Articles

We present a method that learns bilexical operators over distributional representations of words and leverages supervised data for a linguistic relation. The learning algorithm exploits low- rank bilinear forms and induces low-dimensional embeddings of the lexical space tailored for the target linguistic relation. An advantage of imposing low-rank constraints is …...

Distributed word representations have been widely used and proven to be useful in quite a few natural language processing and text mining tasks. Most of existing word embedding models aim at generating only one embedding vector for each individual word, which, however, limits their effectiveness because huge amounts of words …...

The techniques of using neural networks to learn distributed word representations (i.e., word embeddings) have been used to solve a variety of natural language processing tasks. The re- cently proposed methods, such as CBOW and Skip-gram, have demonstrated their effectiveness in learning word embeddings based on context information such that …...

Despite the overwhelming use of statistical language models in speech recognition, machine translation, and several other domains, few high probability guarantees exist on their generaliza- tion error. In this paper, we bound the test set perplexity of two popular language models – the n-gram model and class-based n-grams – using …...

A significant portion of search engine queries mention business entities such as restaurants, cin- emas, banks, and other places of interest. These queries are commonly known as “local search” queries, because they represent an information need about a place, often a place local to the user. A portion of these …...

Most web search results clustering (SRC) strategies have predominantly studied the definition of adapted representation spaces to the detriment of new clustering techniques to improve perfor- mance. In this paper, we define SRC as a multi-objective optimization (MOO) problem to take advantage of most recent works in clustering. In particular, …...

Community question answering (CQA) has become an important service due to the popularity of CQA archives on the web. A distinctive feature is that CQA services usually organize questions into a hierarchy of natural categories. In this paper, we focus on the problem of question re- trieval and propose a …...

This paper addresses implicit opinions expressed via inference over explicit sentiments and events that positively/negatively affect entities (goodFor/badFor, gfbf events). We incorporate the inferences developed by implicature rules into an optimization framework, to jointly improve sentiment detection toward entities and disambiguate components of gfbf events. The framework simultaneously beats the …...

Sentiment analysis of short texts such as single sentences and Twitter messages is challenging because of the limited contextual information that they normally contain. Effectively solving this task requires strategies that combine the small text content with prior knowledge and use more than just bag-of-words. In this work we propose …...

This paper describes an approach for political tendency identification of Twitter users. We define some metrics that take into account the polarity of the political entities in the tweets of each user. To obtain this polarities we present the sentiment analysis system developed. The evaluation was performed on the general …...

The intersection of psychology and computational linguistics is capable of providing novel au- tomated insight into the language of everyday cognition through analysis of micro-blogs. While Twitter is often seen as banal or focused only on the who, what, when or where tweets can ac- tually serve as a source …...

Image retrieval models typically represent images as bags-of-terms, a representation that is well- suited to matching images based on the presence or absence of terms. For some information needs, such as searching for images of people performing actions, it may be useful to retain data about how parts of an …...

We present a hierarchical topical segmenter for free text. Hierarchical Affinity Propagation for Segmentation (HAPS) is derived from a clustering algorithm Affinity Propagation. Given a doc- ument, HAPS builds a topical tree. The nodes at the top level correspond to the most prominent shifts of topic in the document. Nodes …...

This work is, to our knowledge, a first attempt at a machine learning approach to cross-lingual coreference resolution, i.e. coreference resolution (CR) performed on a bitext. Focusing on CR of English pronouns, we leverage language differences and enrich the feature set of a standard monolingual CR system for English with …...

In this paper we investigate whether unsupervised models can be used to induce conventional aspects of rhetorical language in scientific writing. We rely on the intuition that the rhetorical language used in a document is general in nature and independent of the document’s topic. We describe a Bayesian latent-variable model …...

This study aims at retrieving tweets with an implicit topic, which cannot be identified by the current query-matching system employed by Twitter. Such tweets are relevant to a given query but do not explicitly contain the term. When these tweets are combined with a relevant tweet containing the overt keyword, …...

This papers presents a context-aware NLP approach to automatically detect noteworthy infor- mation in spontaneous mobile phone conversations. The proposed method uses a supervised modeling strategy which considers both features from the content of the conversation as well as contextual information from the call. We empirically analyze the predictive performance …...

Most of the recent literature on Sentiment Analysis over Twitter is tied to the idea that the senti- ment is a function of an incoming tweet. However, tweets are filtered through streams of posts, so that a wider context, e.g. a topic, is always available. In this work, the contribution …...

The state-of-the-art methods used for relation classification are primarily based on statistical ma- chine learning, and their performance strongly depends on the quality of the extracted features. The extracted features are often derived from the output of pre-existing natural language process- ing (NLP) systems, which leads to the propagation of …...

We develop an instance (token) based extension of the state of the art word (type) based part-of- speech induction system introduced in (Yatbaz et al., 2012). Each word instance is represented by a feature vector that combines information from the target word and probable substitutes sampled from an n-gram model …...