New avenues in knowledge bases for natural language processing
Between the birth of the Internet and 2003, year of birth of so1 cial networks such as MySpace, Delicious, LinkedIn, and Facebook, 2 there were just a few dozen exabytes of information on the Web. 3 Today, that same amount of information is created weekly. The ad4 vent of the Social Web has provided people with new content5 sharing services that allow them to create and share their own 6 contents, ideas, and opinions, in a timeand cost efficient way, 7 with virtually millions of other people connected to the World 8 Wide Web. This huge amount of information, however, is mainly 9 unstructured (because it is specifically produced for human con10 sumption) and hence not directly machine processable. The auto11 matic analysis of text involves a deep understanding of natural lan12 guage by machines, a reality from which we are still very far off. 13 Hitherto, online information retrieval, aggregation, and process14 ing have mainly been based on algorithms relying on the textual 15 representation of webpages. Such algorithms are very good at re16 trieving texts, splitting them into parts, checking the spelling and 17 counting the number of words. When it comes to interpreting sen18 tences and extracting meaningful information, however, their capa19 bilities are known to be very limited, as most of the existing ap20 proaches are still based on the syntactic representation of text, a 21 method that relies mainly on word co occurrence frequencies. Such 22 algorithms are limited by the fact that they can process only the 23 information that they can 'see' As human text processors, we do 24 not have such limitations as every word we see activates a cas25 cade of semantically related concepts, relevant episodes, and sen26 sory experiences, all of which enable the completion of complex 27 natural language processing (NLP) tasks such as word sense dis28 ambiguation, textual entailment, and semantic role labeling in a 29 quick and effortless way. 30 Knowledge based NLP focuses on the intrinsic meaning asso31 ciated with natural language text. Rather than simply processing 32 documents at syntax level, knowledge based approaches rely on 33 implicit denotative features associated with natural language text, 34 hence stepping away from the blind usage of word co occurrence 35 count. Unlike purely syntactical techniques, knowledge based ap36 proaches are also able to detect semantics that are expressed in 37 a subtle manner, e.g. through the analysis of concepts that do 38 not explicitly convey relevant information, but which are implic39 itly linked to other concepts that do so. 40 This special issue aimed at bringing together contributions from 41 both academics and practitioners in the context of knowledge42 based NLP in order to address the wide spectrum of issues related 43 to NLP research and, hence, better grasp the current limitations 44 and opportunities related to this fast evolving branch of artificial 45 intelligence. Out of the 54 submissions received for this special is46 sue, 17 were accepted. Two of the accepted papers underwent four 47 rounds of revisions, five papers underwent three, and the rest were 48 revised twice. 49 The article "Using Neural Word Embeddings to Model User Be50 havior and Detect User Segments" by Ludovico Boratto, Salvatore 51 Carta, Gianni Fenu, and Roberto Saia proposes to model user be52 havior for detecting segments of users to target for advertising. 53 Various sources of data are mined and modeled in order to de54 tect these segments, such as the queries issued by the users. Au55 thors first show the need for a user segmentation system to em56 ploy reliable user preferences, since nearly half of the times users 57 reformulate their queries in order to satisfy their information need. 58 Then, they propose a method that analyzes the description of the 59 items positively evaluated by the users and extracts a vector rep60 resentation of the words in these descriptions (word embeddings) 61 Since it is widely known that users tend to choose items of the 62 same categories, the proposed approach is designed to avoid the 63 so called preference stability, which would associate the users to 64 trivial segments. Authors performed different sets of experiments 65 on a large real world dataset, which validated the proposed ap66 proach and showed its capability to produce effective segments. 67 In "Bilingual Recursive Neural Network Based Data Selection for 68 Statistical Machine Translation" Derek Wong, Yi Lu, and Lidia Chao 69 address the problem of data selection as an effective solution to 70 domain adaptation in statistical machine translation (SMT) The 71 dominant methods are perplexity based ones, which do not con72 sider the mutual translations of sentence pairs and tend to se73 lect short sentences. Authors propose bilingual semi supervised 74 recursive neural network data selection methods to differentiate 75 domain relevant data from out domain data. The proposed meth76 ods are evaluated in the task of building domain adapted SMT sys77 tems. Authors present extensive comparisons and show that the 78 proposed methods outperform the state of the art data selection 79 approaches. 80 Next, the article "Text Normalization and Semantic Indexing 81 to Enhance Instant Messaging and SMS Spam Filtering" by Tiago 82 Almeida, Tiago Silva, Igor Santos, and Jose Gomez Hidalgo proposes 83 and then evaluates a method to normalize and expand online In84 stant Messaging and SMS text in order to acquire better attributes 85 and enhance the classification performance. The proposed text pro86 cessing approach is based on lexicographic and semantic dictio87 naries along with state of the art techniques for semantic analysis 88 and context detection. This technique is used to normalize terms 89 and create new attributes in order to change and expand original 90
Knowl. Based Syst.