Answering natural language questions against structured knowledge bases (KB) has been attracting increasing attention in both IR and NLP communities. The task involves two main challenges: recognizing the questions' meanings, which are then grounded to a given KB. Targeting simple factoid questions, many existing open domain semantic parsers jointly solve these two subtasks, but are usually expensive in complexity and resources.In this paper, we propose a simple pipeline framework to efficiently answer more complicated questions, especially those implying aggregation operations, e.g., argmax, argmin.We first develop a transition-based parsing model to recognize the KB-independent meaning representation of the user's intention inherent in the question. Secondly, we apply a probabilistic model to map the meaning representation, including those aggregation functions, to a structured query.The experimental results showed that our method can better understand aggregation questions, outperforming the state-of-the-art methods on the Free917 dataset while still maintaining promising performance on a more challenging dataset, WebQuestions, without extra training.

Knowledge bases have the potential to advance artificial intelligence, but often suffer from recall problems, i.e., lack of knowledge of new entities and relations. On the contrary, social media such as Twitter provide abundance of data, in a timely manner: information spreads at an incredible pace and is posted long before it makes it into more commonly used resources for knowledge extraction. In this paper we address the question whether we can exploit social media to extract new facts, which may at first seem like finding needles in haystacks. We collect tweets about 60 entities in Freebase and compare four methods to extract binary relation candidates, based on syntactic and semantic parsing and simple mechanism for factuality scoring. The extracted facts are manually evaluated in terms of their correctness and relevance for search. We show that moving from bottom-up syntactic or semantic dependency parsing formalisms to top-down frame-semantic processing improves the robustness of knowledge extraction, producing more intelligible fact candidates of better quality. In order to evaluate the quality of frame semantic parsing on Twitter intrinsically, we make a multiply frame-annotated dataset of tweets publicly available.

Compositional semantic aims at constructing the meaning of phrases or sentences according to the compositionality of word meanings. In this paper, we propose to synchronously learn the representations of individual words and extracted high-frequency phrases. Representations of extracted phrases are considered as gold standard for constructing more general operations to compose the representation of unseen phrases. We propose a grammatical type specific model that improves the composition flexibility by adopting vector-tensor-vector operations. Our model embodies the compositional characteristics of traditional additive and multiplicative model. Empirical result shows that our model outperforms state-of-the-art composition methods in the task of computing phrase similarities.

In order to advance action generation and creation in robots beyond simple learned schemas we need computational tools that allow us to automatically interpret and represent human actions. This paper presents a system that learns manipulation action plans by processing unconstrained videos from the World Wide Web. Its goal is to robustly generate the sequence of atomic actions of seen longer actions in video in order to acquire knowledge for robots. The lower level of the system consists of two convolutional neural network (CNN) based recognition modules, one for classifying the hand grasp type and the other for object recognition. The higher level is a probabilistic manipulation action grammar based parsing module that aims at generating visual sentences for robot manipulation. Experiments conducted on a publicly available unconstrained video dataset show that the system is able to learn manipulation actions by ``watching'' unconstrained videos with high accuracy.

Word segmentation is a necessary first step for automatic syntactic analysis of Chinese text. Chinese segmentation is highly accurate on news data, but the accuracies drop significantly on other domains, such as science and literature. For scientific domains, a significant portion of out-of-vocabulary words are domain-specific terms, and therefore lexicons can be used to improve segmentation significantly. For the literature domain, however, there is not a fixed set of domain terms. For example, each novel can contain a specific set of person, organization and location names. We investigate a method for automatically mining common noun entities for each novel using information extraction techniques, and use the resulting entities to improve a state-of-the-art segmentation model for the novel. In particular, we design a novel double-propagation algorithm that mines noun entities together with common contextual patterns, and use them as plugin features to a model trained on the source domain. An advantage of our method is that no retraining for the segmentation model is needed for each novel, and hence it can be applied efficiently given the huge number of novels on the web.

In particular, we investigate demographic business. Banks, telecommunication companies, airlines, Internet churn indicators (obtained from users of microposts), service providers, pay TV companies, and insurance content churn indicators (obtained from the textual firms etc., utilize customer churn or attrition rates as one of content of micro-posts), and context churn indicators (obtained their key business metrics. This metric is important as the from threads containing the micro-posts). We examine churn rate of a business is a good indicator of customer response factors that make this problem more challenging and investigate to services, pricing, and competitions. The ability to the performance of several state-of-the-art machine identify churny contents / behaviors can enable early intervention learning techniques on this problem. A challenging aspect processes (as part of retention campaigns) and ultimately of such classification task is that churny contents can be expressed a reduction in customer churn.

Word alignment is an important natural language processing task that indicates the correspondence between natural languages. Recently, unsupervised learning of log-linear models for word alignment has received considerable attention as it combines the merits of generative and discriminative approaches. However, a major challenge still remains: it is intractable to calculate the expectations of non-local features that are critical for capturing the divergence between natural languages. We propose a contrastive approach that aims to differentiate observed training examples from noises. It not only introduces prior knowledge to guide unsupervised learning but also cancels out partition functions. Based on the observation that the probability mass of log-linear models for word alignment is usually highly concentrated, we propose to use top-$n$ alignments to approximate the expectations with respect to posterior distributions. This allows for efficient and accurate calculation of expectations of non-local features. Experiments show that our approach achieves significant improvements over state-of-the-art unsupervised word alignment methods.

Combinatory Categorial Grammar (CCG) is a lexicalized grammar formalism in which words are associated with categories that, in combination with a small universal set of rules, specify the syntactic configurations in which they may occur. Categories are selected from a large, recursively-defined set; this leads to high word-to-category ambiguity, which is one of the primary factors that make learning CCG parsers difficult, especially in the face of little data. Previous work has shown that learning sequence models for CCG tagging can be improved by using linguistically-motivated prior probability distributions over potential categories. We extend this approach to the task of learning a CCG parser from weak supervision. We present a Bayesian formulation for CCG parser induction that assumes only supervision in the form of an incomplete tag dictionary mapping some word types to sets of potential categories. Our approach outperforms a baseline model trained with uniform priors by exploiting universal, intrinsic properties of the CCG formalism to bias the model toward simpler, more cross-linguistically common categories.

Word Sense Disambiguation is a difficult problem to solve in the unsupervised setting. This is because in this setting inference becomes more dependent on the interplay between different senses in the context due to unavailability of learning resources. Using two basic ideas, sense dependency and selective dependency, we model the WSD problem as a Maximum A Posteriori (MAP) Inference Query on a Markov Random Field (MRF) built using WordNet and Link Parser or Stanford Parser. To the best of our knowledge this combination of dependency and MRF is novel, and our graph-based unsupervised WSD system beats state-of-the-art system on SensEval-2, SensEval-3 and SemEval-2007 English all-words datasets while being over 35 times faster.

While most work on trajectory mining is applied to pre- dict movements of mobile users, in this paper we consider a more general problem of building behavior models of users from their state-action trajectories. We assume that the user behavior can be compactly modeled as a Probabilistic State-Dependent Grammar (PSDG) which represents a hierarchical policy. The key problem is that while the states and actions of the user are directly observed, his intentional structure is not. We propose to learn the user’s policy from a set of selected trajectories and intention queries at selected states in the trajectory. Our main contributions are an algorithm for learning hierarchical policies from state-action trajectories, and principled heuristics for selecting suitable trajectories and intention queries. Experiments in multiple domains show that our approach is effective and more sample-efficient than learning non-hierarchical policies.