabilistic joint model of aspect and sentiment based on factorial LDA.

Past work has relied on faceted browsing of document metadata or on natural language processing of document text. In this paper, we present a new web-based tool that integrates topics learned from an unsupervised topic model in a faceted browsing experience. The user can manage topics, filter documents by topic and summarize views with metadata and topic graphs.

We report a user study of the usefulness of topics in our tool. Effectively exploring and analyzing large text corpora requires visualizations that provide a high level summary. Language models, which play a crucial role in speech recognizers and machine translation systems, are particularly sensitive to such changes, unless some form of adaptation takes place.

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One approach to speech language model adaptation is self-training, in which a language model's parameters are tuned based on automatically transcribed audio. However, transcription errors can misguide self-training, particularly in challenging settings such as conversational speech.

In this work, we propose a model that considers the confusions errors of the ASR channel. By modeling the likely confusions in the ASR output instead of using just the 1-best, we improve self-training efficacy by obtaining a more reliable reference transcription estimate.

We demonstrate improved topic-based language modeling adaptation results over both 1-best and lattice self-training using our ASR channel confusion estimates on telephone conversations. Furthermore, users who communicate with each other often have similar hidden properties. We propose an algorithm that exploits these insights to cluster the observed attributes of hundreds of millions of Twitter users.

Attributes such as user names are grouped together if users with those names communicate with other similar users. We separately cluster millions of unique first names, last names, and userprovided locations. The efficacy of these clusters is then evaluated on a diverse set of classification tasks that predict hidden users properties such as ethnicity, geographic location, gender, language, and race, using only profile names and locations when appropriate. Our readily-replicable approach and publicly released clusters are shown to be remarkably effective and versatile, substantially outperforming state-of-the-art approaches and human accuracy on each of the tasks studied.

Hidden properties of social media users, such as their ethnicity, gender, and location, are often reflected in their observed attributes, such as their first and last names. However, real-world datasets often have multiple metadata attributes that can divide the data into domains. It is not always apparent which single attribute will lead to the best domains, and more than one attribute might impact classification. We propose extensions to two multi-domain learning techniques for our multi-attribute setting, enabling them to simultaneously learn from several metadata attributes.

Experimentally, they outperform the multi-domain learning baseline, even when it selects the single "best" attribute. Cohen and Carolyn P. Multi-Domain learning assumes that a single metadata attribute is used in order to divide the data into so-called domains. However, previous work has relied on simple content analysis, which conflates flu tweets that report infection with those that express concerned awareness of the flu. By discriminating these categories, as well as tweets about the authors versus about others, we demonstrate significant improvements on influenza surveillance using Twitter.

Twitter has been shown to be a fast and reliable method for disease surveillance of common illnesses like influenza. We consider such models for clinical research of new recreational drugs and trends, an important application for mining current information for healthcare workers. We use a "three-dimensional" f-LDA variant to jointly model combinations of drug marijuana, salvia, etc.

Since a purely unsupervised topic model is unlikely to discover these specific factors of interest, we develop a novel method of incorporating prior knowledge by leveraging user generated tags as priors in our model. We demonstrate that this model can be used as an exploratory tool for learning about these drugs from the Web by applying it to the task of extractive summarization. Multi-dimensional latent text models, such as factorial LDA f-LDA , capture multiple factors of corpora, creating structured output for researchers to better understand the contents of a corpus.

In addition to providing useful output for this important public health task, our prior-enriched model provides a framework for the application of f-LDA to other tasks. AROW performs adaptive regularization of the prediction function upon seeing each new instance, allowing it to perform especially well in the presence of label noise. We derive mistake bounds for the binary and multiclass settings that are similar in form to the second order perceptron bound. Our bounds do not assume separability. We also relate our algorithm to recent confidence-weighted online learning techniques.

Empirical evaluations show that AROW achieves state-of-the-art performance on a wide range of binary and multiclass tasks, as well as robustness in the face of non-separable data. We present AROW, an online learning algorithm for binary and multiclass problems that combines large margin training, confidence weighting, and the capacity to handle non-separable data.

Entity Linking, also referred to as record linkage or entity resolution, involves aligning a textual mention of a named-entity to an appropriate entry in a knowledge base, which may or may not contain the entity. This has manifold applications ranging from linking patient health records to maintaining personal credit files, prevention of identity crimes, and supporting law enforcement. We discuss the key challenges present in this task and we present a high-performing system that links entities using max-margin ranking.

We also summarize recent work in this area and describe several open research problems. In the menagerie of tasks for information extraction, entity linking is a new beast that has drawn a lot of attention from NLP practitioners and researchers recently. This article examines the types of health topics discussed on Twitter, and how tweets can both augment existing public health capabilities and enable new ones.

The author also discusses key challenges that researchers must address to deliver high-quality tools to the public health community. Recent work in machine learning and natural language processing has studied the health content of tweets and demonstrated the potential for extracting useful public health information from their aggregation. We introduce factorial LDA, a multi-dimensional model in which a document is influenced by K different factors, and each word token depends on a K-dimensional vector of latent variables. Our model incorporates structured word priors and learns a sparse product of factors.

Experiments on research abstracts show that our model can learn latent factors such as research topic, scientific discipline, and focus methods vs. Our modeling improvements reduce test perplexity and improve human interpretability of the discovered factors. Latent variable models can be enriched with a multi-dimensional structure to consider the many latent factors in a text corpus, such as topic, author perspective and sentiment.

We describe our data set construction and experiments with binary classification of data into influenza versus general messages and classification into concerned awareness and existing infection. We present preliminary results for mining concerned awareness of influenza tweets.

We identify patient safety related tweets and characterize them by which medical populations caused errors, who reported these er- rors, what types of errors occurred, and what emotional states were expressed in response. Our long term goal is to improve the handling and reduction of errors by incorpo- rating this patient input into the patient safety process. In this paper we report preliminary results from a study of Twitter to identify patient safety reports, which offer an immediate, untainted, and expansive patient perspective un- like any other mechanism to date for this topic.

In this work we support such research through the use of a multi-dimensional latent text model -- factorial LDA -- that captures orthogonal factors of corpora, creating structured output for researchers to better understand the contents of a corpus. Since a purely unsupervised model is unlikely to discover specific factors of interest to clinical researchers, we modify the structure of factorial LDA to incorporate prior knowledge, including the use of of observed variables, informative priors and background components.

The resulting model learns factors that correspond to drug type, delivery method smoking, injection, etc. We demonstrate that the improved model yields better quantitative and more interpretable results. Clinical research of new recreational drugs and trends requires mining current information from non-traditional text sources.

In its simplest version, the algorithm simply increases the weight of n-gram features which appear in the correct oracle hypothesis and decreases the weight of n-gram features which appear in the 1-best hypothesis. In this paper, we show that the perceptron algorithm can be successfully used in a semi-supervised learning SSL framework, where limited amounts of labeled data are available. Our framework has some similarities to graph-based label propagation [2] in the sense that a graph is built based on proximity of unlabeled conversations, and then it is used to propagate confidences in the form of features to the labeled data, based on which perceptron trains a discriminative model.

The novelty of our approach lies in the fact that the confidence "flows" from the unlabeled data to the labeled data, and not vice-versa, as is done traditionally in SSL. Experiments conducted at the CLSP Summer Workshop on the conversational telephone speech corpora Dev04f and Eval04f demonstrate the effectiveness of the proposed approach. The perceptron algorithm was used in [1] to estimate discriminative language models which correct errors in the output of ASR systems. We extend the SLM framework in two new directions.

First, we propose a new syntactic hierarchical interpolation that improves over previous approaches. Second, we develop a general information-theoretic algorithm for pruning the underlying Jelinek-Mercer interpolated LM used in [1], which substantially reduces the size of the LM, enabling us to train on large data. When combined with hill-climbing [2] the SLM is an accurate model, space-efficient and fast for rescoring large speech lattices.

The structured language model SLM of [1] was one of the first to successfully integrate syntactic structure into language models. We show how to learn a stochastic transducer from an unorganized collection of strings rather than string pairs. The role of the transducer is to organize the collection. Our generative model explains similarities among the strings by supposing that some strings in the collection were not generated ab initio, but were instead derived by transduction from other, "similar" strings in the collection.

Our variational EM learning algorithm alternately reestimates this phylogeny and the transducer parameters. The final learned transducer can quickly link any test name into the final phylogeny, thereby locating variants of the test name. We find that our method can effectively find name variants in a corpus of web strings used to refer to persons in Wikipedia, improving over standard untrained distances such as Jaro-Winkler and Levenshtein distance.

Many linguistic and textual processes involve transduction of strings. First, many multi-domain learning algorithms resemble ensemble learning algorithms.


Second, these algorithms are traditionally evaluated in a balanced label setting, although in practice many multi-domain settings have domain-specific label biases. When multi-domain learning is applied to these settings, 2 are multi-domain methods improving because they capture domain-specific class biases? An understanding of these two issues presents a clearer idea about where the field has had success in multi-domain learning, and it suggests some important open questions for improving beyond the current state of the art.

We present a systematic analysis of existing multi-domain learning approaches with respect to two questions. They are regarded as linguistically naive, but estimating them from any amount of text, large or small, is straightforward. Furthermore, they have doggedly matched or outperformed numerous competing proposals for syntactically well-motivated models.

This unusual resilience of n-grams, as well as their weaknesses, are examined here. It is demonstrated that n-grams are good word-predictors, even linguistically speaking, in a large majority of word-positions, and it is suggested that to improve over n-grams, one must explore syntax-aware or other language models that focus on positions where n-grams are weak.

Statistical language models used in deployed systems for speech recognition, machine translation and other human language technologies are almost exclusively n-gram models. When co-referent text mentions appear in different languages, these techniques cannot be easily applied. Consequently, we develop new methods for clustering text mentions across documents and languages simultaneously, producing cross-lingual entity clusters. Our approach extends standard clustering algorithms with cross-lingual mention and context similarity measures.

Crucially, we do not assume a pre-existing entity list knowledge base , so entity characteristics are unknown. On an Arabic-English corpus that contains seven different text genres, our best model yields a Standard entity clustering systems commonly rely on mention string matching, syntactic features, and linguistic resources like English WordNet. Much less attention has been given to the underlying structure of the topics themselves.

As a result, most topic models generate topics independently from a single underlying distribution and require millions of parameters, in the form of multinomial distributions over the vocabulary. In this paper, we introduce the Shared Components Topic Model SCTM , in which each topic is a normalized product of a smaller number of underlying component distributions. Our model learns these component distributions and the structure of how to combine subsets of them into topics.

With a few exceptions, extensions to latent Dirichlet allocation LDA have focused on the distribution over topics for each document. However, these language models can be difficult to use in practice because of the time required to generate features for rescoring a large hypothesis set. In this work, we propose substructure sharing, which saves duplicate work in processing hypothesis sets with redundant hypothesis structures.