We propose a methodology to construct a term dictionary for text analytics through an interactive process between a human and a machine.The interactive approach helps the creation of flexible dictionaries with precise granularity required in text analysis.This paper introduces the first formulation of interactive dictionary construction to address this issue.To optimize the interaction, we propose a new algorithm that effectively captures an analyst's intention starting from only a small number of sample terms.Along with the algorithm, we also design an automatic evaluation framework that provides a systematic assessment of any interactive method for the dictionary creation task.Experiments using real scenario based corpora and dictionaries show that our algorithm outperforms baseline methods, and works even with a small number of interactions.Also, we provide our dataset for future studies 1 .1 https://github.com/kohilin/ IDC-evalset.git

We propose a task called Visual Concept Naming to associate visual concepts with the corresponding textual expressions, i.e., names of visual concepts found in real-world multimodal data. To tackle the task, we create a dataset consisting of 3.4 million tweets in total in three languages. We also propose a method for extracting candidate names of visual concepts and validating them by exploiting Web-based knowledge obtained through image search. To demonstrate the capability of our method, we conduct an experiment with the dataset we create and evaluate names obtained by our method through crowdsourcing, where we establish an evaluation method to verify the names. The experimental results indicate that the proposed method can identify a wide variety of names of visual concepts. The names we obtained also show interesting insights regarding languages and countries where the languages are used.1

Topic models have been successfully applied in lexicon extraction. However, most previous methods are limited to document-aligned data. In this paper, we try to address two challenges of applying topic models to lexicon extraction in non-parallel data: 1) hard to model the word relationship and 2) noisy seed dictionary. To solve these two challenges, we propose two new bilingual topic models to better capture the semantic information of each word while discriminating the multiple translations in a noisy seed dictionary. We extend the scope of topic models by inverting the roles of "word" and "document". In addition, to solve the problem of noise in seed dictionary, we incorporate the probability of translation selection in our models. Moreover, we also propose an effective measure to evaluate the similarity of words in different languages and select the optimal translation pairs. Experimental results using real world data demonstrate the utility and efficacy of the proposed models.

Abstract This article proposesclause-level evaluation detection, which is a fine-grained type of opinion mining, and describes an unsupervised lexicon building method for capturing domain-specific knowledge by leveraging the similar polarities of sentiments between adjacent clauses. The lexical entries to be acquired are calledpolar atoms, the minimum human-understandable syntactic structures that specify the polarity of clauses. As a hint to obtain candidate polar atoms, we usecontext coherency, the tendency for the same polarity to appear successively in a context. Using the overall density and precision of coherency in the corpus, the statistical estimation picks up appropriate polar atoms from among the candidates, without any manual tuning of the threshold values. The experimental results show that the precision of polarity assignment with the automatically acquired lexicon was 83 per cent on average, and our method is robust for corpora in diverse domains and for the size of the initial lexicon.

We present sentiment analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document about a subject, SA detects all references to the given subject, and determines sentiment in each of the references using natural language processing (NLP) techniques. Our sentiment analysis consists of 1) a topic specific feature term extraction, 2) sentiment extraction, and 3) (subject, sentiment) association by relationship analysis. SA utilizes two linguistic resources for the analysis: the sentiment lexicon and the sentiment pattern database. The performance of the algorithms was verified on online product review articles ("digital camera" and "music" reviews), and more general documents including general Webpages and news articles.

This paper illustrates a sentiment analysis approach to extract sentiments associated with polarities of positive or negative for specific subjects from a document, instead of classifying the whole document into positive or negative.The essential issues in sentiment analysis are to identify how sentiments are expressed in texts and whether the expressions indicate positive (favorable) or negative (unfavorable) opinions toward the subject. In order to improve the accuracy of the sentiment analysis, it is important to properly identify the semantic relationships between the sentiment expressions and the subject. By applying semantic analysis with a syntactic parser and sentiment lexicon, our prototype system achieved high precision (75-95%, depending on the data) in finding sentiments within Web pages and news articles.

In a consistent text, many words and phrases are repeatedly used in more than one sentence. When an identical phrase (a set of consecutive words) is repeated in different sentences, the constituent words of those sentences tend to be associated in identical modification patterns with identical parts of speech and identical modifiee-modifier relationships. Thus, when a syntactic parser cannot parse a sentence as a unified structure, parts of speech and modifiee-modifier relationships among morphologically identical words in complete parses of other sentences within the same text provide useful information for obtaining partial parses of the sentence.In this paper, we describe a method for completing partial parses by maintaining consistency among morphologically identical words within the same text as regards their part of speech and their modifiee-modifier relationship. The experimental results obtained by using this method with technical documents offer good prospects for improving the accuracy of sentence analysis in a broad-coverage natural language processing system such as a machine translation system.