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We also show comparable results to several structure-dependent methods. Finally, we analyzed the effect of our alignment mechanis Constantin Orasan. Amir Bakarov. Word embeddings are real-valued word representations able to capture lexical semantics and trained on natural language corpora.
Models proposing these representations have gained popularity in the recent years, but the issue of the most adequate evaluation method still remains open. This paper presents an extensive overview of the field of word embeddings evaluation, highlighting main problems and proposing a typology of approaches to evaluation, summarizing 16 intrinsic methods and 12 extrinsic methods.
I describe both widely-used and experimental methods, systematize information about evaluation datasets and discuss some key challenges. We present symbolic and neural approaches for Arabic paraphrasing that yield high paraphrasing accuracy. This is the first work on sentence level paraphrase generation for Arabic and the first using neural models to generate paraphrased sentences for Arabic. We present and compare several methods for paraphrasing and obtaining monolingual parallel data.
We share a large coverage phrase dictionary for Arabic and contribute a large parallel monolingual corpus that can be used in developing new seq-to-seq models for paraphrasing. This is the first large monolingual corpus of Arabic. We also present first results in Arabic paraphrasing using seq-to-seq neural methods. Additionally, we propose a novel automatic evaluation metric for paraphrasing that correlates highly with human judgement. Dimitri Kartsaklis. This paper addresses the problem of mapping natural language text to knowledge base entities.
The mapping process is approached as a composition of a phrase or a sentence into a point in a multi-dimensional entity space obtained from a knowledge graph.
Further, the knowledge base space is prepared by collecting random walks from a graph enhanced with textual features, which act as a set of semantic bridges between text and knowledge base entities.
The ideas of this work are demonstrated on large-scale text-to-entity mapping and entity classification tasks, with state of the art results.
Dominique Estival. Extending semantic parsing systems to new domains and languages is a highly expensive , time-consuming process, so making effective use of existing resources is critical.
In this paper, we describe a transfer learning method using crosslin-gual word embeddings in a sequence-to-sequence model.
On the NLmaps corpus, our approach achieves state-of-the-art accuracy of Most importantly , we observed a consistent improvement for German compared with several baseline domain adaptation techniques.
As a by-product of this approach, our models that are trained on a combination of English and German utterances perform reasonably well on code-switching utterances which contain a mixture of English and German, even though the training data does not contain any code-switching.
As far as we know, this is the first study of code-switching in semantic parsing. We manually constructed the set of code-switching test utterances for the NLmaps corpus and achieve Yuri Bizzoni. Metaphor is one of the most prominent, and most studied, figures of speech. While it is considered an element of great interest in several branches of linguistics, such as semantics, pragmatics and stylistics, its automatic processing remains an open challenge.
First of all, the semantic complexity of the concept of metaphor itself creates a range of theoretical complications. Secondly, the practical lack of large scale resources forces researchers to work under conditions of data scarcity.
The first task has already been tackled by a number of studies. I approach it as a way to assess the potentialities and limitations of our approach, before dealing with the second task.
Libraries to help with reading and manipulating data import numpy as np import pandas as pd libraries for visualizations import seaborn as sns import matplotlib. In [2]:. You’ll need to install NLTK if you don’t have it already! In [3]:. Let’s use the NLTK library import nltk from nltk. Where are the files that we’re downloading? In [4]:. In [5]:. In [6]:. In [7]:. We can divide apply the string to both files with the objective of converting them into a lists.
In [8]:. In [9]:. Checking tweets in the position 6 from both lists. In [10]:. In [11]:. Since we’ve checked that we have now two lists, we can get the amount of positive and negative tweets that we have available for our analysis. In [12]:. Positive tweets: Negative tweets: In [13]:. In [14]:. We will merge the positive and negative tweets into one dataset to handle the data in a better and simpler way.
We’ll add tags for each kind of tweet. Positive tweets: pos and negative tweets: neg. Steps: Create a new column to identify both, positive and negative tweets. Call this new column sentiment. Do this for both DataFrames. In [15]:. How do the positive tweets look like?
In [16]:. How do the negative tweets look like? In [17]:. Merging the DataFrames to have both, positive and negative tweets in one DataFrame.
In [18]:. In [19]:. Adding the negative tweets to our new DataFrame “tweets”. In [20]:. In [21]:. Let’s visualize and verify that our data is consistent. In [22]:. Engaging in text processing allows us to move onto more difficult tasks which are unique to dealing with text What is text processing? There are a whole host of powerful libraries dedicated to this, including: string and str. For an easier text manipulation we will convert any string to lowercase.
We will remove special characters and any strings that are not going to be needed for further analysis. String module Cleaning the tweets before going though any other text manipulation is helpful.
In [23]:. Before we start, let’s create a copy of our data so we can compare all the changes later. Converting any uppercase string to lowercase. In [24]:. In [25]:. In [26]:. Reviewing the tweets that include URL’s. In [27]:. Looking at the datapoint with index 0 to confirm that it has an URL.
Removing URL’s from tweets. In [28]:. In [29]:. In [30]:. In [32]:. We will use the library emot, which is open source.
In [33]:. In [34]:. In [35]:. Converting emojis into words. In [36]:. Convert emoticons into words. In [37]:. In [38]:. Replacing emojis and emoticons from the tweets.
In [39]:. Removing mentions. For example mariacamila In [40]:. Removing any noise that might be left: Special characters. In [41]:. In [42]:. What are stopwords? What are the languages available? In [43]:. In [44]:. Tokenization consists of dividing a piece of text into smaller pieces. We can divide paragraph into sentences, sentence into words or word into characters.
How do we understand the meaning of a sentence? Why is important? It is important because before doing a text analysis we to identify the words that constitute a string of characters. It’s also important because we can identify the different type of words after obtaining the tokens.
In [45]:. Let’s review an example before applying it to our DataFrames. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. In [46]:. In [47]:. We can check how many sentences we got from the first text we have used for this example: print “The number of sentences is” , len sentences.
The number of sentences is 7. The patterns can be chosen from the regular expressions. Click here to learn more. In [48]:. In [49]:. How many substrings did we get? The number of substrings is In [50]:.
In [51]:. In [52]:. In [53]:. Let’s try the tokenizers for our tweets. Which one would be better for the following text processing steps? Is the combination of some characters useful for analyzing tweets? In [54]:. Name: tweets, Length: , dtype: object.
In [55]:. In [56]:. Before getting the tokens from our tweets, we will proceed to remove the stop words. In [57]:. In [58]:. Let’s check how many tokens do we get from each tweet after the tokenization. In [59]:. After getting the length for each tweet, we can check the distribution for the tweet’s length. In [60]:. In [61]:.
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Recent progress on this task has been based on exploiting the grammatical structure of sentences but often this structure is difficult to parse and noisy. To deal with metaphor aptness assessment, I framed the problem as a case of paraphrase identi- fication. We also present first results in Arabic paraphrasing using seq-to-seq neural methods.
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