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Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

How to Do Things With Words

Recently there has been a lot of interest in Cross Language Sentiment Analysis (CLSA) using Machine Translation (MT) to facilitate Sentiment Analysis in resource deprived languages. The idea is to use the annotated resources of one language (say, L1) for performing Sentiment Analysis in another language (say, L2) which does not have annotated resources. The success of such a scheme crucially depends on the availability of a MT system between L1 and L2. We argue that such a strategy ignores the fact that a Machine Translation system is much more demanding in terms of resources than a Sentiment Analysis engine. Moreover, these approaches fail to take into account the divergence in the expression of sentiments across languages. We provide strong experimental evidence to prove that even the best of such systems do not outperform a system trained using only a few polarity annotated documents in the target language. Having a very large number of documents in L1 also does not help because most Machine Learning approaches converge (or reach a plateau) after a certain training size (as demonstrated by our results). Based on our study, we take the stand that languages which have a genuine need for a Sentiment Analysis engine should focus on collecting a few polarity annotated documents in their language instead of relying on CLSA.

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Lost in translation : viability of machine translation for cross language sentiment analysis

Sense marked corpora is essential for supervised word sense disambiguation (WSD). The marked sense ids come from wordnets. However, words in corpora appear in morphed forms, while wordnets store lemma. This situation calls for accurate lemmatizers. The lemma is the gateway to the wordnet. However, the problem is that for many languages, lemmatizers do not exist, and this problem is not easy to solve, since rule based lemmatizers take time and require highly skilled linguists.Satistical stemmers on the other hand do not return legitimate lemma. We present here a novel scheme for creating accurate lemmatizers quickly. These lemmatizers are human mediated. The key idea is that a trie is created out of the vocabulary of the language. The lemmatizing process consists in navigating the trie, trying to find a match between the input word and an entry in the trie. At the point of first mismatch, the yield of the subtree rooted at the partially matched node is output as the list of possible lemma. If the correct lemma does not appear in the listas noted by a human lexicographerbacktracking is initiated. This can output more possibilities. A ranking function filters and orders the output list of lemma. We have evaluated the performance of this human mediated lemmatizer for eighteen Indian Languages and five European languages. We have compared accuracy figures against well known lemmatizers/stemmers like Morpha, Morfessor and Snowball stemmers, and observed superior performance in all cases. Our work shows a way of speedily creating human assisted accurate lemmatizers, thereby removing a difficult roadblock in many NLP tasks, e.g., sense annotation.

Facilitating Multi-Lingual Sense Annotation: Human Mediated Lemmatizer

Multilingual learning for Neural Named Entity Recognition (NNER) involves jointly training a neural network for multiple languages. Typically, the goal is improving the NER performance of one of the languages (the primary language) using the other assisting languages. We show that the divergence in the tag distributions of the common named entities between the primary and assisting languages can reduce the effectiveness of multilingual learning. To alleviate this problem, we propose a metric based on symmetric KL divergence to filter out the highly divergent training instances in the assisting language. We empirically show that our data selection strategy improves NER performance in many languages, including those with very limited training data.

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Judicious Selection of Training Data in Assisting Language for Multilingual Neural NER.

In this paper, we propose a hybrid technique for semantic question matching. It uses a proposed two-layered taxonomy for English questions by augmenting state-of-the-art deep learning models with question classes obtained from a deep learning based question classifier. Experiments performed on three open-domain datasets demonstrate the effectiveness of our proposed approach. We achieve state-of-the-art results on partial ordering question ranking (POQR) benchmark dataset. Our empirical analysis shows that coupling standard distributional features (provided by the question encoder) with knowledge from taxonomy is more effective than either deep learning or taxonomy-based knowledge alone.

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Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy

Case structure analysis forms the foundation for any natural language processing task. In this paper we present the computational analysis of the complex case structure of Bengali- a member of the Indo Aryan family of languages- with a view toward interlingua based MT. Bengali is ranked 4 th in the list of languages ordered according to the size of the population that speaks the language. Extremely interesting language phenomena involving morphology, case structure, word order and word senses makes the processing of Bengali a worthwhile and challenging proposition. A recently proposed scheme called the Universal Networking Language has been used as the interlingua. The approach is adaptable to other members of the vast Indo Aryan language family. The parallel development of both the analyzer and the generator system leads to an insightful intra-system verification process in place. Our approach is rule based and makes use of authoritative treatises on Bengali grammar.

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Pushpak Bhattacharyya

Papers

Lost in translation : viability of machine translation for cross language sentiment analysis

Facilitating Multi-Lingual Sense Annotation: Human Mediated Lemmatizer

Judicious Selection of Training Data in Assisting Language for Multilingual Neural NER.

Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy

Universal Networking Language Based Analysis and Generation for Bengali Case Structure Constructs