Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.

/pdf/mining-and-summarizing-customer-reviews-3kfih6aknk.pdf

Mining and summarizing customer reviews

Standard alphabetical procedures for organizing lexical information put together words that are spelled alike and scatter words with similar or related meanings haphazardly through the list. Unfortunately, there is no obvious alternative, no other simple way for lexicographers to keep track of what has been done or for readers to find the word they are looking for. But a frequent objection to this solution is that finding things on an alphabetical list can be tedious and time-consuming. Many people who would like to refer to a dictionary decide not to bother with it because finding the information would interrupt their work and break their train of thought.

/pdf/introduction-to-wordnet-an-on-line-lexical-database-1cshcej27g.pdf

Introduction to WordNet: An On-line Lexical Database

is one of the most recognizablecharacters in 20th century cinema. HAL is an artiﬁcial agent capable of such advancedlanguage behavior as speaking and understanding English, and at a crucial moment inthe plot, even reading lips. It is now clear that HAL’s creator, Arthur C. Clarke, wasa little optimistic in predicting when an artiﬁcial agent such as HAL would be avail-able. But just how far off was he? What would it take to create at least the language-relatedpartsofHAL?WecallprogramslikeHALthatconversewithhumansinnatural

/pdf/speech-and-language-processing-1xjlmt8snu.pdf

Speech and Language Processing

The Web has become an excellent source for gathering consumer opinions. There are now numerous Web sites containing such opinions, e.g., customer reviews of products, forums, discussion groups, and blogs. This paper focuses on online customer reviews of products. It makes two contributions. First, it proposes a novel framework for analyzing and comparing consumer opinions of competing products. A prototype system called Opinion Observer is also implemented. The system is such that with a single glance of its visualization, the user is able to clearly see the strengths and weaknesses of each product in the minds of consumers in terms of various product features. This comparison is useful to both potential customers and product manufacturers. For a potential customer, he/she can see a visual side-by-side and feature-by-feature comparison of consumer opinions on these products, which helps him/her to decide which product to buy. For a product manufacturer, the comparison enables it to easily gather marketing intelligence and product benchmarking information. Second, a new technique based on language pattern mining is proposed to extract product features from Pros and Cons in a particular type of reviews. Such features form the basis for the above comparison. Experimental results show that the technique is highly effective and outperform existing methods significantly.

/pdf/opinion-observer-analyzing-and-comparing-opinions-on-the-web-2aduawrwgh.pdf

Opinion observer: analyzing and comparing opinions on the Web

The relationship between semantic and contextual similarity is investigated for pairs of nouns that vary from high to low semantic similarity. Semantic similarity is estimated by subjective ratings; contextual similarity is estimated by the method of sorting sentential contexts. The results show an inverse linear relationship between similarity of meaning and the discriminability of contexts. This relation, is obtained for two separate corpora of sentence contexts. It is concluded that, on average, for words in the same language drawn from the same syntactic and semantic categories, the more often two words can be substituted into the same contexts the more similar in meaning they are judged to be.

Contextual correlates of semantic similarity

This paper identifies some linguistic properties of technical terminology, and uses them to formulate an algorithm for identifying technical terms in running text. The grammatical properties discussed are preferred phrase structures: technical terms consist mostly of noun phrases containing adjectives, nouns, and occasionally prepositions; rerely do terms contain verbs, adverbs, or conjunctions. The discourse properties are patterns of repetition that distinguish noun phrases that are technical terms, especially those multi-word phrases that constitute a substantial majority of all technical vocabulary, from other types of noun phrase.The paper presents a terminology indentification algorithm that is motivated by these linguistic properties. An implementation of the algorithm is described; it recovers a high proportion of the technical terms in a text, and a high proportaion of the recovered strings are vaild technical terms. The algorithm proves to be effective regardless of the domain of the text to which it is applied.

https://brenocon.com/JustesonKatz1995.pdf

Technical terminology: some linguistic properties and an algorithm for identification in text

Charles and Miller propose that lexical associations between antonymous adjectives are formed via their co-occurences within the same sentence (the co-occurrence hypothesis), rather than via their syntatic substitutability (the substitutability hypothesis), and that such co-occurrences must take place more often than expected by chance. This paper provides empirical support for the co-occurrence hypothesis in a corpus analysis of all high-frequency adjectives and their antonyms and of a major group of morphologically derived antonyms (e.g., impossible, un-happy). We show that very high co-occurrence rates do appear to characterize all antonymous adjective pairs, supporting the precondition for the formation of the association; and we find that the syntactic contexts of these co-occurrences raise the intrinsic associability of antonyms when they do co-occur. We show that via one of these patterns, mutual substitution within otherwise repeated phrases in a sentence, the co-occurrences hypothesis captures the generalizations that were the basis for the substitutability hypothesis for the formation of antonymic associations.

Co-occurrences of antonymous adjectives and their contexts

The decipherment of part of the epi-Olmec script of ancient Mexico, which yields the earliest currently readable texts in Mesoamerica, has been achieved over the last 2 years. This was made possible by the discovery of a stela with a long inscription at La Mojarra, Veracruz, Mexico, in 1986. This decipherment is based on both a reconstruction of the early stages of languages spoken in the region and semantic clues provided by comparison with cultural practices and other script traditions of early southern Mesoamerica. Summarized here is the current state of the phonetic decipherment, the methods used for the decipherment, and results concerning the epi-Olmec language and script. The language identified in the inscriptions is pre-proto-Zoquean, the ancestor of four languages now spoken in the states of Veracruz, Tabasco, Chiapas, and Oaxaca. The decipherment contributes to knowledge of early Mixe-Zoquean language history. The script is more closely related to Mayan hieroglyphic writing than to other early Mesoamerican scripts, and this relation is closer than previously recognized.

http://science.sciencemag.org/content/sci/259/5102/1703.full.pdf

A Decipherment of Epi-Olmec Hieroglyphic Writing

A method and apparatus for extracting multi-word technical terms from a text file in a computer system. Word strings are selected from the text that have at least two words, that have at most a specified maximum number of words, that include none of a special set of selected tokens, and that only include selected characters. Word string which occur less than a specified minimum number of times in the text file are deleted. The remaining strings form a set of word strings very likely to be multi-word technical terms. Improvements on the quality of the set of word strings can be accomplished by deleting word strings which do not satisfy certain grammatical constraints.

Method for extracting multi-word technical terms from text

Recent corpus-based work on word sense disambiguation explores the application of statistical pattern recognition procedures to lexical co-occurrence data from very large text databases. In this paper we argue for a linguistically principled approach to disambiguation, in which relevant contextual clues are narrowly defined, in syntactic and semantic terms, and in which only highly reliable clues are exploited. Statistical methods play a definite role in this work, helping to organize and analyze data, but the disambiguation method itself does not employ statistical data or decision criteria. This approach results in improved understanding of the disambiguation problem both in general and on a word-specific basis and leads to broadly applicable and nearly errorless clues to word sense. The approach is illustrated by an experiment discriminating among the senses of adjectives, which have been relatively neglected in work on sense disambiguation. In particular, the paper assesses the potential of nouns for discriminating among the senses of adjectives that modify them. This assessment is based on an empirical study of five of the most frequent ambiguous adjectives in English: hard, light, old, right, and short. About three-quarters of all instances of these adjectives can be disambiguated almost errorlessly by the nouns they modify or by the syntactic constructions in which they occur. Such disambiguation requires only simple rules, which can be automated easily. Furthermore, a small number of semantic attributes supply a compact means of representing the noun clues in a very few rules. Clues other than nouns are required when modified nouns are not useable. The sense of an ambiguous modified noun may be needed to determine the relevant semantic attribute for disambiguation of a target adjective; and other adjectives, verbs, and grammatical constructions all show evidence of high reliability, and sometimes of high applicability, when they stand in specific, well-defined syntactic relations to the ambiguous adjective. Some of these clues, however, may be hard to automate.

John S. Justeson

Papers

Technical terminology: some linguistic properties and an algorithm for identification in text

Co-occurrences of antonymous adjectives and their contexts

A Decipherment of Epi-Olmec Hieroglyphic Writing

Method for extracting multi-word technical terms from text

Principled disambiguation: discriminating adjective senses with modified nouns